Measuring autonomic activity Heart rate variability Centre for Doctoral Training in Healthcare Innovation

Transcription

1 Measuring autonomic activity Heart rate variability Centre for Doctoral Training in Healthcare Innovation Dr. Gari D. Clifford, University Lecturer & Director, Centre for Doctoral Training in Healthcare Innovation, Institute of Biomedical Engineering, University of Oxford

2 Autonomic regulation HRV metrics Time domain Spectral Nonstationary Short term PRSA, HRT Long term wavelet scaling Dealing with noise in the time series Resampling issues Removing abnormal beats ECG-derived respiration (EDR) Physical Autonomic

3 Rest & Digest Fight & Flight

4 ANS autoregulates heart through SA node So measure HRV to gain insight into how autoregulation is working Why? To provides a metric of health by looking for departures form normality (given demographics)

5 Sequence of RR intervals is called Tachogram 60/.RR interval = Instantaneous HR Plot (t,rr) (time vs differential of time!) Now you can employ signal processing on the data! Data taken from PhysioNet;

6 Oscillations in RR tachogram from: Simple example: RSA Also changes due to blood pressure Myogenic changes? Smooth muscle Diurnal variations (temp, sleep, activity) Sudden changes ectopy, arrhythmia

7 ECG, HR and Respiration: HR and respiration highly correlated Resp rate is highest Freq component ~ Hz

8 HRV is a quantification of variation of the beat-to-beat intervals Frequency domain analysis is traditionally split into 4 frequency bands (ULF, VLF, LF & HF) representing the 4 (approx. distinct) time scales over which cardiovascular variations are thought to occur HF ( Hz): Vagal/parasympathetic variations over seconds (e.g. respiration) LF ( Hz): Sympatheticover minutes (BP, Meyer waves) VLF ( Hz): Myogenic? Variations over hours, e.g. temp. ULF ( Hz):Circadian e.g. activity nonstationarities

9 Sympathetic & parasympathetic braches of CNS act in opposition Think of it like 2 pedals in a car both are accelerators AND breaks (innervate / inhibit) The sympathetic brake/accelerator is less sticky Fight/flight response = rapid sympathetic innervation, and slower parasympathetic inhibition (note that parasympathetic action leads to higher frequency oscillations in RR tachogram why?) LF HF The ratio of the LF & HF power reflects the autonomic balance between these continuously interacting inhibitory and innervating actions of the CNS Small values indicate you are relaxing Large values indicate a highly active system e.g. when you are running Elevated values when you are relaxing indicate health problem

10 Record ECG Detect Peaks Calculate RR interval (time between each R peak) Remove RR intervals associated with noise & non-sinus beats (& following N beats?) Interpolate through missing data (insert phantom beats) if using cubic spline? [or cut data] (why do we remove non-sinus beats?) Resample / interpolate time series WHY?

11 Data is unevenly sampled! Nyquist not strictly defined

12 Data is unevenly sampled! Nyquist not strictly defined Nyquist frequency = 1/mean(RR) Hz (generally 0.5Hz for 60 BPM) Some frequencies above this, so you can beat Nyquist through uneven sampling But the accuracy at higher frequencies depends on number of samples with corresponding intervals

13 Data courtesy of PhysioNet;

14 Non-sinus beats are not reflective of SA node activity They lead to nonstationarities in time series so we remove a few following beats Insert phantom beats to create stability for nonlinear interp Resample to make an evenly sampled time series Generally you remove any RR intervals which change by more than 20% on the previous RR interval Example with linear interpolation Note phantom beat is interpolated with linear interp so phantom not needed here

15 Depends on task. Is (average) Nyquist the right resampling frequency? Smallest RR interval possible?? Hz For respiration, what s the fastest rate?? Hz Autonomic information up to? Hz

16 Depends on task. There are frequencies above average Nyquist! Smallest RR interval possible? 200ms 10 Hz But HRV only for sinus beats: <160 BPM ~6 Hz Fastest respiration rate? 60 RPM 2 Hz Autonomic information up to 1 Hz 2 Hz (In reality you need to sample a bit faster than Nyquist)

17 Many measures, some parametric, some nonparametric. Work over many scales Some deal with non-stationarities, some do not

18 Sine wave, with standard deviation calculated over different lengths Eventually it tends to a limit, but local measures can be unrepresentative

19 Time domain (assumes stationarity) Standard frequency domain (assumes stationarity) Scaling over 24 hours Nonlinear measures (sample entropy) Multi-scale entropy

20 Recap: Moments of a distribution Continuous Discrete approx Mean Var General:

21 x

22 Gaussians are mesokurtic with κ =3 SubGaussian SuperGaussian x

23 Approximations of distributions: AVNN: SDNN: SDANN: Average of all NN intervals Standard deviation of all NN intervals Standard deviation of the averages of NN intervals in all 5-minute segments of a 24-hour recording SDNNIDX: Mean of the standard deviations of NN intervals in all 5-minute segments of a 24-hour recording rmssd: Square root of the mean of the squares of differences between adjacent NN intervals pnn50: Percentage of differences between adjacent NN intervals that are > 50 ms. This is one member of the larger pnnx family

24 LF, HF, LF/HF, VLF, ULF Why these bands? Chemical experiments provide evidence HF ( Hz): Vagal/parasympathetic variations over seconds (e.g. respiration) LF ( Hz): Sympathetic- over minutes (BP, Meyer waves) VLF ( Hz): Myogenic? Variations over hours, e.g. temp. ULF ( Hz):Circadian e.g. activity nonstationarities

25 Tachogram has many states with similar means or variances HRi Length of state varies minutes (weakly stationary) Movements between states have brief accelerations in RR interval new mean and/or variance. Time (40 mins)

26 24 hour spectrum of RR intervals exhibits 1/f β scaling β indicates type of long term correlation β=2 : Brownian motion β=1 : Pink (natural) noise β=0 : White noise (no long-term correlation) Measure of fractal properties? (Probably not) Scaling should be not too white, or too brown. Pink is normal for humans How do we measure β since HRV is not stationary? Hint: not with Fourier.

27 Entropy kln(w) is a measure of disorder the more random the time series, the more disorder HRV should have some randomness, but not too much. (c.f. scaling) More info at:

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29 Only short segments of data required Unit-free - no scaling issues Thought to reflect the sympathovagal balance

30 HRV changes significantly in different sleep cycles and for different conditions: Wakefulness Light Sleep Deep Sleep REM (Dream) Sleep

31 Activity : LF/HF ratio : Spontaneous Breathing Controlled Breathing (15 rpm) Controlled Breathing (18 rpm) Silent Reading Reading Aloud Free Talking Performing mentally stressful tasks silently (e.g. arithmetic) Performing mentally stressful tasks aloud Changes can be larger than inter-patient differences with different pathologies (Bernardi et al.)

32 HRT Heart rate turbulence a cardiovascular response to ectopy PRSA Phase rectified signal averaging the normal response of HR accelerations and decelerations

33 Data courtesy of PhysioNet;

34 SA node response to ectopic beat; short HR acceleration then deceleration. Maintain BP; rapid parasympathetic withdrawal? Then parasympathetic innervation baseline Credit: R. Schneider:

35 Ectopic beats disturb RR tachogram stationarity Disturbance lasts beats HRT quantifies this disturbance using 2 metrics: TO: Turbulence Onset TS: Turbulence Slope

36 Credit: Bauer A, Barthel P, Schneider R, Schmidt G. Dynamics of Heart Rate Turbulence. Circulation 2001b; Vol. 104; No. 17; Supplement; II-339, 1622.

37 (+ index intervals after ectopic, - index before) Percentage difference between mean of each pair of NN intervals on either side of ectopic pair Must average the TO over >> 10 ectopics

38 Find steepest slope for each possible sequence of 5 consecutive normal intervals from RR +2 RR +16 Usually average time series first then calculate one TS on the average time series! Outlier Rejection Important: (See Notes)

39 Run: Figure Credit: Mäkikallio et al., Eur. Heart J., April 2005; 26:

40 TO < 0 and TS > 2.5 are normal (a healthy response to PVCs is a strong sinus acceleration followed by a rapid deceleration)

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42 An independent predictor of late mortality after acute MI [Schmidt 1999, Ghuran 2002, Wichterle 2004, Watanabe 2005, Baur 2006] Abnormal HRT Predicts Initiation of Ventricular Arrhythmias [Iwasa 2005] HRT indices appear to correlate better with EF than SDNN in Chagas disease [Tundo2005] HRT Predicts Cardiac Death in Patients Undergoing CABG [Cygankiewicz 2003] Prognostic Marker in Patients with Chronic Heart Failure [Kayama 2002] Risk Predictors in Patients With Diabetes Mellitus [Barthel 2000 Barthel 2002] LF/HF ratio indicates stress [Healey others] LF/HF ratio separates normal and sleep apneoic patients (with sleep stage) [Clifford 2003] LF/HF ratio used to screen patients who are responsive to sleep treatments [Campana 2011]

43 HRV is scale dependent short and long term metrics exist Some metrics appropriate only to short scales Can deal with nonstationarities by using: Perturbation analysis Signal averaging Short term boxing Nonstationary measures e.g. wavelets. Can also measure how HRV changes over scale Must be careful to remove noise from data first Choose resample rate based on task! (Always true of all pre-processing there s no magic pre-processing unit) Try to use a smooth resampler if you must or avoid resampling (advanced) HRV very useful in risk stratification, but often need to use some other measure univariate analysis is limited

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46 SNA: Sympathetic Nerve Activity (recorded from peroneal nerve)

47 What if there is noise in the data (or ectopy)?

48 Abnormal or Non-sinus beats (ectopics) generally appear earlier (or sometimes later) than when a sinus beat is expected Ectopic beats usually replace the sinus beat and are followed by a compensatory pause. Their frequency is on average about 1 per hour for the NSRDB Artefacts occur are additional and occur independently of the phase of the sinus rhythm with a recording method-dependent distribution.

49 Linear/cubic interpolation of RR intervals then perform FFT Over-estimation of LF and under-estimation of HF Use Lomb-Scargle periodogram to avoid interpolation

50 Spectral estimation of unevenly sampled data without resampling Variable integration step size Equivalent to least squares fitting of sines to data! Scargle, J. D. (1982). "Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data". Astrophysical Journal 263: 835. doi: /160554