Cardiac state diagnosis using higher order spectra of heart rate variability

Abstract

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart, by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are nonlinear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of nonlinear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and seven classes of arrhythmia. We present some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. We also extracted features from the HOS and performed an analysis of variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test.

Noções básicas de variabilidade da frequência cardíaca e sua aplicabilidade clínica

“…This analysis decomposes the HRV in fundamental oscillatory components, whereas the main ones are [4,7,23,29,[37][38][39][40][41][42]: a) High-frequency component (High Frequency -HF), ranging from 0.15 to 0.4 Hz, which corresponds to the respiratory modulation and is an indicator of the performance of the vagus nerve on the heart; b) Low frequency component (Low Frequency -LF), ranging between 0.04 and 0.15 Hz, which is due to the joint action of the vagal and sympathetic components on the heart, with a predominance of the sympathetic ones; c) Components of very low frequency (Very Low Frequency -VLF) and ultra-low frequency (Ultra Low Among the nonlinear methods used for HRV analysis, we can mention: detrended fluctuation analysis, correlation function, Hurst exponent, fractal dimension and Lyapunov exponent [4,23,29].…”

Assessment of Heart Rate Variability during an Endurance Mountain Trail Race by Multi-Scale Entropy Analysis

“…Primarily because of the non-linear interplay of different physiological control loops in generating the heart rate, linear methods are not adequate to fully describe such a complex system. Several non-linear HRV methods such as fractal scaling analysis, higher order spectra analysis, multi-scale entropy analysis, power law analysis, complexity analysis, symbolic dynamics analysis and heart rate turbulence analysis have been studied for various diseases [7][8][9][10][11][12][13][14][15]. It must be taken into account that while the collection of heart rate (HR) was initially only possible with expensive laboratory-based electrocardiograph recorders, the recent availability of specifically designed portable recorders has substantially boosted the use of HRV monitoring [16,17].…”

Computer-Aided Diagnosis of Depression Using EEG Signals

“…Different nonlinear methods can be used to extract features from the EEG signal to differentiate a depressive EEG from a healthy one such as Fractal Dimension (FD) [48,49], Recurrence Quantification Analysis (RQA) [50,51], Higher-Order Spectra (HOS) [52,53,54], sample entropy [55,56], approximate entropy [57], Largest Lyapunov Exponent (LLE) [58], Hurst's exponent (H) [59] and Detrended Fluctuation Analysis (DFA) [60]. …”