Probabilistic principal component analysis-based dimensionality reduction and optimization for arrhythmia classification using ECG signals

Abstract

Electrocardiogram (ECG) is an electrical signal that contains data about the state and functions of the heart and can be used to diagnose various types of arrhythmias effectively. The modeling and simulation of ECG under different conditions are significant to understand the function of the cardiovascular system and in the diagnosis of heart diseases. Arrhythmia is a severe peril to the patient recovering from acute myocardial infarction. The reliable detection of arrhythmia is a challenge for a cardiovascular diagnostic system. As a result, a considerable amount of research has focused on the development of algorithms for the accurate diagnosis of arrhythmias. In this paper, a system for the classification of arrhythmia is developed by employing the probabilistic principal component analysis (PPCA) model. Initially, the cluster head is selected for the effective transmission of ECG signals of patients using the adaptive fractional artificial bee colony algorithm, and multipath routing for transmission is selected using the fractional bee BAT algorithm. Features such as wavelet features, Gabor transform, empirical mode decomposition, and linear predictive coding features are extracted from the ECG signal with high dimension (which are reduced using PPCA) and finally given to the proposed classifier called adaptive genetic-bat (AGB) support vector neural network (which is trained using the AGB algorithm) for arrhythmia detection. The experimentation of the proposed system is done based on evaluation metrics, such as the number of alive nodes, normalized network energy, goodput, and accuracy. The proposed method obtained a classification accuracy of 0.9865 and a goodput of 0.0590 and provides a better classification of arrhythmia. The experimental results show that the proposed system is useful for the classification of arrhythmias, with a reasonably high accuracy of 0.9865 and a goodput of 0.0590. The validation of the proposed system offers acceptable results for clinical implementation.

[Retracted] Music Rhythm Detection Algorithm Based on Multipath Search and Cluster Analysis

“…e rhythm model and the beat model are interdependent, and they together reflect the regularity of time organization. In Western music, this rule is often multilevel, so the rhythm model should also be multilevel [2]. In rhythm recognition, the method often used is to compare the recognized music with a set of typical rhythm models.…”

Synthesis of an integral signal for solving the problem of morphological analysis of electrocardiograms

“…It is easy to see from the above scheme that not a single step of ECG analysis is completed by without morphological analysis of the signal. As noted above, in the context of the introduction of telemedicine into the health care system of Ukraine, the creation of cardiological DSSs based on automatic morphological ECG analysis is of particular importance, for which various methods are used:  ECG analysis in the time domain using various classification methods, including probabilistic classification [14,15], cluster analysis and pattern recognition [16,17], neural networks [18,19], fuzzy clustering [20,21] and others;  ECG analysis in the time-frequency domain, for example, local (window) Fourier transform (spectral-time mapping) and wavelet transform [22,23], as well as in the phase plane [10,24];  morphological filtration of ECG using the multichannel matched morphological filter proposed by the authors [25].…”