Deep Learning Framework with ECG Feature-Based Kernels for Heart Disease Classification
Keywords:Deep learning framework, Dmey wavelet, Heartbeat standardization, Kernel size calculation, MIT-BIH arrhythmia database, Heart disease classification
Heart disease classification with high accuracy can support the physician’s correct decision on patients. This paper proposes a kernel size calculation based on P, Q, R, and S waves of one heartbeat to enhance classification accuracy in a deep learning framework. In addition, Electrocardiogram (ECG) signals were filtered using wavelet transform with dmey wavelet, in which the shape of the dmey is closed to that of one heartbeat. With this selected dmey, each heartbeat was standardized with 300 samples for calculation of kernel sizes so that it contains most features in each heartbeat. Therefore, in this research, with 103,459 heart rhythms from the MIT-BIH Arrhythmia Database, the proposed approach for calculation of kernel sizes is effective with seven convolutional layers and other fully connected layers in a Deep Neural Network (DNN). In particular, with five types of heart disease, the result of the high classification accuracy is about 99.4 %. It means that the proposed kernel size calculation in the convolutional layers can achieve good classification performance and it may be developed for classifying different types of disease.
How to Cite
The copyright for the paper in this journal is retained by the author(s) with the first publication right granted to the journal. The authors agree to the Creative Commons Attribution 4.0 (CC BY 4.0) agreement under which the paper in the Journal is licensed.
By virtue of their appearance in this open access journal, papers are free to use with proper attribution in educational and other non-commercial settings with an acknowledgement of the initial publication in the journal.