Heart-rate Variability Christoph Guger, 10.02.2004
Heart-rate Variability (HRV) 1965 Hon & Lee Fetal distress alterations in interbeat intervals before heart rate (HR) changed 1980 HRV is strong and independent predictor for mortality following myocardial infarct Signs of increased sympathetic and reduced vagal activity has triggered the development of quantitiative markers Content: Standardization and definition of terms Standard methods for measurement Physiological and pathophysiological correlation HRV in high-altitude medicine
QRS Complex Detection Continuous ECG - > find the QRS complexes in the time series. This signal is non-uniformly sampled and can be shown as Tachogram (the RR intervals are plotted over the number of RR intervals).
HRV Time Domain Measures In a continuous ECG recording the normal-to-normal (NN or RR) intervals can be determined (interval from one QRS complex to the next). Simple Time Domain Measures Complex Time Domain Measures a) derived from direct measurement of NN intervals b) derived from difference between adjacent NN intervals This measure can be used to investigate variations of the heart rate secondary to tilt, Vasalva manoeuvre, to describe the difference between night and day,
Simple Time Domain Measures Mean RR interval: e.g. 1000 ms or 256 samples Mean heart rate: e.g. 60000 ms / 1000 ms = 60 bpm Minimum RR interval: e.g. 700 ms Maximum RR interval: e.g. 1200 ms Difference between RRmin and RRmax: 500ms Difference between night and day,... From entire recording or from smaller segments -> allows comparision e.g. rest, sleep,...
Complex Time Domain Measures SDNN... Standard deviation of RR intervals SDNN = SQRT ( VAR(RR) ) Reflects the cyclic components responsible for variability in the period of recording Normally over 24 hours -> describes short term high frequency variations and low frequency components! Recording Period -> SDNN estimates shorter -> shorter cycles Total variance increases with recording length -> SDNN depends on recording length Inappropriate to compare SDNN measures of different recording length Standardize recording length: 5 min or 24 h
Segmented Measures SDANN standard deviation of the average NN interval calculated over short periods e.g. 5 minutes µ1 µ2 µ3 µ4... µn SDANN = STD ( [µ1, µ2, µ3, µ4,... µn]) Estimates changes in heart rate due to cycles longer than 5 minutes (for 5 min intervals) SDNNindex mean of 5 min standard deviation of NN intervals calculated over 24 h STD1 STD2 STD3 STD4... STDN SDNNindex = MEAN ( [STD1, STD2, STD3, STD4,... STDN]) Measures variability due to cycles shorter than 5 min (for 5 min intervals)
Interval Differences RMSSD square root of the mean squared difference of successive NN intervals D1 D2 D3 D4... DN D1= D2-D1 D2= D3-D2... DN RMSSD = SQRT ( MEAN ([ D1 2, D2 2,... DN 2 ]) )
Interval Differences NN50 number of intervals of successive NN intervals greater than 50 ms Segments must be equal!! D1 D2 D3 D4... DN D1= D2-D1 D2= D3-D2... DN NN50 = NN50+1 if Di > 50 ms pnn50... NN50 / (total number of NN intervals)
Geometric Measures Histogram of RR intervals a) Basic measurements of the geometric pattern e.g width of the histogram b) Geometric pattern is interpolated by mathematically defined shape (e.g. exponential curve) Most experience with bins of 8 ms (128 Hz)
Geometric Measures HRVtriangular index integral of density distribution / maximum of density distribution maximum number HRVtriangular index = Number of all NN intervals / maximum number Dependent on the length of the bin -> quote the bin size + relative insensitive to the analytic quality of the series of NN intervals - need of reasonable number of NN intervals to generate the geometric pattern (in practice 20 min to 24 h) - not appropriate to assess short-term changes in HRV
Summary and Recommendation Many measures correlate closely -> following measures are recommended SDNN estimate of overall HRV HRVtriangular index estimate of overall HRV SDANN estimate of long term components RMSSD estimate of short term components Easy pre-processing Methods for overall HRV, short and long-term can NOT replace each other Method should correspond to aim of study Do NOT compare overall measures from recordings with different duration
Tilt Table Experiment: HRV Time Domain Measures Rest Tilt
Tilt Table Experiment: Histogram 4 th Presencia meeting - TUG
HRV Frequency Domain Measures Spectral analysis provides information how power is distributed as function of frequency (Power Spectral Density PSD) -> detects periodic oscillations Non-parametric methods Fast Fourier Transformation (FFT) + simple and fast Parametric methods + smoother spectral components + easy post-processing of the spectrum + easy identification of the central frequency + accurate estimate of PSD even for small number of samples - order of model must be chosen
HRV Frequency Domain Measures Absolute Measures ULF ultra low frequency band <0.003 Hz VLF very low frequency band 0.003 0.04 Hz LF low frequency band 0.04 0.15 Hz HF high frequency band 0.15 0.4 Hz The distribution of LF and HF is not fixed and varies with autonomic modulation of the heart rate The energy in HF is vagal mediated The energy in LF and VLF are due to both sympathetic and parasympathetic systems Relative Measures The normalization minimizes the effect of changes of Total Power (TP) on LF and HF Lfnorm = LF / (TP-VLF), unit n.u. Hfnorm = HF/(TP-VLF), unit n.u. LF/HF
HRV Frequency Domain Measures Shows balanced behavior of the 2 branches of the autonomic nervous system Normalized units should be quoted with absolute values to describe the distribution of spectral components Short term recording (5 minutes) VLF is not reliable Long term recording (24 hours) Results include also ULF Problem that heart period modulation responsible for LF and HF is not stationare during 24 h
Tilt Table Experiment: HRV Frequency Domain Measures Rest Tilt
Tilt Table Experiment: HRV Map
Technical Requirements Deviation from following requirements may lead to unreproduceable results that are difficult to interpret Sampling rate Low sampling rate produces jitter in R wave point -> alters spectrum Optimal: 256 500 Hz or higher If sampling rate is lower (in any case >100 Hz) -> use interpolation algorithm to refine R wave point QRS point must be accurate Ectopic beats, arrhythmic events, missing data and noise effects the estimation of PSD -> use linear interpolation to reduce error -> use error free data
Technical Requirements Filter Filter much lower than 200 Hz creates jitter in QRS point -> error in RR interval Editing Manuel editing of QRS complexes must be very accurate Automatic filters can NOT replace manuell editing (remove intervals which differ more than 20 % from previous interval)
Algorithm Standards QRS detection produces event series Ri Ri-1 versus time -> irregularly time sampled signal: Tachogram Spectrum is calculated of Tachogram or of interpolated DES Recommendation: Tachogram + parametric PSD estimation DES + non-parametric PSD estimation or DES + parametric PSD estimation Sampling frequency of DES must be high enough that Nyquist frequency is not in frequency range of interest (2 Hz or 4 Hz) Windowing function: Hanning, Hamming or triangular Order of parametric method: 8-20
Correlation between Time and Frequency Domain Time Domain SDNN HRVtriangular index SDANN SDNNindex RMSSD SDSD NN50 pnn50 Frequency Domain TP TP ULF Mean of 5 min TP HF HF HF HF Stability of HRV Many studies show that short-term measures return to baseline after mild exercise,... More powerful stimuli, e.g. maximum exercise result in longer effect
Duration of ECG recording Duration is dictated by nature of investigation -> standardization is needed FDM for short-term recording At least 10 times the wavelength of the LF band 1 min is needed for HF 2 min are needed for LF 5 min should be used for standardization Use Compressed Spectral Array to show variations over long time SDNN, RMSSD can be used for short term, but FD are more easily to interpret TDM are ideal for long-term FDM are difficult to interpret for long-term
Physiological correlates of HRV HR and HRV are largely under control of autonomic nervous system Parasymphathetic influence on HR is mediated by vagus nerve (release of acetylcholine -> slow diastolic depolarization) Sympathetic influence on HR is mediated by epinephrine and norepinephrine -> acceleration of the diastolic depolarization Under rest: vagal tone is prominent variations are largely dependent on vagal modulation vagal and sympathetic activity interact vagal impulses are brief (acetylcholine is rapidly hydrolyzed) Vagal activity inhibits sympathetic activity and vice versa
Physiological correlates of HRV HRV is modulated by central (vasomotor and respiratory centres) and peripheral (oscillations in arterial pressure and respiratory movements) oscillations HF... Vagal activity LF... Marker for sympathetic modulation (when expressed in n.u.) and marker for sympathetic and parasympathetic modulation Discrepance: under exercise (SA ) LF is reduced! Explanation: during SA -> Tachycardia -> TP during PA -> TP Therefore, use normalized units SA -> TP : Lfnorm= LF/(TP ) -> Lfnorm PA : Lfnorm=LF/(TP ) -> Lfnorm
Tilt Table Experiment: HRV Frequency Domain Measures Rest Tilt Lfnorm : tilt, standing, mental stress, moderate exercise, physical activity Hfnorm : controlled respiration, cold stimulus in face,... LF/HF show balance of SA and PA
HRV changes related to pathology Myocardial infarction HRV after MI -> PA, SA Heart transplantation HRV, no definite spectral components Myocardial dysfunction HRV Tetraplegic patients Patients with complete high spinal cord lesion have intact efferent vagal and sympathetic neural pathways to sinus node. But spinal sympathetic neurons have no control of baroreflex inhibitory impuls LF
Tests of g.mobilab on Dachstein What 4 th Presencia meeting - TUG
Effects of a fast ascent with the cable car on EEG and ECG Measurements in the Dachstein (3000 m) region Altitude difference of 998 m (1702 2700m) Ascent with the cable car in 6 min Drop of oxygen partial pressure has effect on human body
Measurements 13 people performed a reaction time task paradigm simultaneous EEG and ECG recording 3 time points: in the morning in the hotel (initial state) during the cable car ascent at the mountain each measurement lasted 6 minutes
g.mobilab Mobile Pocket PC biosignal acquisition system 1 ECG derivation (Einthoven I) 2 EEG derivations (right and left hemisphere) Button to log human responses
Content What 4 th Presencia meeting - TUG
Measurement in the Cable Car
Biosignal Visualization
Results 1700 m 2700 m Increase of the heart rate from 85 to 98 beats per minute during the cable car ride ERD/ERS-Analysis: red colors - amplitude attenuation blue colors amplitude enhancement
Results Parameter Valley Mountain Heart rate [bpm] 69,72 82,47 SDNN [ms] 53,12 28,80 HRV-Index [nr] 12,99 8,04 PNN50 [%] 12,86 1,44 LF/HF [1] 1,96 3,09 Reaction time [ms] 333,44 367,28 Alpha ERD [%] 23,72 28,48 Beta ERD [%] 11,29 21,65
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