Open AccessMachine Learning Classification and Feature Extraction of Arrhythmic ECG Data
Author(s) -
Sumanta Kuila,
Sayandeep Maity,
Suman Kumar,
Subhankar Joardar
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b3548.079220
Subject(s) - computer science , artificial intelligence , feature extraction , pattern recognition (psychology) , support vector machine , wavelet transform , qrs complex , artificial neural network , feature (linguistics) , signal (programming language) , machine learning , wavelet , data mining , cardiology , medicine , linguistics , philosophy , programming language
Electrocardiogram (ECG) is the analysis of the electrical movement of the heart over a period of time. The detailed information about the condition of the heart is measured by analyzing the ECG signal. Wavelet transform, fast Fourier transform are the different methods to disorganize cardiac disease. The paper elaborates the survey on ECG signal analysis and related study on arrhythmic and non arrhythmic data. Here we discuss the efficient feature extraction process for electrocardiogram, where based on position and priority six best P-QRS-T fragments are studied. This survey examines the the outcome of the system by using various Machine learning classification algorithms for feature extraction and analysis of ECG Signals. Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Network (ANN) are the most important algorithms used here for this purpose. There are several publicly available data sets which are used for arrhythmia analysis and among them MIT-BIH ECG-ID database is mostly used. The drawbacks and limitations are also discussed here and from there future challenges and concluding remarks can be done.