MI-OPTNET: AN OPTIMIZED DEEP LEARNING FRAMEWORK FOR MYOCARDIAL INFARCTION DETECTION

The conventional means of myocardial infarction (MI) detection using a 12-lead electrocardiogram (ECG) system include a pretrained network and machine learning interpretation of the complex ECG signals. They are computationally inefficient and demand high-performance hardware. Here, for the first time, we introduce an effective framework (MI-OptNet) using the particle swarm optimization model (PSO) in the design of a lightweight hybrid network combining convolutional neural network (CNN)-long short terms memory (LSTM) for MI and normal ECG detection. We optimized important design and training parameters based on limb leads’ signals and identified leads III and VI as the best ECG leads for the task based on their high classification performance ranging between 80 – 90 %, suggesting that they may provide more information about MI than the others. The other strategy of fusing the scores from all models at the decision level achieved the best result with a 10 % increase in the evaluated metrics. Our findings support the flexibility and adaptability of our framework for the design process using minimal computer efforts. We concluded that this approach may be used for other classification problems to assist engineers and designers in efficient decision-making and to solve complex signal classification and recognition problems.