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ECG Signals Classification for Arrhythmia Detection using Machine Learning Technique

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Garima Chandel1*, Priyanka2, Aastha Mehra3, Setu Garg4, Yogendra Narain5
» doi: : 10.48047/ecb/2023.12.12.196


Arrhythmia is a condition of ectopic (abnormal) heartbeats, which occurs due to irregular pumping of heart. The timely detection of arrhythmia can help the cardiologist in making decision for providing medical aid to the patients. In this work, electrocardiogram (ECG) signal analysis has been done to classify abnormal which are also known as ectopic heart beats and the normal heart rhythms. Mainly ectopic beats are of two types as premature supraventricular (psvc) and premature ventricular contractions, arrhythmia is also a condition of ectopic (abnormal) heartbeats. ECG signals are the graphical plot of heartbeat. It consist of mainly three types of waves. P waves, QRS complex and T wave. Linear Discriminant Analysis (LDA) based classifier has been used to detect abnormality in heartbeat signals automatically, which gave in accuracy, senstivity and specificity of 98.57, 98.7 and 98.2 % respectively. The proposed method has been tested using benchmark dataset, namely MIT-BIH Arrhythmia Data. Finally, the comparison of results of proposed method has been done with existing state of art using same database.

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