Understanding of Normal and Abnormal Hearts by Phase Space Analysis and Convolutional Neural Networks

TL;DR

This study combines phase space analysis and convolutional neural networks to classify healthy and unhealthy hearts from ECG data, achieving over 90% accuracy in distinguishing cardiac health status.

Contribution

It introduces a novel approach integrating phase space visualization with CNNs for improved cardiac diagnostics from ECG signals.

Findings

Achieved 90.90% classification accuracy.

Effective use of phase space images for heart health assessment.

Demonstrated potential for automated cardiac disease detection.

Abstract

Cardiac diseases are one of the leading mortality factors in modern, industrialized societies, which cause high expenses in public health systems. Due to high costs, developing analytical methods to improve cardiac diagnostics is essential. The heart's electric activity was first modeled using a set of nonlinear differential equations. Following this, variations of cardiac spectra originating from deterministic dynamics are investigated. Analyzing a normal human heart's power spectra offers His-Purkinje network, which possesses a fractal-like structure. Phase space trajectories are extracted from the time series electrocardiogram (ECG) graph with third-order derivate Taylor Series. Here in this study, phase space analysis and Convolutional Neural Networks (CNNs) method are applied to 44 records via the MIT-BIH database recorded with MLII. In order to increase accuracy, a straight line is drawn between the highest Q-R distance in the phase space images of the records. Binary CNN classification is used to determine healthy or unhealthy hearts. With a 90.90% accuracy rate, this model could classify records according to their heart status.