Episode Transcript
Welcome, friends, to the Heart Rate Variability Podcast — This Week in Heart Rate Variability Edition — Episode 26.
Each week, we explore the latest research on heart rate variability, or HRV, and what it reveals about the deep relationship among the heart, the brain, and the nervous system. Today we’re going deeper than usual — expanding on the science behind six major studies that span meditation, exercise physiology, psychiatry, cardiology, circadian neuroscience, and neonatal development. Across all six, the theme is the same: HRV is not simply a wellness trend or a performance gadget metric. It’s a window into regulation — how the body responds to challenge, recovers, and organizes itself over time.
Before we begin, a quick reminder that the information shared here is for educational purposes only and is not intended to replace professional medical advice, diagnosis, or treatment. Always consult your physician or qualified healthcare provider with any medical concerns.
We begin with a study that sits at the intersection of ancient practice and modern computation. The paper is titled “Detecting Meditative States Through Heart Rate Variability and Machine Learning Techniques.” It appears in Engineering Letters and is also published as a Springer book chapter in Frontiers in Computing and Systems. The authors are Vivek Ranjan, Raghuwansh Singh, Anindita Ganguly, and Suman Halder.
This study challenges a common misunderstanding about meditation. Meditation is often described as relaxation, but physiologically, it’s more accurate to say it can be a distinct mode of regulation. The body may become quieter, but the nervous system is not simply shutting off like a dimmer switch. Instead, it may be reorganizing itself — shifting into a pattern of control that is calmer, more efficient, and often more stable.
To appreciate why this matters, it helps to remember what HRV actually is. HRV is the variation in time between heartbeats. It reflects the constant negotiation between two branches of the autonomic nervous system: the sympathetic branch, which mobilizes energy and prepares us for action, and the parasympathetic branch, especially through the vagus nerve, which supports recovery, restoration, and flexible regulation. A healthy system doesn’t mean “always high HRV” or “always low HRV.” It means the system can shift appropriately, then return to balance. That flexibility is the core of resilience.
Traditional HRV metrics often reduce the story to a few numbers — maybe the overall amount of variability, or how much power is in one frequency band versus another. Those measures are useful, but meditation can change something deeper than averages. Meditation may alter the structure of regulation—the pattern by which the system flows through time. And that’s what this study goes after.
In this study, the researchers processed the HRV signal to obtain a time–frequency representation using a Continuous Wavelet Transform. That process produces a scalogram, which is like turning your heart rhythm into a map. Instead of reading a list of beat-to-beat intervals, you see how different rhythm components change over time. It’s a way of viewing the autonomic signal as a living landscape rather than a single score. It also captures transient shifts — moments where the system changes state — which can be very relevant in meditation, where attention, breath, and mental state can fluctuate even within a session.
Then they performed texture analysis on these scalograms. In plain terms, they looked at how the image “looks” statistically — how pixel intensities cluster, how smooth or varied the image is, and what hidden structure emerges. This is important because the “texture” of a scalogram can represent how regulation is organized across scales. A rigid system might show repetitive, narrow patterns. A chaotic system may look noisy and unstructured. But an adaptive system often shows organized complexity — patterns that are structured, but not frozen.
They paired this with nonlinear measures of complexity across scales, including entropy-based features. Entropy, in this context, is not “good” or “bad.” It is information about how predictable a signal is. Too predictable can mean rigidity. Too unpredictable can mean instability. The sweet spot is structured complexity — a signal that is rich and adaptable without falling apart.
Next, they used machine learning to separate meditative from pre-meditative states. They started with a large feature set and used a selection method to keep the strongest predictors. This step matters because not every HRV feature adds meaningful information, and too many features can lead to “overfitting,” meaning the model performs well on the training data but fails in real life. Feature selection increases the likelihood that what the model learns reflects true physiological differences rather than noise.
The results showed that meditative states can be detected with high accuracy when the heart rhythm is treated as a structured signal rather than just a number that goes up or down. In other words, meditation leaves a fingerprint — not just in speed, but in organization.
The practical meaning is exciting. If meditative regulation has a distinct autonomic fingerprint, HRV tools could eventually help individuals understand which practice creates the most stable, resilient nervous system pattern for them. This could support biofeedback training, meditation coaching, and even personalized practice recommendations based on how someone’s body responds, not just how they describe the experience.
And this also validates something many practitioners already know: deep meditation is not blankness. It is often a state of quiet clarity — where the body is calm, but regulation is highly coordinated.
Now we shift from stillness to physical strain. The second paper is titled “Agreement between heart rate variability-derived and lactate/ventilatory thresholds during a 4-min stepwise incremental cycling test in male adults,” published in Physiological Reports. The authors are Anton Olieslagers, Yoram Müller‐Jabusch, Margot Vancoillie, Emma Delen, and Toon de Beukelaar.
This study addresses a major challenge in performance science: how do we determine metabolic thresholds without blood draws or laboratory respiratory equipment?
To understand why thresholds matter, imagine exercise intensity like climbing a hill. At the bottom of the hill, your breathing is easy, your muscles are comfortable, and you can talk in full sentences. As you climb, you reach a point where you can still keep going, but talking becomes harder. You feel that you’re working. And then, at higher intensities, you reach a point where you can only sustain the effort briefly — breathing becomes heavy, legs burn, and recovery takes longer.
Those transition points are thresholds. They help define training zones. They help clinicians prescribe exercise safely. They help athletes avoid undertraining and overtraining. The problem is that identifying thresholds precisely usually requires laboratory methods.
During exercise, the body moves through different intensity zones. At lower intensities, energy production is efficient, breathing is controlled, and the body can maintain a stable internal environment. As intensity increases, the body relies more on faster, less efficient energy systems. Lactate rises, ventilation changes, and sustaining the workload becomes more difficult.
This paper explores whether HRV can reveal the same turning points. Specifically, it examines a nonlinear HRV measure called DFA alpha-1. You can think of DFA alpha-1 as a measurement of how flexible and “naturally complex” the heart rhythm is. When the body is in a stable, regulated state, the heartbeat pattern is complex and adaptable. As the body moves into high strain, the pattern becomes more rigid and predictable because the system is being pushed into a narrower survival-style control mode.
Why does complexity drop? Because as intensity rises, sympathetic energy increases, vagal influence decreases, and the heart becomes more driven by a simpler command: maintain output. The system prioritizes performance and survival over flexibility. This is adaptive in the short term, but it also means the heart rhythm becomes more correlated, more locked in, and less complex.
The cycling protocol used four-minute stages, which are long enough for heart rate, breathing, and metabolic variables to stabilize before stepping up intensity. That staging is important because if the stages are too short, the body may not reach a steady state, and the comparisons become messy.
The study compares HRV-derived points to gold standards: lactate thresholds and ventilatory thresholds. Ventilatory thresholds are based on changes in breathing patterns and gas exchange. Lactate thresholds are based on changes in blood lactate levels. Both reflect the body’s shift in metabolism and buffering systems.
What emerges is a reality check that is actually useful. The higher-intensity threshold — the point where strain becomes unmistakable — can be detected more reliably because the body undergoes a major internal shift. In contrast, the first threshold — where light work becomes moderate — can vary more from person to person.
This variability makes sense. At low intensity, vagal withdrawal and sympathetic rise can happen gradually, and the timing depends on conditioning, hydration, sleep, caffeine, stress, and even excitement. Two people can have the same fitness level but different nervous system responses that day. That makes a single universal cut-off difficult.
So the deeper lesson is not that HRV thresholds work or don’t work. It’s that HRV appears most dependable when the system undergoes a large, clear physiological phase shift — especially the transition into high-intensity strain. For practical use, this suggests that HRV-derived thresholds may be most effective for identifying high-intensity boundaries, whereas low-intensity boundaries may require personalization and repeated measures.
Now we move into psychiatry and brain-body regulation. The third paper is titled “Heart Rate Variability and Cognitive Function as Potential Endophenotypes in Schizophrenia: A Cross-Sectional Observational Study Using First-Degree Relatives,” published in Cureus. The authors are Priyadarsini Samanta, Barsha B. Parida, Jigyansa I. Pattnaik, Rama Chandra Das, Rashmi Kumari, Vedaant Parekh, Jayanti Mishra, Jyotiranjan Sahoo, and Laxman Kumar Senapati.
Schizophrenia is commonly framed as a disorder of perception and thought. But it is also associated with major physical health disparities, including increased cardiovascular risk and reduced life expectancy. That is a clinical reality that often doesn’t get enough attention. Many factors contribute, including medication effects, lifestyle challenges, access to healthcare, and chronic stress. But there may also be deeper physiology at play — physiology that links mental illness risk with bodily regulation.
This study asks whether an autonomic nervous system imbalance could be part of the biological bridge that connects mental illness and cardiovascular risk.
The word “endophenotype” is central here. An endophenotype is a measurable biological trait that sits between genetics and symptoms. It is not the disease itself, but a marker of underlying vulnerability. If HRV patterns can serve as an endophenotype, then HRV might reflect broader regulatory differences linked to genetic risk and clinical expression.
That’s why the comparison matters: patients are compared to first-degree relatives. First-degree relatives share a genetic background, but they don’t have schizophrenia. If HRV differences are seen only in patients and not in relatives, it suggests that those differences may be more closely tied to the illness state. If relatives also show changes, it suggests a heritable vulnerability.
The study used short resting ECG recordings for HRV and paired them with standardized cognitive testing. What they found was a strong sign of sympathovagal imbalance in the schizophrenia group. The system was shifted toward sympathetic dominance — the body’s stress-response branch — and away from parasympathetic buffering — the vagal branch linked to calm, recovery, and flexible regulation.
A particularly important point is that some simple time-domain HRV measures may not differ dramatically, but the balance measures do. That means it’s not always that variability disappears. Instead, the nervous system may redistribute control toward a stress-dominant profile. The rhythm may still vary, but it varies under a different steering system, with less of the “brake” that supports adaptive flexibility.
Even more compelling is the link to cognition. Autonomic regulation and cognitive regulation share overlapping brain networks. The neurovisceral integration model suggests that the prefrontal cortex helps regulate the autonomic nervous system through inhibitory pathways that influence vagal activity. When that regulatory capacity is weakened, cognitive control and physiological flexibility may decline together.
The practical implication is that HRV monitoring might one day help clinicians track not only cardiovascular risk but also physiological regulation related to cognitive stability — providing another lens for understanding day-to-day functioning. It also suggests potential intervention targets. If vagal support can be strengthened through behavioral interventions, breathing training, physical activity, sleep stabilization, or biofeedback, it may benefit both bodily health and regulatory capacity.
Now, before we continue into cardiology and circadian science, I want to take a moment to talk about the tools that make this kind of physiological tracking accessible in real-world practice.
The Optimal HRV platform sponsors this episode.
For clinicians, researchers, and healthcare professionals, listening to reliable, clinical-grade data is the foundation of meaningful intervention. Optimal HRV provides a comprehensive suite of tools designed to bring advanced HRV analytics into daily practice.
The professional dashboard lets you monitor and manage multiple clients or patients from a single centralized platform. Because HRV is trend-based rather than snapshot-based, longitudinal tracking is essential. Whether you’re working with trauma survivors, high-performance athletes, corporate teams, or individuals managing chronic stress, you can monitor trends remotely and identify concerning shifts early.
This is especially powerful in trauma-informed care. If you see a drop in regulatory capacity before a session, you can shift your approach from a challenge-based protocol to a safety-based, resourcing intervention. Objective physiology helps guide timing, intensity, and personalization.
The Optimal HRV app and high-accuracy readers are designed to provide data you and your clients can trust — turning complex autonomic science into actionable insight.
Now, back to the research.
We step into cardiology with a study linking HRV to the physical structure of coronary disease. The fourth paper is titled “Heart rate variability, unstable coronary plaques, and cardiovascular outcomes,” published in the European Heart Journal. The authors are Yue Yu, Weifeng Guo, Ziwei Shen, Han Chen, Changyi Zhou, Cheng Yan, Yanli Song, Chenguang Li, Mengsu Zeng, Li Shen, Dijia Wu, Jiasheng Yin, and Junbo Ge.
This study moves beyond correlation and into imaging-based evidence. Low HRV has long been associated with worse cardiac outcomes, but this research links HRV directly to plaque risk features and arterial inflammation.
Participants underwent 24-hour Holter monitoring to measure HRV and coronary CT angiography to visualize plaques. Advanced analysis techniques were used to identify high-risk plaque characteristics.
Not all plaques are equal. Some are stable and unlikely to rupture. Others are inflamed, structurally fragile, and more likely to trigger a heart attack. A rupture is not just a mechanical accident. It’s often the end result of long-standing inflammation and biological instability in the vessel wall.
The study found that reduced HRV was associated with a higher risk of major adverse cardiovascular events. More importantly, low HRV was associated with markers of perivascular inflammation and the presence of high-risk plaques.
Mechanistically, chronic sympathetic activation can promote inflammatory signaling and vascular instability. Reduced vagal activity removes an important anti-inflammatory influence. Over time, that autonomic imbalance may contribute to plaque vulnerability.
So when HRV is chronically low, it may signal not only reduced resilience but also active cardiovascular risk at the structural level. HRV becomes a warning signal that the system is not regulating inflammation well, and that the cardiovascular system may be closer to instability than symptoms alone would suggest.
Now we shift to circadian science. The fifth paper is titled “Control vs. salience: a new axis of circadian brain-body organization,” published in Nature’s npj Biological Timing and Sleep. The authors are Olivier Demers, Sanaz Ghaffari, Chen Li, and Russell Butler.
This study reframes circadian health as coordination rather than just rhythm strength. Participants wore devices to track movement and heart rate for about a month and also underwent brain imaging.
The researchers identified a Control–Salience axis. Control refers to brain networks responsible for planning and top-down regulation. Salience refers to networks that monitor internal signals and determine what is important in the moment.
Some individuals showed strong behavioral rhythms but lagging heart-rate rhythms, suggesting that behavior was heavily driven by cognitive control. That can look like a life structured by obligations, scheduling, deadlines, and commitments — where behavior is consistent, but the body’s autonomic timing may not be perfectly aligned.
Others showed tighter synchronization between heart rate and movement, suggesting closer alignment between autonomic signals and daily behavior. That may reflect a different relationship between internal cues and daily action, in which the body’s signals and behavior move more closely together.
Chronic misalignment between cognitive schedules and autonomic readiness can contribute to fatigue, mood changes, and burnout. If you are repeatedly pushing yourself to be active and productive at times when your physiology is not aligned, that mismatch becomes a quiet source of stress.
This framework offers a way to quantify that mismatch and connect it to brain organization. It suggests that wearable data might reveal not just sleep issues, but deeper timing relationships between brain networks and autonomic systems.
Finally, we return to the beginning of life. The sixth paper is titled “Early Maturation of Heart Rate Variability in Very Preterm Infants Depends on Neonatal Factors and Is Associated With Neurodevelopmental Risk,” published in Acta Paediatrica. The authors are Léa Bonneau, Cyril Flamant, Maxime Esvan, Jean Michel Roué, Géraldine Favrais, Géraldine Gascoin, Sandie Cabon, Fabienne Porée, Guy Carrault, and Patrick Pladys.
In preterm infants, HRV is not just a resilience marker. It is a maturation marker.
These infants are developing autonomic regulatory systems outside the womb, in a complex medical environment. Their nervous systems are learning to regulate breathing, temperature, digestion, cardiovascular control, and state transitions — while also being exposed to stressors that full-term infants may never experience.
The researchers used HRV data to estimate functional maturation age, meaning how mature the autonomic system appears based on rhythm patterns. By comparing this estimate with the infant’s chronological age, they calculated the degree of maturation delay.
They found that lower gestational age at birth is associated with greater delay, and neonatal complications amplify that delay. But the most important part comes later. Developmental outcomes were evaluated at age two, and greater HRV maturation delay was associated with increased risk of social development difficulties.
Why social development? Because early social engagement depends on regulation — the ability to settle, to orient, to attend, to connect, and to recover. Those abilities rest on autonomic foundations. The vagal system plays a major role in supporting calm engagement and flexible state changes. If early autonomic development is delayed, it can signal a higher-risk developmental trajectory.
This positions HRV as a non-invasive, real-time window into early brain-body development. It also suggests a practical pathway for care: using physiological signals in the NICU to identify which infants may benefit from early developmental support, follow-up, and targeted interventions long before challenges become obvious.
As we look across all of these studies—from the machine-learned "textures" of meditation to the silent struggle of a preterm infant—a powerful, unifying theme begins to emerge. Heart rate variability is far more than just a number on a screen. It is a living, moment-by-moment record of our adaptability, our physiological history, and our capacity for resilience. It is the language of the nervous system made legible.
We've seen how it reflects the shared internal reorganization of meditation, the precise metabolic shifts of high-intensity exercise, and the hidden physiological stress of psychiatric disorders. It shows that our nervous systems are constantly reorganizing and adapting to the unique challenges we face, whether that is the stress of a clinical diagnosis or the simple daily "tug-of-war" between our cognitive schedules and our autonomic hearts. This neuroplasticity is both a gift and a challenge.
For the individuals listening, the takeaway is clear: your heart rate variability is a vital sign of your internal coherence. Practicing mindfulness isn't just about relaxation; it is about reorganizing your autonomic "texture" to be more resilient. Pay attention to your circadian archetype—are you schedule-led or heart-led? Understanding this can help you align your lifestyle with your biology to avoid social jetlag and the associated mood and cognitive risks. Most importantly, remember that low HRV is a long-term signal of cardiovascular vulnerability and inflammation, making it one of the most proactive tools you have for lifelong health.
For the professionals, this research provides the objective data you need to personalize care. We now know that HRV can act as an endophenotype in schizophrenia, linking autonomic balance directly to cognitive function. In sports and rehabilitation, remember the limitations of autonomic surrogates; while the 0.50 fractal threshold is a robust marker for the anaerobic transition, the 0.75 aerobic threshold is much more variable and requires personalized caution. In the NICU, HRV maturation is not just a physiological milestone; it is also a predictor of social development that can guide early intervention for vulnerable infants.
And for our researchers, the frontier is expanding into nonlinear dynamics and systems biology. The traditional focus on a "weak-strong" continuum is giving way to complex archetypes, such as the Control-Salience axis, which requires us to examine the coordination between locomotor and autonomic signals. The high accuracy achieved by texture-based machine learning models demonstrates that the HRV signal contains a wealth of information that linear metrics cannot capture. The challenge for future studies is to continue refining these endophenotypic markers and longitudinal models to move from observation to truly predictive healthcare.
Our future depends on our ability to translate this rich, complex data into meaningful action. Whether that means using DFA alpha to prevent overtraining in athletes, using HRV as an endophenotype to guide psychiatric care, or using autonomic maturation delays to provide early intervention for children, we are entering an era of truly personalized, neuro-visceral medicine.
And for everyone else, remember that your autonomic nervous system is incredibly flexible and trainable. You have the power to influence it every day through your choices—by finding moments of stillness, by pushing your body to its healthy limits, and by listening to the internal rhythms that coordinate your brain and your heart. Resilience isn't about avoiding stress; it is about building a system that can return to a state of calm and complexity.
HRV is the marker of that return. It is the ultimate metric of human adaptability. We are so grateful to the researchers doing this work and to all of you who are bringing this science into your lives and practices. Thank you for joining us for this expanded edition of the Heart Rate Variability Podcast. If you found these insights valuable, please subscribe and share this episode with your colleagues and friends. Your support helps us continue to explore the frontiers of autonomic health. I'm Matt Bennett, and we look forward to having you with us again next week. Until then, stay healthy, stay curious, and stay resilient.
