Discrimination of the Healthy and Sick Cardiac Autonomic Nervous System by a New Wavelet Analysis of Heartbeat Intervals

Ashkenazy, Yosef; Lewkowicz, M.; Levitan, Jacob; Moêlgaard, H.; Thomsen, Poul Erik Bloch; Saermark, K.

doi:10.1142/s0218348x98000249

Cited by 34 publications

(35 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The WAV Method. In the WAV method [13,14,19] one finds the wavelet coefficients W m,j , where m is a 'scale parameter' and j is a 'position' parameter (the scale m is related to the number of data points in the window by n = 2 m [18]), by means of a wavelet transform. The standard deviation σ wav (m) of the wavelet coefficients W m,j across the parameter j is used as a parameter to separate healthy from sick subjects.…”

Section: Typeset Using Euro-t E Xmentioning

confidence: 99%

Scale-specific and scale-independent measures of heart rate variability as risk indicators

et al. 2001

Self Cite

View full text Add to dashboard Cite

Abstract. -We study the Heart Rate Variability (HRV) using scale specific variance and scaling exponents as measures of healthy and cardiac impaired individuals. Our results show that the variance and the scaling exponent are uncorrelated. We find that the variance measure at certain scales is well suited to separate healthy subjects from heart patients. However, for cumulative survival probability the scaling exponents outperform the variance measure. Our risk study is based on a database containing recordings from 428 individuals after myocardial infarct (MI) and on database containing 105 healthy subjects and 11 heart patients. The results have been obtained by applying three recently developed methods (DFA -Detrended Fluctuation Analysis, WAV -Multiresolution Wavelet Analysis, and DTS -Detrended Time Series analysis) which are shown to be highly correlated.The study of heart rate variability (HRV) has been in use for the last two decades as part of clinical and prognostic work; international guidelines for evaluating conventional HRVparameters do exist [1]. The conventional parameters are power spectra [2] and standard deviation [3,4]. Recently three new methods of analyzing heart interbeat interval (RR) time series have been developed, all of them showing signs of improved diagnostic performance. The three methods are: Detrended Fluctuation Analysis (DFA) [5,6,7,8,9], Multiresolution

show abstract

Section: Typeset Using Euro-t E Xmentioning

confidence: 99%

Scale-specific and scale-independent measures of heart rate variability as risk indicators

et al. 2001

Self Cite

View full text Add to dashboard Cite

show abstract

“…To evaluate the discriminating capabilities of σ d we examined RR-data for a group of 33 subjects (the same data group as in ref. [10]) consisting of 21 healthy subjects, 9 diabetics and 3 heart patients including one heart transplanted patient. Thus we calculate σ d for a time-series consisting of 2 16 = 65536 data points, corresponding to approx- * An RR interval is the time difference between two consecutive pronounced peaks -the R peaks -of the ECG recording.…”

Section: The Detrended Time Seriesmentioning

confidence: 99%

Discrimination Between Healthy and Sick Cardiac Autonomic Nervous System by Detrended Heart Rate Variability Analysis

et al. 1999

Self Cite

View full text Add to dashboard Cite

Multiresolution Wavelet Transform and Detrended Fluctuation Analysis have been recently proven as excellent methods in the analysis of Heart Rate Variability, and in distinguishing between healthy subjects and patients with various dysfunctions of the cardiac nervous system. We argue that it is possible to obtain a distinction between healthy subjects/patients of at least similar quality by, first, detrending the time-series of RR-intervals by subtracting a running average based on a local window with a length of around 32 data points, and then, calculating the standard deviation of the detrended time-series. The results presented here indicate that the analysis can be based on very short time-series of RR-data (7-8 minutes), which is a considerable improvement relative to 24-hours Holter recordings.

show abstract

“…A HR regulation state was understood as the complex of HR parameters in a preset period of time (5 min) since it is known that the statistical and spectral characteristics of a cardiointerval time series make it possible to monitor quantitative indices of the tone of the sympathetic and parasympathetic nervous systems, which form the current state of HR regulation [1][2][3][4][5][6][7][8][9][10][11]. The question is whether the sequence of functional states (in terms of the complex of the autonomic regulation characteristics during the 5-min interval) is a nonrepeating continuum of events or there are interchanging "discrete" classes of states.…”

mentioning

confidence: 99%

Dynamics of the states of heart rate regulation during 24-h monitoring

et al. 2005

View full text Add to dashboard Cite

The aim of this work was to attempt to classify the changes in states of the heart rate (HR) regulation during 24-h monitoring in patients with arterial hypertension and coronary heart disease. A HR regulation state was understood as the complex of HR parameters in a preset period of time (5 min) since it is known that the statistical and spectral characteristics of a cardiointerval time series make it possible to monitor quantitative indices of the tone of the sympathetic and parasympathetic nervous systems, which form the current state of HR regulation [1][2][3][4][5][6][7][8][9][10][11]. The question is whether the sequence of functional states (in terms of the complex of the autonomic regulation characteristics during the 5-min interval) is a nonrepeating continuum of events or there are interchanging "discrete" classes of states. Elucidation of this question might be of importance for constructing models for short-term prediction of states. METHODSWe tested 30 patients with stage 1 or 2 (according to the Society of Cardiology of the Russian Federation, 2001) hypertension verified by conventional examination. Of them, three patients had coronary heart disease in the form of class II stable angina of effort. The group consisted of 20 men and 10 women. The mean age was 46.4 ± 10.1 years. The mean systolic pressure was 132.3 ± 11.2 mmHg; the mean diastolic pressure, 82.4 ± 9.3 mmHg; and the mean HR, 66.9 ± 9.98 bpm. The patients were treated with hypotensive drugs of the main types. In all patients, cardiointervals were continuously monitored for 24 h using a Cardio Tens-01 instrument (Meditech, Hungary), which allows continuous recording of RR intervals over 24 h and temporal and spectral analyses of the HR for a predetermined time interval. A continuous chain of consecutive nonoverlapping 5-min RR interval segments was analyzed for a 24-h period. Patients with a minimal number of artifacts (no more than five to ten episodes during the observation) and without rhythm disorders were selected. Segments with artifacts were excluded from the analysis. The following parameters were determined for each 5-min interval: the RR interval mode (MoRR, ms), the total spectral power (TP, ms 2 ), the low-frequency component (LF, ms 2 ), the high-frequency component (HF, ms 2 ), and the LF/HF ratio; the technique and the ranges were as recommended in [1]. The number of 5-min intervals analyzed for a patient varied from 239 to 286. Since the above HR parameters reflect the influence of several control systems [1-11], the term functional state of HR regulation in a given segment of time may be used. For example, Fig. 1 shows the chronological sequence of the synchronous HR parameters in a 65-min segment (13 5-min intervals) in patient Kh. The letters a , b , c , and d at the top of vertical lines 7, 8, 9, and 10 designate different functional states of HR regulation. To classify the states changing over 24 h, we used pattern recognition (clustering) methods, including a combination of the hypersphere and potential function metho...

show abstract

Discrimination of the Healthy and Sick Cardiac Autonomic Nervous System by a New Wavelet Analysis of Heartbeat Intervals

Cited by 34 publications

References 12 publications

Scale-specific and scale-independent measures of heart rate variability as risk indicators

Scale-specific and scale-independent measures of heart rate variability as risk indicators

Discrimination Between Healthy and Sick Cardiac Autonomic Nervous System by Detrended Heart Rate Variability Analysis

Dynamics of the states of heart rate regulation during 24-h monitoring

Contact Info

Product

Resources

About