DOIONLINE 
DOIONLINE Publish In |
International Journal of Industrial Electronics and Electrical Engineering (IJIEEE)-IJIEEE |
 Journal Home Volume Issue |
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Issue |
Volume-8,Issue-1 ( Jan, 2020 ) | |||||||||
Paper Title |
Classification of Ecg Beats for Types of Arrhythmia Using Cascaded Lstm and Cnn | |||||||||
Author Name |
Tufail Farooq, Daljit Singh | |||||||||
Affilition |
Dept. of Electronics and Communication, GNDEC, Ludhiana Assistant Professor, Dept. of Electronics and Communication, GNDEC, Ludhiana | |||||||||
Pages |
21-25 | |||||||||
Abstract |
The work presented in this paper aims to classify ECG beats into five super classes of Arrhythmia. The learning model is trained using a cascaded model of long short term memory network and Convolution neural network. The data for classification is acquired from the Arrhythmia database provided by the Massachusetts Institute’s Beth-Israel hospital. The data is labeled using the help of the annotations of the Cardiologists. The features chosen are the local maximas of a segmented beats. The signal records of 10 sec duration are then segmented into beats of 100 samples each. The beats are compiled as a training dataset. A training and validation split of 80-20% is made upon which the performance metrics of the classifier are evaluated. The maximum classification accuracy obtained is 98.2% From the confusion matrix it can be deduced that average precision and average recall per class is obtained to be 96.94% and 97.12% respectively. Keywords - Machine Learning, Lstm, Convolution, Back Propagation, Activation Function, Svm, Accuracy | |||||||||
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