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

• Autorzy: Vishwanath Bhagyalakshmi , Pujeri Ramchandra Vittal , Devanagavi Geeta

Identyfikatory

DOI

10.1515/bams-2018-0037

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

arrhythmia classification; Gabor transform; linear predictive coding; probabilistic principal component analysis model; support vector neural network

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2019
• Tom: Vol. 15, no. 1
• Strony: art. no. 20180037
• Opis fizyczny: Bibliogr. 52 poz., rys., tab.

Twórcy

autor

Vishwanath Bhagyalakshmi

• REVA University , Bangalore, Karnataka 560064, India

autor

Pujeri Ramchandra Vittal

• MIT College of Engineering, Pune, Maharashtra 411038, India

autor

Devanagavi Geeta

• REVA University , Bangalore, Karnataka 560064, India

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).