Arrhythmia Classification Based on Combination of Heart Rate, Auto Regressive Coefficient and Spectral Entropy Using Probabilistic Neural Network

This paper presents a classification method for arrhythmia using probabilistic neural network, based on unique combination of three Electrocardiogram (ECG) features; heart rate, Auto Regressive (AR) coefficients & spectral entropy (SE). Heart rate has been very critical parameter for the detecti...

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Bibliographic Details
Main Authors: Patidar, Vikas, Srivastava, Rohini, Kumar, Basant, Tewari, R. P., Sahai, Nitin, Bhatia, Dinesh
Format: Conference Proceeding
Language:English
Subjects:
AR coefficients
ECG
Electrocardiography
Entropy
Feature extraction
Heart rate
Neural networks
Probabilistic logic
Probabilistic Neural Network
Spectral Entropy
Training data
Online Access:Request full text
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Summary:This paper presents a classification method for arrhythmia using probabilistic neural network, based on unique combination of three Electrocardiogram (ECG) features; heart rate, Auto Regressive (AR) coefficients & spectral entropy (SE). Heart rate has been very critical parameter for the detection of life-threatening arrhythmia. The purpose of this paper is to develop a Probabilistic Neural Network (PNN) based algorithm for improved detection and broader classification of cardiac arrhythmia. The results show that the unique combination of ECG features considered in this work provides more accurate and robust classification of arrhythmias.
ISSN:2325-9418
DOI:10.1109/INDICON45594.2018.8987120