Statistical and Learning Aided Classifier for ECG Based Predictive Diagnostic Tool

Sumi Bharali,Nikos Mastorakis,Kandarpa Kumar Sarma,Manash Pratim Sarma,N Mastorakis,A Bulucea,V Mladenov

doi:10.1051/matecconf/201712505008

Sumi Bharali, Nikos Mastorakis + Show 5 more

Open Access

https://doi.org/10.1051/matecconf/201712505008

Copy DOI

Abstract

Early diagnosis and classification of long term cardiac signals are crucial issues in the treatment of heart related disorders. The available number of medical professional are not sufficient to deal with the increase patients for which design of certain machine based diagnostics tools have been accepted as a viable option. Typical Electrocardiogram (ECG) machine is helpful for monitoring the heart abnormalities only for short interval of time. Therefore, it becomes necessary to design a system which captures relevant features of the ECG signal for use with certain classifiers. In our proposed system, ECG signal elements like Q, R and S peaks are detected and heart rate estimated using Linear Discriminant Analysis (LDA), Adaptive Linear Discriminant Analysis (ALDA) and Support Vector Machine (SVM). For our work we have been used MIT BIH (Standard Arrhythmia Database).

Highlights

Electrocardiograph (ECG) is an instrument used in the detection and diagnosis of heart abnormalities that occur in human's body
The available number of medical professional are not sufficient to deal with the increasing number of patients for which design of certain machine based diagnostics tools have been accepted as a viable option
Pam-Tompkins algorithm is used for features extraction of ECG Signal. With this algorithm Q, R and S peak is searched for every cycle of ECG signal and the heart rate of patients

Summary

Introduction

Electrocardiograph (ECG) is an instrument used in the detection and diagnosis of heart abnormalities that occur in human's body. ECG is used for the diagnosis of heart related disorders. Such a machine measures the electrical potentials of a patient by placing electrodes on body surface and in this way system records continuous activity of heart muscle. It becomes necessary to design a system which captures relevant features of the ECG signal for use with certain classifiers. ECG signal elements like Q, R and S peaks are detected and heart rate estimated using Linear Discriminant Analysis (LDA), Adaptive Linear Discriminant Analysis (ALDA) and Support Vector Machine (SVM). Some of the relevant literature are [1]-[10]

Proposed Method

System Block Diagram

Classifier design

Experimental Details and Results

Conclusion