A new approach to detect cardiovascular diseases using ECG scalograms and ML-based CNN algorithm Online publication date: Wed, 01-May-2024
by Lanka Alekhya; P. Rajesh Kumar
International Journal of Computational Vision and Robotics (IJCVR), Vol. 14, No. 3, 2024
Abstract: Convolutional neural networks (CNNs) have gained popularity in the classification of cardiovascular diseases using ECG signals. This paper uses a pre-trained CNN model Visual Geometry Group16 (VGG16) network with the transfer learning process is used for feature extraction with SVM, k-NN and RF algorithms to classify the signals. The input to VGG16 net were ECG signals that are considered from the MIT-BIH database for four classes of heart ailments. Around 27 min and 42 sec of elapsed time is engaged to train the network. The study evaluates that this hybrid model of CNN performs on test data and gives an overall model accuracy and mean of MCC for SVM as 95.83% and 94.52%, for k-NN as 96.67% and 95.60% and for Random Forest as 96.94% and 95.96% respectively which gives a better performance when compared with only pretrained CNN-VGG16Net with an overall accuracy of 95.3% and 93.75% as mean MCC.
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