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Heart rate variability, technology and applications

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Short bio:
Marco holds a PhD in applied machine learning (cum laude) and MSc in computer science engineering (cum laude). He is currently leading data science activities at Bloomlife, a digital health startup using wearable technology and data analytics to improve prenatal healthcare. Marco is also the creator of HRV4Training, a mobile platform that helps you making sense of physiological data.

Resources:
http://www.marcoaltini.com/
http://www.hrv4training.com/ (the blog is probably more interesting than the rest of the website: http://www.hrv4training.com/blog)

Talk overview:
Overview of heart rate variability (HRV) & physiological stress, with focus on tools, best practices, caveats, practical applications and limitations of a very valuable but easily misunderstood metric. I believe we can do a lot with what is available today, but we can also get easily fooled when performing a study if we do not understand all the critical components of a system, either because of lack of transparency from technology providers, or just because of the inherit complexity of measuring and interpreting physiology. For these reasons I will focus on points that should help in critically analyzing available tools, studies, and implementing processes leading to valid, replicable, outcomes.

Intro / What is heart rate variability (HRV)?
- Physiological mechanisms mediating heart rhythm, homeostasis, CNS, definition of HRV

How to collect data
- Technology (ECG, PPG, wrist based sensors, issues with artifacts)

Best practices
- Context, factors influencing HRV (confounders), features / metrics

What to do with the data / studies
- types of analysis, short term, long term, spot checks

Practical applications
- sports, medical, lifestyle, other research areas

Published in: Healthcare

Heart rate variability, technology and applications

  1. 1. 1 HEART RATE VARIABILITY Marco Altini, PhD
  2. 2. Making it smaller Maternal and fetal monitoring during pregnancy Prediction of pregnancy complications Labour detection Load Data Scientist Bloomlife
  3. 3. Cardiorespiratory Fitness Estimation Energy Expenditure Estimation Activity Recognition PhD Applied Machine Learning BSc, MSc Computer Science Engineering
  4. 4. Making it smaller Heart Rate + Heart Rate Variability + Electrohysterography + Blood Pressure Gestational hypertension prediction Labour detection Preterm birth Head of Data Science Bloom TechnologiesHRV4Training
  5. 5. THIS TALK Historically HRV analysis has been poorly standardized, leading to: •  Difficulties in properly designing and implementing studies •  Difficulties in comparing studies outcomes 5
  6. 6. THIS TALK The ease of access to HRV data today often obscures the complicated nature of understanding and correctly interpreting the information provided and underlying physiological processes Therefore the very nature of HRV itself may have led (or might lead in the future) to confusion for its use in applied research 6
  7. 7. THIS TALK The ease of access to HRV data today often obscures the complicated nature of understanding and correctly interpreting the information provided and underlying physiological processes Therefore the very nature of HRV itself may have led (or might lead in the future) to confusion for its use in applied research This talk is about trying to provide some clarity 7
  8. 8. THIS TALK •  What’s Heart Rate Variability? (HRV) –  Physiological mechanisms, definition •  How to collect data –  Technology •  Best practices –  Context, confounding factors •  What to do with the data –  Experiment design •  Applications 8
  9. 9. WHAT’S HRV? Each heart beat is triggered by an electrical impulse that can be captured by an electrocardiogram (ECG), one of the most common ways to measure heart activity However, heart beats are not constant in frequency, when we talk about heart rate variability, we are interested in capturing the variability that occurs between beats 9
  10. 10. BEAT TO BEAT VARIATION Differences between beats are called RR intervals (the name derives from the fact that the ECG shape has been assigned letters to identify different parts, namely the QRS complex) 10
  11. 11. WHY DO WE CARE? 11
  12. 12. STRESS & HOMEOSTASIS Homeostasis is about maintaining balance The body senses stress through its senses and sends information to the brain, which determines how to deal with it No matter the stressor, the body reacts in the same way 12
  13. 13. CENTRAL NERVOUS SYSTEM Changes in beat to beat variation reflect the output of the central autonomic network, which is responsible for our (visceral) response to stimuli (see Thayer et al.). HRV is mediated by neurons (with sympathetic and parasympathetic origin) and by the vagus nerve, however mainly dominated by parasympathetic (vagal) influence 13
  14. 14. AUTONOMIC NERVOUS SYSTEM Conducts impulses from the brain and spinal cord to smooth muscles, cardiac muscles, etc. Regulated by the hypothalamus, and in control of the fight or flight response Controls 80-90% of processes in the body Longer term mechanisms: circardian rhythm, body temperature, hormones Short term: respiration, baroreflex, 14
  15. 15. VAGUS NERVE Measure of parasympathetic activity, controls many organs, including the heart, and connects processes like respiration and blood pressure to the heart Instead of measuring directly the vagus nerve, we measure processes that the vagus nerve alters 15
  16. 16. HRV AS A PROXY TO VAGAL TONE HRV can capture changes in the ANS non- invasively, giving us insights into the body’s mental and physical abilities It does so by quantifying one of the main controllers of the ANS, which is the vagus nerve, a cranial nerve which brings information from the body to the brain The amount of vagal influence on the heart, vagal tone, can be measured by HRV 16
  17. 17. TO SUM UP The autonomic nervous system (ANS) controls and regulates many functions of our body and is in control of how our body reacts to stressors Heart Rate Variability (HRV) •  Regulated by sympathetic / parasympathetic branches of the ANS hence it provides insights into this control mechanism 17
  18. 18. 18 Higher HRV Less physiologically stressed Ready to perform Lower HRV More physiologically stressed Tiredness
  19. 19. 19 Higher HRV Less physiologically stressed Ready to perform Lower HRV More physiologically stressed Tiredness Huge oversimplification
  20. 20. MAIN POINTS TO COVER •  What’s Heart Rate Variability? (HRV) –  Physiological mechanisms, definition •  How to collect data –  Technology •  Best practices –  Context, confounding factors •  What to do with the data –  Experiment design •  Applications 20
  21. 21. HOW TO COLLECT DATA Electrocardiography (ECG): gold standard Chest strap + app: typically very accurate, recommend Polar H7/H10 (not all chest straps are equal) Photoplethysmography (PPG): HRV4Training, same as an ECG/Polar. Higher compliancy Wristbands, ear or arm sensors (also PPG): with some limitations Ballistocardiography (BCG): based on detecting body movement resulting from heart activity (e.g. mounted on your bed) 21
  22. 22. A NOTE ON SENSORS (I) consider them only if they either: -  Comply to standards (e.g. bluetooth 4.0 heart rate profile) which allow to collect basic information such as RR intervals -  Provide standard features such as rMSSD -  Provide other ways to access basic information such as RR intervals (if not the raw signal, ECG, PPG, BCG, etc.) Why is this important? Unfortunately most sensors are either locked behind proprietary software / apps or providing only custom metrics, hence they cannot be evaluated in their ability to do what they claim (can they actually measure HRV?), and they cannot be used by other apps Even if they do send RR intervals and comply to standards, it does not mean that they do so accurately 22
  23. 23. ECG Basic signal processing: filtering, beat detection, feature computation 23
  24. 24. CHEST STRAP + APP Typically very accurate, recommend Polar H7 – we validated Polar H7 against full ECGs (healthy subjects) 24
  25. 25. Adapted from Tamura et al. Wearable Photoplethysmographic Sensors—Past and Present PPG
  26. 26. Adapted from Tamura et al. Wearable Photoplethysmographic Sensors—Past and Present PPG
  27. 27. PPG SIGNAL PROCESSING
  28. 28. PPG SIGNAL PROCESSING
  29. 29. Choi et al. Photoplethysmography sampling frequency: pilot assessment of how low can we go to analyze pulse rate variability with reliability? (2017) PPG SAMPLING FREQUENCY
  30. 30. PPG SAMPLING FREQUENCY Choi et al. Photoplethysmography sampling frequency: pilot assessment of how low can we go to analyze pulse rate variability with reliability? (2017) not a badge of honor
  31. 31. Liu et al. Towards a Smartphone ApplicaPon for esPmaPon of pulse transit Pme (2015) PPG INTERPOLATION
  32. 32. Liu et al. Towards a Smartphone ApplicaPon for esPmaPon of pulse transit Pme (2015) PPG INTERPOLATION
  33. 33. CAMERA VS ECG
  34. 34. VALIDATED PPG HRV4Training provides validated camera based measurements 34
  35. 35. ECG vs PPG vs CHEST STRAP From Plews et al. “Comparison of HRV recording with smart phone PPG, Polar H7 chest strap and electrocardiogram methods” 35
  36. 36. OTHER PPG SENSORS: EAR, WRIST, ARM 36
  37. 37. ANGEL SENSOR (RIP) Hardware limitations? 37
  38. 38. ANGEL SENSOR (RIP) 38 •  Very noisy
  39. 39. ANGEL SENSOR (RIP) 39 •  Very noisy
  40. 40. MIO ALPHA* 40 *and many others: targePng a different applicaPon (reliable HR during exercise), too much filtering for HRV analysis
  41. 41. VARIOUS PPG vs CHEST STRAP Mio Alpha: case with too much averaging (note that it does capture some variability) 41
  42. 42. KYTO EARCLIP Kyto earclip: issues with higher HRV 42
  43. 43. VARIOUS PPG vs CHEST STRAP Kyto earclip: issues with higher HRV 43
  44. 44. WRISTBANDS AND OTHER SENSORS 44
  45. 45. WRISTBANDS AND OTHER SENSORS 45
  46. 46. ZOOM HRV 46 Accurate when corrected for artifacts (measurement time limited to 3 minutes)
  47. 47. ZOOM HRV 47 Accurate when corrected for artifacts (measurement time limited to 3 minutes)
  48. 48. MAIN POINTS TO COVER •  What’s Heart Rate Variability? (HRV) –  Physiological mechanisms, definition •  How to collect data –  Technology •  Best practices –  Context, confounding factors •  What to do with the data –  Experiment design •  Applications 48
  49. 49. CONFOUNDING FACTORS A number of external factors are usually controlled for in HRV research, including the intake of nico%ne (Hayano et al., 1990; Sjoberg and Saint, 2011) and caffeine (Sondermeijer et al., 2002) preceding data collecPon. CardioacPve medica%on use, including some anPdepressant classes (e.g., tricyclics; Kemp et al., 2010), some anPpsychoPc classes (e.g., clozapine; Cohen et al., 2001), benzodiazepines (Agelink et al., 2002), and anPhypertensives (Schroeder et al., 2003) are also usually accounted for, although this may be somewhat difficult in pracPce when tesPng paPent populaPons. Other factors that are usually accounted for include the %me of day (Massin et al., 2000; van Eekelen et al., 2004), levels of habitual alcohol use (Quintana et al., 2013a,b), physical ac%vity levels (Bricon et al., 2007; Soares-Miranda et al., 2014), and age (O’Brien et al., 1986). Diges%on of food and water are less commonly accounted for in HRV research, but both provoke a coordinated autonomic response. For instance, digesPng food has been shown to reduce parasympathePc acPvity, even an hour ager eaPng a 500 kcal meal (Lu et al., 1999). Even exposure to food-related cues elicits a similar response (Nederkoorn et al., 2000), suggesPng a physiological response to the an%cipa%on of a meal. Conversely, missing a meal (i.e., fasPng) appears to have its own coordinated effects on HRV (Pivik et al., 2006), supporPng the recommendaPon that parPcipants consume a light meal approximately 2 h before the assessment of HRV (Tak et al., 2009). Water consumpPon has also been shown to increase HF-HRV in parPcular (Routledge et al., 2002), due to the vagal buffering response to the pressor effect provoked by hypo-osmoPc fluids (Scoc et al., 2001). Notably, this buffering response to the pressor effect is acenuated in older individuals (Jordan et al., 2000) and not observed in those with cardiac vagal denervaPon (Routledge et al., 2002). In addiPon, both bladder and gastric distension can also have an appreciable influence on HRV; these have been associated with increases in blood pressure and sympathePc oullow (Fagius and Karhuvaara, 1989; Rossi et al., 1998). However, papers only very rarely report that parPcipants were asked to empty their bladder before experimental parPcipaPon ( Heathers, 2014). Quintana et al. 49
  50. 50. HOW DO WE CONTROL FOR THEM? •  Depends on research question, most commonly: –  Response to acute stressor (drug, food, activity, psychological, anything to challenge the ANS.) •  pre-post measurement within individuals –  24 hours recordings: affected by anything, less useful within individual (more like a between individual difference across macro-categories, like chronic disease and healthy) •  Select similar subjects across groups? –  Progression of certain conditions, adaptation, etc.: less traditional (or less standardized) but more common methods (due to technological developments) such as long term / longitudinal, spot checks or night measurements •  Create replicable situations w/ clear context 50
  51. 51. BEST PRACTICES FOR LONGITUDINAL MEASUREMENS Physiology (the ANS) responds acutely to basically anything (activity, food / drink intake, mental stress, etc.) – data needs to be contextualized to be correctly interpreted •  How/when to take the measurement –  Morning, still in bed?, etc. •  What type of measurement –  Lying down, sitting? •  What metric to use? –  Time domain, frequency domain? •  Are 60 seconds enough? •  Other issues / recommendations 51
  52. 52. HOW/WHEN TO MEASURE •  First thing after waking up – Relaxed physiological state – Limit all external stressors – Closest to what we do in research / clinical studies – Highly correlated with night measurements – Don’t read your email before the measurement 52
  53. 53. HOW/WHEN TO MEASURE The inability of daily measurements to reflect underlying physiological stress was also shown recently by Mesquita et al.: Analyzing RMSSD from daily routine activities was not reliable, and therefore validity cannot be assumed •  Acute stressors during the day prevent reliability / repeatability •  This is even assuming that rMSSD can be captured correctly during daily activities, which is not to take for granted when using PPG based devices (artifacts) 53
  54. 54. WHAT TYPE OF MEASUREMENT •  Lying down while still in bed –  Limits other factors like not waiting enough after standing up –  Performed in clinical studies –  Sitting/Standing also valid, however for simplicity I’d recommend lying down unless your heart rate is very low (<40 bpm, in this case sitting or standing might be preferable) –  Going to another room also fine, take time to relax –  Highly discourage measuring at the lab / sport facilities / outside of your house 54
  55. 55. WHAT METRICS TO USE •  HRV is not a single number •  Use rMSSD or ln rMSSD (or HF) – Marker of parasympathetic activity (only thing you can reliably measure). There is no clear sympathetic marker – HF, LF, HF/LF or other frequency domain features require more time (and are computed differently by everyone, difficult to generalize/compare), more influenced by breathing / RSA 55
  56. 56. WHY RMSSD OR HF? The vagus nerve acts on receptors signaling nodes to modulate pulse on a beat to beat basis while sympathetic activity has different pathways with slower signaling hence beat to beat changes reflect parasympathetic activity (rMSSD). Vagal influence: very short latency (less than 1s), while sympathetic influence is too slow to result in beat to beat differences (4-20s, see Nunan et al.). 56
  57. 57. ARE 60 SECONDS ENOUGH? Yes. 57
  58. 58. ARE 60 SECONDS ENOUGH? Yes, but.. 58
  59. 59. MEASUREMENT FREQUENCY •  While 4-5 times/week will get you a good baseline, valuable information might be lost (e.g. weekly variability in measurements). Less than 3 measurements per week might be insufficient to get a reliable baseline •  Single morning spot check pre/post intervention …… ?? 59
  60. 60. MEASUREMENTS REPEATABILITY 60
  61. 61. MEASUREMENTS REPEATABILITY 61
  62. 62. MEASUREMENTS REPEATABILITY 62
  63. 63. ARTIFACTS HRV data is highly affected by artifacts, either in the measurement device (wrong beat detected, movement for PPG sensors), or in the actual data (ectopic beat, arrhythmias) that need to be handled properly. Check your app / device and ask what methods are used to deal with artifacts 63
  64. 64. ECTOPIC BEATS 64
  65. 65. ECTOPIC BEATS rMSSD clean data: 79 ms rMSSD noisy data: 201 ms (ectopic beat)65
  66. 66. ECTOPIC BEATS Identifying ectopic beats is done in clinical practice by removing RR intervals that differ more than 20 or 25% from the previous one, since that’s very uncommon and most likely is indicative of a problem (ectopic beat, noise, motion, etc.) 66
  67. 67. IT HAPPENS WITH ALL THE SENSORS Chest strap (H7) 67
  68. 68. PPG •  Camera based (disruption caused by finger movement) 68
  69. 69. ARTIFACTS ECG recordings are the only ones that allow the researcher to see the QRS complex and hence the heart beats, leading to accurate identification of possible issues and meaningful correction In certain cases (certain arrhythmias) it is simply not possible to compute HRV 69
  70. 70. MAIN POINTS TO COVER •  What’s Heart Rate Variability? (HRV) –  Physiological mechanisms, definition •  How to collect data –  Technology •  Best practices –  Context, confounding factors •  What to do with the data –  Experiment design •  Applications 70
  71. 71. EXPERIMENT DESIGN •  Between subject – 24 hours measurements (typical medical studies in the early days of HRV analysis) – Single spot check •  Within subject –  Acute changes in response to a stressor (pre/post) –  Longitudinal data collection: spot checks in the morning or night measurements 71
  72. 72. EXPERIMENT DESIGN Given high inter-individual variations and the complex interactions influencing HRV, within- subject designs are highly recommended Within-subject designs offer optimal experimental control, contribute to the elimination of individual differences in respiratory rates and reduce the impact of external factors such as medication, alcohol, smoking, etc.  Quintana and Heathers et al. 72
  73. 73. BETWEEN SUBJECT EXPERIMENTS Typical studies in medical literature (40 years ago to now). Non-linear relationship when analyzing data across individuals •  At lower HRs there is more time between heartbeats and variability naturally increases. 73
  74. 74. BETWEEN SUBJECT EXPERIMENTS 24 hours measurements: •  Macro-differences in physiology between specific medical conditions and healthy controls •  Dependent on physical activity behavior and other confounding factors •  Would speculate most differences between groups detectable by SDNN over 24 hrs is also captured by morning / night measurements of rMSSD or HF 74
  75. 75. BETWEEN SUBJECT EXPERIMENTS Spot check: •  Single measurement to analyze differences in vagal tone between conditions, for example: HRV between healthy pregnancies, hypertensive pregnancies, controls. 75 Yang et al. PreeclampPc pregnancy is associated with increased sympathePc and decreased parasympathePc control of hr.
  76. 76. WITHIN SUBJECT EXPERIMENTS •  Acute HRV changes: day to day variations to acute stressors (or intra-day) –  intense workout –  getting sick –  Travel –  Meds –  Food –  etc. •  Longitudinal data: long term/chronic changes in baseline values –  Adaptation to training / overtraining –  Development of a chronic condition –  Pregnancy approaching labour –  etc. 76
  77. 77. ACUTE STRESSORS Most examples are shown from HRV4Training data – Real-life – Contextualized (but in real-life J) – Clear signal processing pipeline (artifacts, etc.) – Covers many situations (training, travel, alcohol intake, menstruation, getting sick, etc.) – Sample size (for population derived analysis) 77
  78. 78. HRV change following training TRAINING EXAMPLE
  79. 79. HRV change following training TRAINING EXAMPLE
  80. 80. The day after rest or easy trainings Higher HRV TRAINING EXAMPLE
  81. 81. The day after average or intense trainings Lower HRV TRAINING EXAMPLE
  82. 82. TRAINING EXAMPLE
  83. 83. −8 −4 0 4 8 20 to 3020 to 3030 to 4030 to 4040 to 5040 to 5050 to 6050 to 60 Age groups HRchange(%) Training Low training load High training load HR change −8 −4 0 4 8 20 to 3020 to 3030 to 4030 to 4040 to 5040 to 5050 to 6050 to 60 Age groups rMSSDchange(%) HRV (rMSSD) change TRAINING EXAMPLE
  84. 84. TRAVEL EXAMPLE
  85. 85. TRAVEL EXAMPLE
  86. 86. ALCOHOL EXAMPLE
  87. 87. GETTING SICK EXAMPLE rMSSD Heart Rate
  88. 88. GETTING SICK EXAMPLE rMSSD Heart Rate
  89. 89. GETTING SICK EXAMPLE > 1 million data points from 6000 users
  90. 90. ACUTE STRESSORS Acute stressors are typically quite strong, and can be detected even when analyzed in isolation as we’ve just seen However, we should remember that all of the above factors (and many others) act simultaneously on the ANS (-> collect contextual data, physiological measurements per se are pretty useless) 90
  91. 91. LONGITUDINAL DATA COLLECTION –  Enabled by today’s technologies •  Measurements can be taken in optimal conditions (in terms of context and reductions of confounders, e.g. first thing in the morning). Much better than going to the lab (every day?) •  Compliancy increases as measuring requires less bulky technology / sensors (or no sensors at all) – Powerful analysis: within-subject, longitudinal 91
  92. 92. LONGITUDINAL DATA COLLECTION Examples: -  Progression of specific conditions, healthy ones (e.g. pregnancy) as well as disease (typical reduction in vagal tone, see diabetic neuropathy) -  Adaptation to specific environments or activities: -  Altitude adaptations (might be reflected in the degree of the reduction in HRV) -  Training block / overtraining 92
  93. 93. EXAMPLE: night rMSSD during pregnancy 93 10 20 30 40 Pregnancy week ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● 20 40 60 10 20 30 40 Pregnancy week rMSSD(ms) Daily heart rate variability over pregnancy (night only) 20 40 60 rMSSD(ms)
  94. 94. EXAMPLE: morning rMSSD and chronic load to predict injury risk in Crossfit 94 Williams S. et al. "Heart Rate Variability is a ModeraPng Factor in the Workload-Injury RelaPonship of CompePPve CrossFit™ Athletes" Journal of Sports Science and Medicine, 2017
  95. 95. EXAMPLE: morning rMSSD and chronic load to predict injury risk in Crossfit 95 Williams S. et al. "Heart Rate Variability is a ModeraPng Factor in the Workload-Injury RelaPonship of CompePPve CrossFit™ Athletes" Journal of Sports Science and Medicine, 2017
  96. 96. LONGITUDINAL DATA COLLECTION As pretty much anything affects the ANS, collecting longitudinal data representative of vagal tone can provide insights in many complex mechanisms taking place in health and disease Provided that we have….. 96
  97. 97. LONGITUDINAL DATA COLLECTION •  Accurate RR intervals •  Artifact removal •  Context / best practices •  More context….. (Valid data) 97
  98. 98. MAIN POINTS TO COVER •  What’s Heart Rate Variability? (HRV) –  Physiological mechanisms, definition •  How to collect data –  Technology •  Best practices –  Context, confounding factors •  What to do with the data –  Experiment design •  Applications 98
  99. 99. MEDICAL Classic literature answering the question “does the HR dynamically respond?” Even just at the day / night level, or during challenging tasks The inability of the physiological self- regulatory systems to adapt to the current context and situation is associated with numerous clinical conditions 99
  100. 100. MEDICAL •  Conditions where changes in HRV are associated with early manifestation •  Risk stratification •  Pharmacological responses 100
  101. 101. SPORTS Widespread for a simple reason: highly engaged user and easily quantifiable stressor (training) Faster feedback loop as well (performance, competition), with respect to other applications (e.g. aging J or progression of a specific disease) 101
  102. 102. PSYCHOLOGY Better self-regulation (higher vagal tone / HRV) linked to a series of emotional / mental states, for example more social engagement 102
  103. 103. AND MORE •  Lifestyle •  Sleep •  Normal values •  Etc. 103
  104. 104. 104 HEART RATE VARIABILITY Marco Altini, PhD

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