Why the study?
Arrhythmias are a major cause of cardiac mortality worldwide, motivating the development of accurate and automated cardiovascular analysis using mature artificial intelligence technology.
An AI-based ECG acquisition and classification system using a 12-layer deep CNN can successfully categorize different types of heartbeats.
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Novel ECG acquisition system may aid automated arrhythmia detection; leaves open prospective clinical validation before practice change.
The electrocardiogram (ECG) is a common test that measures the electrical activity of the heart. On the ECG, several cardiac abnormalities can be seen, including arrhythmias, which are one of the major causes of cardiac mortality worldwide. The objective for the research community is accurate and automated cardiovascular analysis, especially given the maturity of artificial intelligence technology and its contribution to the health area. The goal of this effort is to create an acquisition system and use artificial intelligence to classify ECG readings. This system is designed in two parts: the first is the signal acquisition using the ECG Module AD8232; the obtained signal is a single derivation that has been amplified and filtered. The second section is the classification for heart illness identification; the suggested model is a deep convolutional neural network with 12 layers that was able to categorize five types of heartbeats from the MIT-BIH arrhythmia database. The results were encouraging, and the embedded system was built.
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Benba et al. (2023) studied Arrhythmias (n=100,012). 12-layer 1D Convolutional Neural Network was evaluated on Classification accuracy.
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