ECG arrhythmia analysis based on inverse power-law spectrum

In this study, we analyzed spectrum characteristics of 10 presumed-normal subjects and 44 records from the MIT-BIH ECG database. We found that the inverse power-law spectrum is a quantitative approach for characterizing erratic fluctuations of heart rate and can be used to distinguish between health...

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Bibliographic Details
Main Authors: Luo, Shen, Urrusti, Jose L, Tompkins, Willis J
Format: Conference Proceeding
Language:English
Online Access:Get full text
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Summary:In this study, we analyzed spectrum characteristics of 10 presumed-normal subjects and 44 records from the MIT-BIH ECG database. We found that the inverse power-law spectrum is a quantitative approach for characterizing erratic fluctuations of heart rate and can be used to distinguish between healthy and abnormal subjects. We conclude three important features for normal heartbeats: (1) the average of the regression line slopes is about -1,15; (2) cross correlations between the spectral data and the regression lines are relatively higher than for most abnormals; (3) all parameters (slops, y-intercepts, and crosscorrelations) among series of different temporal lengths are robust. Subjects with arrhythmias typically do not match all three features. Medications may help patients to return to sinus rhythm. We analyzed heart rate frequency spectrum distributions for three different temporal lengths of each ECG to investigate the hypothesis of temporal self-similarly. Our results do not support this hypothesis.