Heart rate variability, technology and applications

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HEART RATE VARIABILITY Marco Altini, PhD

Making it smaller

Maternal and fetal monitoring during

pregnancy Prediction of pregnancy complications

Labour detection

Load Data Scientist Bloomlife

Cardiorespiratory Fitness Estimation Energy Expenditure Estimation

Activity Recognition

PhD Applied Machine Learning

BSc, MSc Computer Science Engineering

Making it smaller

Heart Rate + Heart Rate Variability + Electrohysterography +

Blood Pressure Gestational hypertension prediction

Labour detection Preterm birth

Head of Data Science Bloom Technologies HRV4Training

THIS TALK

Historically HRV analysis has been poorly standardized, leading to: •  Difficulties in properly designing and

implementing studies •  Difficulties in comparing studies

outcomes

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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

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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

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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

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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

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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)

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WHY DO WE CARE?

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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

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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

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,

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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

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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

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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

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Higher HRV

Less physiologically stressed

Ready to perform

Lower HRV

More physiologically stressed

Tiredness

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Higher HRV

Less physiologically stressed

Ready to perform

Lower HRV

More physiologically stressed

Tiredness

Huge oversimplification

MAIN POINTS TO COVER





•  Applications

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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)

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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

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ECG

Basic signal processing: filtering, beat detection, feature computation

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CHEST STRAP + APP

Typically very accurate, recommend Polar H7 – we validated Polar H7 against full ECGs (healthy subjects)

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AdaptedfromTamuraetal.WearablePhotoplethysmographicSensors—PastandPresent

PPG

AdaptedfromTamuraetal.WearablePhotoplethysmographicSensors—PastandPresent

PPG

PPG SIGNAL PROCESSING

PPG SIGNAL PROCESSING

Choietal.Photoplethysmographysamplingfrequency:pilotassessmentofhowlowcanwegotoanalyzepulseratevariabilitywithreliability?(2017)

PPG SAMPLING FREQUENCY

PPG SAMPLING FREQUENCY

Choietal.Photoplethysmographysamplingfrequency:pilotassessmentofhowlowcanwegotoanalyzepulseratevariabilitywithreliability?(2017)

not a badge of honor

Liuetal.TowardsaSmartphoneApplicaPonforesPmaPonofpulsetransitPme(2015)

PPG INTERPOLATION

Liuetal.TowardsaSmartphoneApplicaPonforesPmaPonofpulsetransitPme(2015)

PPG INTERPOLATION

CAMERA VS ECG

VALIDATED PPG

HRV4Training provides validated camera based measurements

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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”

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OTHER PPG SENSORS: EAR, WRIST, ARM

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ANGEL SENSOR (RIP)

Hardware limitations?

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ANGEL SENSOR (RIP)

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•  Verynoisy

ANGEL SENSOR (RIP)

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•  Verynoisy

MIO ALPHA*

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*andmanyothers:targePngadifferentapplicaPon(reliableHRduringexercise),toomuchfilteringforHRVanalysis

VARIOUS PPG vs CHEST STRAP

Mio Alpha: case with too much averaging (note that it does capture some variability)

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KYTO EARCLIP

Kyto earclip: issues with higher HRV

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VARIOUS PPG vs CHEST STRAP

Kyto earclip: issues with higher HRV

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WRISTBANDS AND OTHER SENSORS

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WRISTBANDS AND OTHER SENSORS

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ZOOM HRV

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Accurate when corrected for artifacts (measurement time limited to 3 minutes)

ZOOM HRV

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Accurate when corrected for artifacts (measurement time limited to 3 minutes)






•  Applications

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CONFOUNDING FACTORS

AnumberofexternalfactorsareusuallycontrolledforinHRVresearch,includingtheintakeofnico%ne(Hayanoetal.,1990;SjobergandSaint,2011)andcaffeine(Sondermeijeretal.,2002)precedingdatacollecPon.CardioacPvemedica%onuse,includingsomeanPdepressantclasses(e.g.,tricyclics;Kempetal.,2010),someanPpsychoPcclasses(e.g.,clozapine;Cohenetal.,2001),benzodiazepines(Agelinketal.,2002),andanPhypertensives(Schroederetal.,2003)arealsousuallyaccountedfor,althoughthismaybesomewhatdifficultinpracPcewhentesPngpaPentpopulaPons.Otherfactorsthatareusuallyaccountedforincludethe%meofday(Massinetal.,2000;vanEekelenetal.,2004),levelsofhabitualalcoholuse(Quintanaetal.,2013a,b),physicalac%vitylevels(Briconetal.,2007;Soares-Mirandaetal.,2014),andage(O’Brienetal.,1986).Diges%onoffoodandwaterarelesscommonlyaccountedforinHRVresearch,butbothprovokeacoordinatedautonomicresponse.Forinstance,digesPngfoodhasbeenshowntoreduceparasympathePcacPvity,evenanhouragereaPnga500kcalmeal(Luetal.,1999).Evenexposuretofood-relatedcueselicitsasimilarresponse(Nederkoornetal.,2000),suggesPngaphysiologicalresponsetothean%cipa%onofameal.Conversely,missingameal(i.e.,fasPng)appearstohaveitsowncoordinatedeffectsonHRV(Piviketal.,2006),supporPngtherecommendaPonthatparPcipantsconsumealightmealapproximately2hbeforetheassessmentofHRV(Taketal.,2009).WaterconsumpPonhasalsobeenshowntoincreaseHF-HRVinparPcular(Routledgeetal.,2002),duetothevagalbufferingresponsetothepressoreffectprovokedbyhypo-osmoPcfluids(Scocetal.,2001).Notably,thisbufferingresponsetothepressoreffectisacenuatedinolderindividuals(Jordanetal.,2000)andnotobservedinthosewithcardiacvagaldenervaPon(Routledgeetal.,2002).InaddiPon,bothbladderandgastricdistensioncanalsohaveanappreciableinfluenceonHRV;thesehavebeenassociatedwithincreasesinbloodpressureandsympathePcoullow(FagiusandKarhuvaara,1989;Rossietal.,1998).However,papersonlyveryrarelyreportthatparPcipantswereaskedtoemptytheirbladderbeforeexperimentalparPcipaPon(Heathers,2014). Quintana et al.

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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

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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

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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

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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)

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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

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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

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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

ARE 60 SECONDS ENOUGH?

Yes.

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ARE 60 SECONDS ENOUGH?

Yes, but..

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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 …… ??

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MEASUREMENTS REPEATABILITY

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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

ECTOPIC BEATS

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ECTOPIC BEATS

rMSSD clean data: 79 ms rMSSD noisy data: 201 ms (ectopic beat) 65

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.)

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IT HAPPENS WITH ALL THE SENSORS

Chest strap (H7)

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PPG

•  Camera based (disruption caused by finger movement)

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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

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•  Applications

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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

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.

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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.

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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

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Spot check: •  Single measurement to analyze differences in

vagal tone between conditions, for example: HRV between healthy pregnancies, hypertensive pregnancies, controls.

75Yangetal.PreeclampPcpregnancyisassociatedwithincreasedsympathePcanddecreasedparasympathePccontrolofhr.

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.

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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)

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HRV change following training

TRAINING EXAMPLE

HRV change following training

TRAINING EXAMPLE

The day after rest or easy trainings

Higher HRV

TRAINING EXAMPLE

The day after average or intense trainings

Lower HRV

TRAINING EXAMPLE

TRAINING EXAMPLE

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ALCOHOL EXAMPLE

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rMSSD HeartRate


rMSSD HeartRate


> 1 million data points from 6000 users

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)

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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

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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

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EXAMPLE: night rMSSD during pregnancy

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EXAMPLE: morning rMSSD and chronic load to predict injury risk in Crossfit

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WilliamsS.etal."HeartRateVariabilityisaModeraPngFactorintheWorkload-InjuryRelaPonshipofCompePPveCrossFit™Athletes"JournalofSportsScienceandMedicine,2017

EXAMPLE: morning rMSSD and chronic load to predict injury risk in Crossfit

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WilliamsS.etal."HeartRateVariabilityisaModeraPngFactorintheWorkload-InjuryRelaPonshipofCompePPveCrossFit™Athletes"JournalofSportsScienceandMedicine,2017


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…..

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•  Accurate RR intervals •  Artifact removal •  Context / best practices •  More context…..

(Valid data)

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•  Applications

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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

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MEDICAL

•  Conditions where changes in HRV are associated with early manifestation

•  Risk stratification

•  Pharmacological responses

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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)

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PSYCHOLOGY

Better self-regulation (higher vagal tone / HRV) linked to a series of emotional / mental states, for example more social engagement

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AND MORE

•  Lifestyle

•  Sleep

•  Normal values

•  Etc.

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HEART RATE VARIABILITY Marco Altini, PhD

Date post:	22-Jan-2018
Category:	Healthcare
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Heart rate variability, technology and applications

Healthcare