Abstract
Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model’s classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models’ effectiveness.
Highlights
Cardiovascular issues are currently the primary cause of human morbidity, causing more than 17 million deaths each year
The Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) database consists of 48 thirty minutes portions or pieces of 2-channel ambulatory ECG records, received from 47 patients analyzed by the BIH Arrhythmia Laboratory from 1975 to 1979
Our suggested 1D convolutional neural network (CNN) model applied to the ECG signal, which learns useful attributes from the given data
Summary
Cardiovascular issues are currently the primary cause of human morbidity, causing more than 17 million deaths each year. The World Heart Federation report witness about three fourth of the total cardiovascular disease (CVD) patients reside inside low-income regions across the globe [1]. Electrocardiogram (ECG) records the electrical activity generated by heart muscle depolarizations, which propagate in pulsating electrical waves towards the skin. As a result of its easiness, several ECG categorizations processes have been established, counting manuals methods [9,10] and machine learning approaches [11,12,13,14,15,16]. It is used for transient signals like ECG, often necessary for machine learning procedures with excessive computer assets. Machine learning methods are preferred compared to manual processes, though, a useful algorithm needed to diminish it
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