Episode Transcript
[00:00:00] Welcome back to this Week in Heart Rate Variability, the show where we take the newest peer reviewed research on heart rate variability and translate it into something clinicians, coaches, researchers and curious practitioners can actually use. Before we get into the science, a quick disclaimer Everything you hear on this show is for educational purposes only. It is not medical advice and it should not replace a conversation with your own physician, therapist or qualified healthcare provider. If anything we discuss today raises a question about your own health or the health of a client or patient, please bring it to a licensed professional who knows your full history. As always, our goal on the show is not to hand you a shortcut past that conversation, but to make you a more informed participant in it. As always, our goal on this show is not to hand you a verdict on any single study, but to walk through the actual methodology and findings closely enough that you can decide for yourself how much weight a given result deserves and how it might apply or might not yet apply to your own work. This week we have got five studies that pull heart rate variability into some genuinely unexpected corners of clinical and applied science and and one of the pleasures of putting this show together every week is watching the same underlying measurement, the beat to beat variability of the heart show up in fields that on paper have almost nothing to do with one another. We are going to start on the mat with a group of elite wrestlers and ask what heart rate variability tells us about the cost of different training protocols, a question that matters directly for how coaches structure the final days before competition. From there we will move into the modern office and look at whether heart rate variability can serve as an objective biomarker for musculoskeletal pain in desk based professionals, a population that most of us either work alongside or belong to ourselves. Our third study takes us into neurology where researchers asked whether subtle changes in heart rate variability entropy can help distinguish epileptic seizures from functional or dissociative seizures, a question with enormous clinical stakes given how differently these two conditions need to be treated and how similar they can look from the outside. After a short break to hear from our sponsor, we will look at a large retrospective study of neurologically symptomatic patients whose hearts on the surface look completely normal and ask what heart rate variability adds when standard electrocardiography does not, we will, which turns out to be quite a lot. And we will close with one of the more unusual studies we have covered on this show, a crossover trial testing whether inaudible high frequency sound components change brain activity and heart rate variability even when listeners cannot consciously hear a difference between the two conditions. It is a wide ranging episode that moves from the wrestling mat to the office chair to the neurology ward to the acoustics lab, and across all of it, the same underlying question keeps resurfacing in different clothing what is the autonomic nervous system doing and what can heart rate variability tell us about it that we would otherwise have no way of seeing? So let us get into it.
[00:02:20] Our first study comes from the world of elite combat sport, and it asks a question that has real consequences for how athletes train in the days leading up to competition. The title of the paper is Investigating the Acute effect of Different Training Protocols on Heart Rate Variability, published in the journal Sports. The authors are Burhan Demarkuran, Tuba Melikolu and Grzegor Zurich. As with every study we cover on this show, we will walk through why the question mattered, exactly how the researchers designed their protocol to answer it, what they found in specific and accurate terms, and just as importantly, what the study's own design tells us about how confidently we should hold those findings. Anyone who has coached or trained at a high level knows that the days before a competition are a balancing act. Too much training and an athlete shows up depleted. Too little, and they show up under sharpened. Heart rate variability, which is a measure of the beat to beat, variation in the interval between heartbeats and is generally used as a window into the balance between the sympathetic and and parasympathetic branches of the autonomic nervous system, has become one of the more popular tools coaches use to try to walk that line. But most of the existing evidence on how different training protocols acutely affect heart rate variability comes from endurance sports or team sports. Combat sports and Greco Roman wrestling in particular, involve a very different physiological profile. Short, explosive, high intensity efforts under significant isometric load, repeated grappling exchanges and the kind of intermittent high force output that does not map cleanly onto a steady state running or cycling model. And it was not clear whether the same heart rate variability responses seen in runners or cyclists would hold up in wrestlers whose competitive demands and training histories are so different. This gap in the literature is not a minor academic curiosity. National level combat sport programs make real periodization decisions every week based largely on evidence borrowed from other sports. And a study designed specifically around wrestlers offers something those programs have rarely had direct access to before.
[00:04:00] To find out, the researchers recruited 13 elite Greco Roman wrestlers from the Danish national team. Because this was a small, highly specialized population, the kind of athletes you cannot simply recruit in bulk, the study used it within subject design, meaning each wrestler completed both training protocols being compared rather than being randomly split into two separate groups. This approach has a real methodological advantage in elite sport research. By having each athlete serve as their own comparison, the researchers control for the substantial individual variability in baseline autonomic function that exists even within a group of similarly trained elite athletes, variability that can otherwise make between group comparisons and small samples difficult to interpret. The two protocols were high intensity interval training and prolonged endurance training. In the high intensity protocol, wrestlers performed short maximal effort intervals with recovery periods in between, mimicking the stop start intensity profile of an actual wrestling match. In the endurance protocol, wrestlers performed a longer continuous bout of lower intensity aerobic work. Heart rate variability was recorded before each protocol, immediately after and again 24 hours later, allowing the researchers to track both the acute disruption caused by each training type and the speed of recovery. The key metric used to capture this disruption was the standard deviation of normal to normal intervals or often abbreviated sdnn, which reflects overall heart rate variability across a recording period and is sensitive to both sympathetic and parasympathetic influences. The findings were striking in their magnitude. Both training protocols produced a dramatic suppression of SDNN immediately after exercise, with reductions in the range of 73 to 82% from baseline. To put that in perspective, that is not a subtle dip. It represents heart rate variability collapsing to a fraction of its resting value, which is exactly what you would expect physiologically since intense exercise sharply increases sympathetic drive and and withdraws parasympathetic tone almost completely for the duration of the effort and the period immediately following it. This magnitude of suppression is broadly consistent with what has been documented in other high performance athletic populations following maximal or near maximal exertion, which lends some external plausibility to the specific numbers reported here. Even though the wrestling specific context of this study is what makes it a novel contribution rather than a simple replication of prior endurance of sport findings. What is more interesting and more clinically useful is what happened next. By the 24 hour mark, both groups showed partial recovery of heart rate variability, moving back toward, though not fully reaching their pre training baseline. And when the researchers statistically compared the high intensity protocol against the endurance protocol after applying corrections for multiple comparisons to guard against false positives, they did not find a statistically significant difference between the two. In other words, despite feeling and looking very different on the matter on the track, high intensity interval training and prolonged endurance training appear to impose a roughly comparable acute physiological cost on these wrestlers autonomic nervous systems, at least as measured by heart rate variability. Recovery at the 24 hour mark. This finding matters practically because it runs against a fairly common assumption in applied sports science circles that shorter, harder efforts are somehow cheaper on the nervous system than longer steadier ones simply because they take less total time to complete, an assumption that this data does not support, at least not in this specific 24 hour recovery window in this specific athlete population. There are some important caveats here that are worth Sitting with 13 athletes is a small sample even for a highly specialized population and within subject crossover design, while efficient means the study cannot rule out order effects, for example whether doing the endurance protocol first changed the response to the high intensity protocol done later, or vice versa. Even with appropriate washout periods. The population is also extremely narrow elite adult male Greco Roman wrestlers on a national team. Whatever is happening physiologically in this group may not generalize to recreational wrestlers, to other combat sports, or to female athletes whose autonomic responses to training load are increasingly understood to differ in ways that are still being mapped out. And because heart rate variability was only sampled at three points before, immediately after, and 24 hours later, we do not have a detailed recovery curve. We cannot say whether recovery was linear, whether it plateaued, or whether there was a rebound overshoot at some point in between. It is also worth remembering that elite national team athletes, by virtue of years of accumulated training, may show a different baseline autonomic resilience and recovery capacity than less experienced athletes performing the same protocols, with which is one more reason to treat this specific finding as most directly applicable to similarly elite, similarly experienced populations, rather than assuming it generalizes cleanly downward to developing athletes. It is also worth situating this finding within the broader sports science conversation around monitoring training load. A great deal of applied work in this space has been built on the premise that different training modalities carry meaningfully different autonomic signatures, and that coaches can use those signatures to fine tune periodization decisions with real precision. This study offers a useful corrective to an oversimplified version of that premise. At least in this population, and at this 24 hour recovery window, the type of training performed mattered less than the simple fact that intense physical effort of either kind produced a large, broadly similar autonomic disruption. That does not mean training type is irrelevant to performance outcomes. More broadly, it clearly still matters for building sports specific fitness and skill, but it does suggest that coaches should be cautious about assuming heart rate variability. Recovery time will differ dramatically based on training modality alone, at least without measuring it directly in their own athletes.
[00:08:55] For the researchers, clinicians and coaches listening to this show the takeaway from this first study is less about a single number and more about a habit of mind. Do not assume that a training modality's subjective intensity or its outward appearance of being harder or easier tells you what its actual autonomic cost will be. The only way to know is to measure it in the specific population you are working with, using a consistent protocol across time points. That is a modest, unglamorous conclusion, but it is exactly the kind of conclusion that tends to hold up well across different sports and different athlete populations because it does not depend on any single physiological pathway being universal. It simply asks practitioners to verify their assumptions with data rather than intuition.
[00:09:31] Still, for coaches and sports scientists working with combat athletes, this study offers a genuinely useful piece of practical information, the assumption that high intensity work is acutely harder on the autonomic nervous system than a longer, steady endurance session, an assumption that shapes a lot of tapering and periodization decisions. They may not hold up when you actually measure heart rate variability recovery. If the acute autonomic cost of the two protocols is comparable, then the choice between them in the days before competition might be better guided by other factors such as sport specificity, technical rehearsal value, or an athlete's psychological readiness. Rather than a belief that one protocol is inherently gentler on the nervous system than the other, it is a reminder that heart rate variability used well, does not just confirm what training intuition already tells us. Sometimes it quietly overturns it. For practitioners building individualized taper plans, this also underscores the value of tracking each athlete's own heart rate variability recovery trajectory rather than assuming a uniform response across a squad, since even within this small, elite and physiologically similar group, the underlying data reflects considerable individual variation sitting beneath the group level averages the statistics describe. Let us move now from the wrestling mat to the office chair. Our second study is titled Heart Rate Variability as an Objective Biomarker for Work Related Musculoskeletal Disorder Risk. A cross sectional study of Hong Kong professionals published in the journal Exploration of Musculoskeletal Diseases. The authors are Adrian Lowe and Benny Lam. Work Related Musculoskeletal Disorders Persistent pain and dysfunction in the neck, shoulders, back and upper limbs that arise from repetitive strain, sustained postures and prolonged sitting are one of the most common and costly problems in modern office work. Clinicians have long relied on self reported pain scales and physical examination to assess risk, but those tools are inherently subjective and retrospective. They tell you what a worker is already feeling, not necessarily what is happening physiologically before pain becomes noticeable.
[00:11:12] The authors of this study wanted to know whether heart rate variability, and specifically a metric called normalized coherence could serve as a more objective, physiologically grounded early warning signal for musculoskeletal disorder risk. This question sits at an important intersection for occupational health as remote and hybrid work arrangements have made it harder for employers and clinicians to directly observe workers posture and movement patterns throughout the day. The appeal of an objective, wearable, compatible physiological signal that could flag emerging risk before pain sets in has only grown, and this study represents a concrete attempt to test whether heart rate variability can fill that role. Coherence in the heart rate variability literature generally refers to a smooth sine wave like oscillation in heart rate that reflects a state of synchronized, efficient interplay between the sympathetic and parasympathetic branches of the autonomic nervous system often associated with calm, focused physiological states. Normalized coherence adjusts this measure so it can be compared meaningfully across individuals with different baseline heart rate variability profiles. Which matters because raw heart rate variability values can vary substantially from person to person based on age, fitness and a range of other factors that have nothing to do with the question at hand. The hypothesis here was that people carrying more chronic postural strain and musculoskeletal stress would show lower coherence, reflecting a nervous system that is less able to settle into that efficient rhythmic pattern and that this reduced coherence might track with and even help explain pain burden independent of how stressed someone says they feel. This is a meaningful hypothesis to test because sustained musculoskeletal tension and pain are known to interact with autonomic regulation and in ways that go beyond conscious perception of stress. A worker can report feeling calm on a questionnaire while their body is still carrying significant physical strain that shows up physiologically rather than psychologically. To test this, the researchers recruited 105 office based professionals in Hong Kong and measured heart rate variability alongside validated self report measures of musculoskeletal pain intensity and the number of body sites where pain was present, sometimes called multi site pain burden. It's important to be clear about the design here. This was a cross sectional study, meaning all of these measurements were taken at a single point in time across the study group rather than following the same individuals over months or years. That matters enormously for how we interpret the results. Cross sectional data can reveal that two things are statistically associated with one another, but it cannot tell us that one causes the other, and it cannot rule out that some third factor is driving both. We will come back to this point because it shapes everything that follows. With that framing in place, here's what the researchers found. Heart rate variability coherence was negatively correlated with both musculoskeletal pain intensity and and the number of painful body sites. That is, lower coherence was associated with higher reported pain and a broader spread of pain across the body. Importantly, this association held up even after accounting for self reported psychological stress, which suggests that coherence was capturing something about pain burden that was not simply a proxy for how stressed someone said they felt. In an office ergonomics context that is a meaningful distinction because stress questionnaires are already widely used and if coherence were just tracking the same underlying construct it would not add much value. The fact that it appeared to explain additional variance in pain burden beyond self reported stress is what makes it interesting as a potential objective biomarker.
[00:13:59] Statistically, this kind of finding where one measure remains associated with an outcome even after controlling for a related but distinct measure, is often referred to as evidence of incremental validity. And it is precisely the kind of evidence that justifies treating heart rate variability coherence as a genuinely separate signal worth tracking rather than simply a more complicated way of asking someone how stressed they feel. The study went a step further and looked at posture. Two specific postural patterns forward head posture, where the head sits forward relative to the shoulders and and thoracic hyphosis and excessive rounding of the upper back were found to statistically mediate the relationship between coherence and neck and shoulder pain. Specifically, in plain terms, this means that part of the association between lower coherence and neck and shoulder pain appears to run through these postural patterns. Workers with poorer coherence tended to show more forward head posture and more thoracic kyphosis, and it was partly through these postural changes that the pain association emerged. This is a genuinely useful clinical thread because it suggests a physical correctable pathway posture sitting in between an autonomic nervous system signal and a pain outcome, rather than the relationship being some diffuse unexplained correlation. For clinicians who work with desk based patients, this offers a concrete testable chain Autonomic coherence relates to postural pattern and postural pattern relates to localized pain, which means postural intervention has a plausible mechanism through which it could influence not just the biomechanics of pain, but potentially the underlying autonomic picture as well, even though the study alone cannot confirm that direction of influence. It is worth noting too that mediation analysis of this kind, however statistically rigorous, still rests on A cross sectional snapshot and a formal mediation model can describe a plausible pathway among variables measured at the same point in time without proving that the pathway actually unfolds in that sequence in the real world over time. Finally, the researchers examined daily sedentary hours as a moderator of the coherence pain relationship and found that the amount of time spent sitting each day changed the strength of that association.
[00:15:45] In statistical terms, a moderator is a variable that influences how strong or how weak a relationship between two other variables turns out to be, rather than sitting in the causal pathway between them. The way a mediator does this suggests that the link between autonomic coherence and musculoskeletal pain is not fixed. It appears to be sensitive to how much movement, or lack of movement a person accumulates across the day, which again points toward a modifiable behavioral target rather than a fixed trait. Practically, this raises the possibility that the same underlying coherence deficit might might translate into meaningfully different pain outcomes depending on how sedentary a given worker's day actually is, which has obvious implications for how workplaces think about movement breaks and sit stand arrangements. Now the limitations here deserve real emphasis because this is exactly the kind of study where it would be easy to overstate the findings. This is again a cross sectional design, and cross sectional findings describe associations, not causes. We cannot say from this data that low coherence causes musculoskeletal pain, that poor posture causes low coherence, or that any of these relationships run in the direction our intuition might suggest. It is entirely plausible, for instance, that chronic pain itself degrades autonomic coherence over time rather than reduced coherence predisposing someone to pain. The cross sectional snapshot cannot distinguish between these possibilities or between either of them and some third factor, such as poor sleep or general deconditioning that might independently drive both. The sample is also geographically and occupationally specific. 105 professionals in Hong Kong and workplace culture, chair and desk ergonomics, and typical working hours vary enough across regions and industries that we should be cautious about assuming these exact relationships would replicate identically elsewhere. A workforce accustomed to different desk setups, different commuting patterns, or different cultural norms around taking breaks during the workday could plausibly show a different balance among these same variables, which is one more reason this specific finding should be treated as a strong hypothesis generating result rather than a settled universal conclusion about office workers everywhere. It is also worth noting what this study does not tell us about intervention. Even if future longitudinal research confirms that improving posture or reducing sedentary time changes autonomic coherence and in turn, pain burden. This cross sectional study alone cannot tell us how large that effect would be, how quickly it would emerge, or which specific interventions a standing desk scheduled movement breaks, targeted physiotherapy for thoracic kyphosis, or something else entirely would prove most effective. What it does provide is a coherent rationale for testing those interventions with heart rate variability coherence as an outcome measure alongside traditional pain scales, which would be a meaningful next step for a field that has until now relied almost exclusively on subjective reporting to evaluate whether workplace ergonomic programs are actually working at a physiological level. For clinicians, ergonomic specialists and occupational health practitioners listening today, the practical takeaway is this musculoskeletal pain in desk based workers is not purely a mechanical or biomechanical story, and it is not purely a psychological stress story either. This study suggests it sits at the intersection of both mediated in part by posture and moderated by how much a person moves across the day. That means the most effective interventions are likely to be the ones that address multiple layers at postural correction, movement breaks and stress aware workplace design rather than any single lever pulled in isolation. And for researchers, it is a strong invitation to move this line of inquiry from cross sectional snapshot toward longitudinal tracking, ideally paired with a genuine intervention trial that could finally tell us whether shifting posture or sedentary behavior actually changes the coherence pain relationship over time. With those caveats clearly on the table, this study still offers something valuable a plausible, biologically coherent pathway connecting autonomic nervous system function, posture, sedentary behavior and musculoskeletal pain measured with a tool heart rate variability that is objective, non invasive, and increasingly accessible outside of a research lab. For clinicians and ergonomics specialists working with desk based populations, this points toward heart rate variability coherence as a promising candidate for future longitudinal research, and toward posture and sedentary time as concrete intervention targets that might over time be tested for their ability to actually change the trajectory of musculoskeletal risk rather than simply being correlated with it. Now, before we move to our next study, let's pause for a quick word from our sponsor. This episode of this week in Heart Rate Variability is brought to you by Optimal hrv. Optimal HRV is built around a simple idea that heart rate variability is most useful when you track it consistently in the same conditions over time. Rather than a single reading taken at a random moment, the app guides users through a brief morning measurement protocol taken right after waking, before the stresses and stimulants of the day have had a chance to shift the nervous system away from its natural resting state. That consistency is what creates a stable, comparable baseline day after day, which is exactly the kind of data that makes trend interpretation meaningful rather than noisy. From there, optimal HRV supports longitudinal tracking so you can watch trends unfold across weeks and months rather than reacting to a single number in isolation. And it includes built in biofeedback tools designed to help you actively shift your physiological state in the moment, not just observe it after the fact. For practitioners, that combination of consistent measurement and active biofeedback is exactly the kind of toolkit that turns heart rate variability from an interesting data point into an actionable part of a client's or patient's daily routine. Optimal HRV also supports continuing education for practitioners who want to go deeper with this work. There are two trainings available right now. The first is a heart rate variability biofeedback training certified through the Biofeedback Certification Certification International alliance, taught by Dr. Ina Kazan, offering 16 APA Continuing Education credits and covering the clinical application of biofeedback techniques in real practice settings. The second is Ethical Principles and Practice Standards in Clinical Biofeedback, aligned with Biofeedback Certification International alliance standards and taught by Dr. Donald Moss, offering three APA continuing education credits and focused on the ethical framework that should guide any clinician incorporating biofeedback into their scope of practice. Full details and links for both trainings are in the show Notes for this episode. Now back to the research. Our third study takes us into neurology and and it addresses a diagnostic challenge that clinicians dealing with seizure disorders confront constantly telling the difference between epileptic seizures and functional or dissociative seizures, which can look strikingly similar from the outside but require completely different treatment approaches. The paper is titled Preictal Reduction in heart rate Variability. Entropy is associated with functional dissociative seizures and provides modest discrimination from epileptic seizures. Published in the journal Epilepsy and Behavior. The authors are Hesam Shoku Alayi Radio, Rohan Kanasamy, Samane Kuchaki, Mahinda Yogaraja and Daniel Abasolo. This is a study with a particularly careful and honest title. Notice that the authors describe the discrimination their model achieved as modest right there in the title itself, rather than overselling the finding, which is exactly the kind of scientific restraint we like to highlight on the show.
[00:22:02] Functional or dissociative seizures are episodes that resemble epileptic seizures in their outward presentation, convulsive movements, altered awareness, and in some cases, loss of consciousness, but do not arise from the abnormal electrical discharges in the brain that define epilepsy. Instead, they are understood to have a complex psychological and neurophysiological basis, often connected to prior trauma, dissociation, or stress related processes rather than a structural or electrical brain abnormality. Distinguishing between the two conditions matters enormously in practice because treating a functional seizure disorder with anti epileptic medication is not effective and can delay a patient from receiving the psychological and neurological care that actually helps them. While missing, a true epileptic seizure disorder carries its own serious risks, including inadequate seizure control and the dangers that come with unmanaged epilepsy. Misdiagnosis in either direction carries a real cost to the patient, and this diagnostic ambiguity is one of the more persistent challenges in clinical neurology, one that has historically taken patients months or even years to fully resolve. In some healthcare systems, often after multiple failed medication trials and repeated emergency department visits, the gold standard for making this distinction is video electroencephalography monitoring, capturing a seizure on camera alongside brain electrical activity. But that requires prolonged hospital monitoring that is not always feasible or immediately available, and patients can wait considerable stretches of time, sometimes with significant disruption to their lives, before a monitoring bed becomes available. This has driven interest in whether something simpler, like the electrocardiogram, which is far easier to capture continuously and far less resource intensive to deploy, might carry a signal that helps with this distinction, even as a triage or screening step ahead of definitive video monitoring. The idea explored in this study centers on entropy, a family of mathematical measures that quantify the complexity, irregularity, or unpredictability of a signal over time. In the context of heart rate variability, entropy measures assess how complex or how regular the pattern of beat to beat intervals is. A healthy, adaptable autonomic nervous system tends to produce a certain baseline complexity and departures from that complexity. Either too much regularity or too much chaos can reflect changes in autonomic regulation.
[00:24:03] This is a somewhat different lens than the frequency based or time based heart rate variability measures we often discuss on this show, such as the standard deviation measures we heard about in our first study today. Entropy is specifically designed to capture the patterns underlying unpredictability rather than simply its magnitude of variation, which makes it particularly well suited to detecting subtle shifts in autonomic complexity that might not show up as a straightforward change in overall variability.
[00:24:25] The researchers hypothesized that the period leading up to a seizure, referred to as the preictal period might show a reduction in heart rate variability entropy, specifically before functional or dissociative seizures, reflecting the psychophysiological buildup known to precede these events and that this reduction might not appear or might appear differently before true epileptic seizures, which arise from a different underlying mechanism. To test this, the researchers analyze electrocardiogram data from 125 patients undergoing video monitoring and 83 of whom experienced functional or dissociative seizures and 42 of whom experienced epileptic seizures, all confirmed by the video monitoring gold standard. By comparing entropy based heart rate variability measures in the preictal period against each patient's own interictal baseline, the resting period between seizures, the researchers found that entropy dropped meaningfully before functional and dissociative seizures. But this drop was not observed or was substantially less pronounced before epileptic seizures. This is a genuinely interesting physiological finding. On its own, it suggests that whatever autonomic buildup precedes a functional or dissociative seizure leaves a distinct fingerprint in heart rate variability complexity that a true epileptic seizure does not share, which is consistent with the broader understanding that functional and dissociative seizures involve a different underlying physiological process than the abnormal cortical electrical discharges that drive epileptic seizures. It is worth pausing on why using each patient as their own interictal baseline matters methodologically, because heart rate variability entropy can vary meaningfully from person to person for reasons that have nothing to do with seizure type, age, fitness, medication regimen, and general autonomic health. Among them, comparing each patient's pre ectal period against their own resting baseline rather than against a fixed population wide threshold controls for a substantial amount of that individual variability and makes the pre ectal drop a more trustworthy signal than a simple absolute entropy cutoff would have been. The researchers then took this a step further and built a machine learning model using the relative change between preictal and interictal heart rate variability entropy and as an input feature testing whether this signal alone could help classify seizure type, the model achieved 73.5% sensitivity and 61.9% specificity. It is worth translating what those numbers actually mean in practice. Sensitivity of 73.5% means the model correctly identified about three out of every four functional or dissociative seizures using this signal. Specificity of 61.9% means that among true epileptic seizures the model correctly ruled out the functional dissociative label a little under two thirds of the time, which also means that more than a third of epileptic seizures were being misclassified by this signal alone, that is A real non trivial error rate. And the authors themselves were direct about this limitation, concluding explicitly the classification based on the electrocardiogram signal alone is not yet sufficient to stand as a standalone diagnostic tool for a clinician. This distinction between sensitivity and specificity is not just statistical bookkeeping it directly shapes how a tool like this could safely be used in practice. A moderately sensitive but only moderately specific screening signal might reasonably be used to help prioritize which patients get expedited access to video monitoring, but it could not responsibly be used on its own to rule epilepsy in or out for an individual patient. In practice, that means a favorable result from this kind of electrocardiogram based signal might justifiably move a patient up a monitoring wait list, but it should never be treated as license to skip video confirmation altogether. Given how much clinical weight and how many downstream treatment decisions rest on getting seizure type right, there are additional caveats worth naming. This is fundamentally an observational associational finding. The study identifies a statistical relationship between preictal entropy changes and seizure type, not a proven causal mechanism, and it should be read that way. The 42 epileptic seizure patients represent a meaningfully smaller comparison group than the 83 functional and dissociative seizure patients, which can affect the stability of specificity estimates. In particular, since specificity is calculated entirely from that smaller group and smaller groups tend to produce less stable percentage estimates than larger ones. And because all patients were undergoing video electroencephalography monitoring in a hospital setting, we do not yet know how this signal would perform using data captured outside that controlled environment, where noise, movement, artifact and non standardized recording conditions could all degrade signal quality. A hospital monitoring unit represents close to ideal recording conditions and real world deployment, whether through wearable devices or ambulatory monitoring, would need to be validated separately before the signal could be trusted outside that setting. What makes this study valuable, despite its current limitations, is the direction it points toward video electroencephalography. Monitoring is resource intensive and any tool that could help clinicians triage or flag likely functional or dissociative seizure activity. Using something as accessible as an electrocardiogram would meaningfully expand access to earlier, more targeted care, particularly in settings where video monitoring beds are scarce or wait listed. The 73.5% sensitivity and 61.9% specificity are not yet good enough to replace video monitoring, but they establish that the underlying physiological signal, the preictal drop in heart rate variability entropy specific to functional and dissociative seizures is real and detectable, which gives future research a concrete foundation to refine and build on whether, through better entropy measures, multimodal signals combining electrocardiogram data with other easily captured physiological streams, or larger and more balanced patient samples that could improve the stability of both sensitivity and spread specificity estimates. For clinicians managing patients whose seizure type remains uncertain, this line of research offers a genuinely hopeful signal that a lower burden, more accessible tool may one day meaningfully support, even if it will not soon replace the diagnostic process that currently depends so heavily on scarce video monitoring resources. Let us turn now to our fourth study, which asks a broader and in some ways more foundational question. What does heart rate variability add when a patient has neurological symptoms but their heart, by every standard measure, looks fine? The paper is titled Integrated Electrocardiography Heart Rate Variability analysis in Neurologically Symptomatic Individuals without Cardiac Symptoms, A Data driven retrospective observational Study of electrocardiographic autonomic Associations, published in the journal Cureus. The authors are Manjulata Manoharan, Renu Gasundari, Gagan K. Banode, Madzabi Hula, and Tripuan Kumar. Clinicians regularly see patients who present with neurological symptoms dizziness, headaches or migraines or excessive sweating known as hyperhidrosis who explicitly do not report cardiac symptoms like chest pain or palpitations and whose standard cardiac workup, including a resting electrocardiogram, appears unremarkable. The standard electrocardiogram captures the heart's electrical conduction and repolarization, essentially whether the heart's electrical wiring and its recovery between beats are structurally and functionally normal. But the standard electrocardiogram was never designed to capture the more subtle dynamic story of autonomic regulation that heart rate variability measures. The authors of this study wanted to know whether layering heart rate variability analysis on top of standard electrocardiography in this specific population, neurologically symptomatic cardiac symptom free, would reveal autonomic abnormalities that a conventional cardiac workup misses entirely. This is a question of real practical consequence in busy clinical settings, where a normal looking electrocardiogram often signals the end of the cardiac line of inquiry. Even when a patient's neurological symptoms remain unexplained and the underlying autonomic nervous system quietly has never actually been assessed in many healthcare systems, ordering a full heart rate variability workup is neither routine nor immediately accessible in the way a standard electrocardiogram is is. Which means this assessment gap is not simply theoretical it reflects how care actually tends to be structured, with autonomic function frequently falling through the cracks between neurology and cardiology unless a clinician specifically thinks to look for it. This was a retrospective observational study, meaning the researchers looked back at existing patient records and data rather than prospectively enrolling and following patients forward in time. And it was explicitly data driven, meaning the analysis was built around patterns that emerged from a large existing data set rather than testing a narrow pre specified hypothesis. The data set here was substantial 314 neurologically symptomatic patients without cardiac symptoms with dizziness, headache and migraine and hyperhidrosis identified as the most common presenting complaints. For each of these patients, the researchers examined both standard electrocardiographic parameters, the conduction and repolarization measures that a cardiologist would typically review, and heart rate variability parameters, comparing the latter against established normative reference values for a healthy population. A data set of this size, even in a retrospective format, gives the analysis meaningfully more statistical power than the smaller cohorts we have discussed elsewhere in this episode, which lends additional weight to the patterns the researchers were able to detect even as the retrospective nature of the design introduces its own separate set of limitations that we will return to shortly. It is also worth noting that a sample this large and this specifically defined, neurologically symptomatic but explicitly free of cardiac complaints would be genuinely difficult and time consuming to assemble through prospective recruitment, which is one of the practical advantages a well conducted retrospective analysis can offer, even though it comes with its own well understood trade offs in terms of causal inference. The core finding was a genuine divergence between these two layers of analysis. Electrocardiographic conduction and repolarization parameters were for the most part preserved across this patient group. In other words, the conventional cardiac readout looked largely unremarkable, consistent with these patients own reports of no cardiac symptoms. But when the researchers turned to heart rate variability, they found evidence of subtle autonomic dysregulation relative to normative values, meaning that beneath a structurally and electrically normal looking heart, the balance and adaptability of the autonomic nervous system's control over heart rhythm appeared measurably off. This is exactly the kind of finding that heart rate variability as a tool is uniquely positioned to surface because it is sensitive to functional dynamic regulation in a way that a static electrocardiogram trace is not. The electrocardiogram is in a sense a snapshot of cardiac electrical structure, while heart rate variability is closer to a video of how that structure is being modulated moment to moment by the nervous system. This distinction between structural and functional assessment is one of the recurring themes across today's episode. In study after study, we have seen heart rate variability reveal functional dysregulation in populations where a more static structural test came back looking essentially normal, which is precisely the gap in clinical assessment that heart rate variability was always best positioned to fill. Based on this pattern, the authors proposed that heart rate variability could serve as a sensitive tool for early neurocardiac risk detection in this specific population. Patients whose neurological symptoms might otherwise be evaluated purely through a neurological lens, with cardiac autonomic involvement going unassessed simply because there is no obvious cardiac complaint to prompt that evaluation. The practical implication is that dizziness, migraine and hyperhidrosis are not purely neurological phenomena in isolation. They may sit at an intersection where autonomic nervous system dysfunction is a shared underlying thread and heart rate variability offers a way to make that thread visible even when standard cardiac testing gives an all clear. This fits within a broader and growing recognition across neurology that many common, seemingly purely neurological symptoms have an autonomic component that is easy to overlook precisely because the standard evaluation pathway for these complaints does not typically root through cardiac autonomic testing at all. As with our other observational studies today, the limitations here matter and deserve to be stated plainly. This is a retrospective associational study. It establishes that autonomic dysregulation, as measured by heart rate variability, is present alongside these neurological presentations, but it cannot establish that this dysregulation causes the neurological symptoms, that the neurological symptoms cause the dysregulation, or that either one is downstream of some shared underlying process such as chronic stress, an undiagnosed autonomic disorder, or medication effects that were not fully accounted for in the retrospective chart review. Retrospective designs also depend on the completeness and consistency of existing clinical records, which can introduce variability in how symptoms were documented, or how heart rate variability recordings were obtained across different patients and time points.
[00:35:21] And comparing patient values against normative reference ranges rather than against a matched prospectively recruited control group is a reasonable and common approach, but a somewhat blunter one than a head to head comparison would offer. Since normative ranges are necessarily drawn from a different population collected under different conditions than the patients actually being studied. Here, a prospective study with a matched symptom free control group recruited under identical conditions would offer a cleaner comparison and would help rule out the possibility that differences between this patient group and the normative reference values reflect something of the about how or where the reference values were originally collected rather than a true difference in autonomic function tied specifically to these neurological presentations. Even with those caveats, this study adds a meaningful data point to a growing body of work suggesting that autonomic nervous system assessment deserves a more prominent place in the evaluation of common neurological complaints, not as a replacement for standard neurological workup, but as a complementary layer that can reveal risk that a purely structural or symptom based assessment would miss.
[00:36:16] For clinicians managing patients with unexplained dizziness or migraine, this is a reason to consider that the autonomic nervous system, quietly measurable through heart rate variability, may be doing more work in the clinical picture than a clean electrocardiogram alone would suggest. It is worth reflecting for a moment on why this particular combination of symptoms dizziness, headache and migraine and hyperhidrosis showed up so prominently in this data set. Each of these presentations has long been suspected on clinical grounds to have some connection to autonomic factors function. Dizziness can arise from blood pressure and heart rate regulation, issues that are fundamentally autonomic in nature, migraine has a well documented, if incompletely understood relationship with autonomic nervous system activity, and hyperhidrosis is by definition a disorder of autonomic sweat gland regulation.
[00:36:59] What this study adds is not the discovery of an autonomic connection to these symptoms, which clinicians have long suspected, but rather a demonstrably measurable signal captured through a widely available and inexpensive tool in a sample large enough to lend the pattern real statistical weight. That combination, a plausible existing clinical intuition paired with an accessible objective measurement, is exactly the kind of finding that tends to translate into meaningful changes in clinical practice over time, provided it continues to be validated through prospective research that can more firmly establish the direction and strength of the underlying relationships. It is also a reminder of how much diagnostic value can sit in a measurement that is already widely available and inexpensive to collect. The barrier here is not technological, but rather one of clinical habit and workflow flow, since heart rate variability analysis from a standard electrocardiogram recording requires no additional equipment beyond what most clinics already have on hand. Our fifth and final study for this episode takes a genuinely different turn, moving from clinical populations into a controlled laboratory setting to ask a question that sits at the boundary of what we can consciously perceive. The paper is titled Multi Level Physiological Responses to the Hypersonic Effect, Concurrent Electroencephalographic Heart Rate Variability and and Subjective Evaluation in Healthy Adults in a participant blind crossover study published in the journal Cureus. The authors are Genji Takasao, Satsuki Okanamasa, Tatsuya Kitagawa Ichigishimote, Maritarumoto Kanatomi, Otake, Takeuki Kodama, and Akio Goda. The human ear typically perceives sound up to roughly 20 kilohertz, and frequencies above that threshold are generally considered inaudible to adult listeners whose sensitivity to the very top of the audible range tends to decline further with with age. The so called hypersonic effect refers to a body of prior research suggesting that even when high frequency sound components above this audible ceiling cannot be consciously heard, their presence alongside audible sound may still influence brain activity, a claim that has been debated in the acoustics and neuroscience literature for years, in part because it is inherently difficult to test. How do you isolate the effect of something a listener cannot consciously perceive from every other variable in an experiment, and how do you rule out that any observed difference is simply an artifact of the recording or playback equipment than rather rather than a genuine neurophysiological response? This study attempted to address that challenge directly by simultaneously measuring three different levels of response central brain activity through electroencephalography, autonomic nervous system activity through heart rate variability, and conscious objective experience through self report, while keeping participants blind to which sound condition they were hearing, an approach designed specifically to rule out expectation or suggestion as an explanation for any physiological difference observed. The design was a participant blind crossover study conducted in 20 healthy men. Each participant was exposed to two an audible only sound containing frequency content only up to the 20kHz threshold of human hearing, and the identical audible sound with additional high frequency components layered on top extending above 20kHz into the inaudible range. Because it was a crossover design, every participant experienced both conditions, allowing each person to serve as their own control and removing between person variability as a confound.
[00:39:51] Throughout each listening condition, the researchers recorded electroencephalography to capture brain electrical activity, heart rate variability to capture autonomic nervous system response, and collected subjective ratings of how participants experienced the sound. The participant blind element of the design is particularly important here because participants did not know which condition audible only or audible plus inaudible components they were being exposed to at any given time. Any difference in subjective rating between conditions could not simply be explained by participants expecting or being primed to notice something different, which strengthens the interpretive value of finding no subjective difference between the two conditions. The results showed a genuinely interesting dissociation between different levels of the nervous system's response. At the level of subjective experience, there were no significant differences between the audible only condition and the audible plus high frequency components condition. Participants, in other words, did not report feeling or perceiving the sound differently between the two conditions, consistent with the idea that the added high frequency components were indeed outside conscious perception, but at the physiological level the picture was different. The electroencephalography data showed a trend level increase in Posterior Alpha 2 activity, a specific frequency band of brain activity recorded from the back of the scalp, often associated with relaxed wakeful states during exposure to the high frequency component condition, though the authors described this as trend level, meaning it approached but did not clearly cross the threshold for statistical significance on its own. More robustly, the heart rate variability data showed a statistically significant reduction in the relative low frequency component of heart rate variability following exposure to the high frequency component sound compared to the audible only condition. The low frequency component of heart rate variability is generally understood to reflect a mix of sympathetic and parasympathetic influences, and a relative reduction in this component can indicate a shift in the balance of autonomic regulation, even though the precise physiological interpretation of the low frequency band remains a point of ongoing scientific discussion within the heart rate variability research community more broadly, a debate that predates and extends well beyond this particular study. Taken together, these findings suggest a coordinated central to autonomic pattern brain activity nudging in one direction and autonomic nervous system balance shifting in a corresponding way occurring entirely below the threshold of conscious awareness. The authors describe this as a possible hypersonic effect, and the framing is careful and appropriately cautious. It points toward a genuine, measurable physiological response to sound content that cannot be consciously heard. Rather than claiming a settled mechanism. What is particularly notable is that the dissociation ran in a specific direction. The subjective and the physiological measures diverged rather than moving together, which is itself methodologically interesting because it means that self report alone, had it been the only measure collected, would have entirely missed a real physiological effect. The two independent objective measures, electroencephalography and heart rate variability, both pointed toward even if only one of the two crossed the conventional threshold for statistical significance. The limitations here are worth being explicit about because this is a small, tightly controlled laboratory study, not a real world field study. 20 participants, all healthy adult men, is a modest sample, and the exclusively male composition means we do not yet know whether this pattern holds for women whose autonomic and auditory processing profiles can differ in ways relevant to this kind of research.
[00:42:59] The trend level electroencephalography finding, by the author's own description, did not reach full statistical significance, so it should be treated as suggestive rather than confirmed, even though the heart rate variability finding did reach significance and as with any single session laboratory paradigm, we do not know how durable this autonomic shift is, whether repeated or prolonged exposure would produce a larger or smaller effect, or whether the specific sound stimulus used here generalizes to other types of audio content containing similar high frequency components, such as certain music recordings or environmental sound sources. It is also worth noting that laboratory acoustics differ substantially from the acoustic environments most people actually encounter high frequency sound content in, whether through digital audio playback, live performance, or ambient environmental noise. And any translation of this finding to those real world contexts would require its own dedicated research rather than a simple assumption of equivalence. A single session design also cannot tell us whether the observed autonomic shift represents a genuine, consistent physiological response response to high frequency sound content, or more transient reactions specific to the particular novelty of the laboratory listening session itself, a distinction that only repeated measures research across multiple separate sessions could ultimately resolve. What makes this study worth including on this show is less about any immediate practical application and more about what it demonstrates methodologically that heart rate variability can pick up a genuine physiological signal from a stimulus that sits entirely outside conscious perception in a design careful enough to blind participants and and measure multiple levels of the nervous system simultaneously. That is a meaningful contribution to the broader question of how much of our physiological life is being shaped by inputs we are not consciously aware of receiving. And it is exactly the kind of foundational, mechanism oriented research that over time tends to open up new lines of inquiry into sound environment and autonomic health. There is also a broader methodological lesson here that extends well beyond acoustics research.
[00:44:46] This study is a useful reminder that self report, while indispensable and often the most practical tool available, is not a complete window into what the nervous system is actually doing. Any research design in any field that relies solely on subjective report to determine whether an intervention or exposure had an effect risks missing exactly the kind of dissociation. This study uncovered a real, measurable, statistically significant physiological shift occurring in the complete absence of any corresponding change in conscious experience.
[00:45:12] For researchers designing future studies, whether in acoustics, environmental exposure, or any other domain involving inputs at or near the threshold of perception, this argues strongly for including objective physiological measures like heart rate variability alongside subjective ratings, rather than treating the two as interchangeable proxies for the same underlying phenomenon. It is a small, elegant study with a genuinely large implication that our conscious experience of an environment can be an incomplete report of what that environment is actually doing to us physiologically, and that heart rate variability, precisely because it operates entirely outside conscious control, is one of the better tools available for for catching what self report alone would miss. So, stepping back, across all five of these studies, what connects a group of Danish wrestlers, a cohort of Hong Kong office workers, a neurology ward distinguishing seizure types, hundreds of patients with unexplained dizziness and headaches, and 20 men listening to an audible sound in a lab. In every case, heart rate variability is doing the same fundamental job making visible something that other, more conventional measurements cannot see on their own. In the wrestlers, it revealed that two very different feeling training protocols impose a comparable acute autonomic cost information that a stopwatch or a training log would never surface. In the office workers, it offered an objective signal of pain related autonomic strain that went beyond what a stress questionnaire alone could tell us and traced a plausible pathway through posture and sedentary behavior. In the neurology studies, heart rate variability picked up subtle autonomic signatures, a preictal entropy drop specific to functional and dissociative seizures, and a pattern of dysregulation hiding beneath an entirely normal looking standard electrocardiogram in populations where the conventional tools had already run out of answers. And in the hypersonic effects study, it captured a physiological shift produced by a stimulus its own subjects could not consciously perceive. A second thread worth naming is how differently these five studies use sample size and design to earn their conclusions, and how important it is to read each finding through the lens of its own methodology. Rather than treating a study found x as a uniform statement of certainty. Thirteen wrestlers in a within subject crossover design can support a confidence statement about the comparability of two training protocols within that specific group precisely because the design controls so tightly for individual variability. A 314 patient retrospective data set can support a broader, more generalizable claim about a pattern across a diverse clinical population precisely because of its size, even though its retrospective nature limits how tightly it can pin down cause and effect. A 20 person laboratory crossover study with its participant blind design is built to isolate a very specific mechanistic question with high internal control at the cost of the generalizability that would come from a larger, more heterogeneous sample. None of these designs is inherently better than the others. Each is suited to the specific question being asked, but conflating them or holding every study to an identical standard of proof does a disservice to the very careful thinking that went into designing each of them. This is worth internalizing as a general habit for anyone who consumes research regularly. The right question to ask about any study is not simply was this finding significant? But Was this design well suited to answer the question it set out to ask? And what can and cannot this specific design tell us as a result?
[00:47:53] Looking ahead, each of these five lines of research points toward a fairly clear next step. The wrestling study invites replication in larger and more diverse athlete samples and a more granular recovery curve that samples heart rate variability at more time points between the immediate post exercise Measurement and the 24 hour follow up. The office worker study invites a longitudinal or interventional follow up that could finally test whether changing posture or sedentary behavior actually shifts the coherence pain relationship. Rather than simply confirming that the three variables travel together in a single snapshot, the seizure discrimination study invites a larger, better balanced sample and exploration of whether combining entropy based heart rate variability with other easily captured physiological signals could push sensitivity and specificity into a range that might support real clinical decision making. The neurologically symptomatic patient study invites prospective validation with a matched control group, which would allow a more direct comparison than reliance on normative reference ranges permits. And the hypersonic effects study invites replication in larger and more diverse samples, including women, an exploration of whether the effect holds up with real world audio content rather than a controlled laboratory stimulus. None of these next steps is especially glamorous, but they are exactly the steps that turn an interesting preliminary finding into a piece of knowledge that clinicians and researchers can actually build practice and further research on top of the other thread running through nearly all of today's studies is a note of appropriate humility. Three of our five studies today were observational or cross sectional in design, and in each of those cases the authors and we on this show have been careful to describe what was found as an association, not a proven cause. Low coherence tracking with musculoskeletal pain, autonomic dysregulation showing up in neurologically symptomatic patients, entropy drops preceding one class of seizure and not another. These are real replicated in this sample patterns worth taking seriously and worth building further research on that. But none of them on their own tells us that changing one variable will reliably change the other. That distinction is not a weakness of the research. It is simply where the science currently stands. And knowing exactly where the science stands is what lets clinicians and researchers use these findings appropriately rather than overreaching with them. If there is one takeaway to carry out of this episode, it might be this heart rate variability keeps proving itself as a tool for surfacing signals that other measurements miss.
[00:50:07] But the discipline of correctly characterizing what those signals do and do not prove is just as important as detecting them in the first place.
[00:50:15] That discipline is not a limitation on the field's ambition, if anything, is what earns heart rate variability research the credibility it needs to eventually move from association to genuinely actionable, causally grounded clinical guidance. One carefully designed follow up study at a time. That is where we will leave things for this week. Thank you as always for spending this time with us and for caring enough about the details of this science to sit with the nuance rather than the headline. Until next week. Keep measuring, keep questioning, and keep learning. This has been this week in heart rate variability.
