Optional Hyperparameter Tuning of Convolutional Neural Network for ECG Classification

Abstract

Although deep learning has resulted in tremendous success for image classification processing, speech processing, and video detection processing applications in recent years, most of the training uses sub-optimal hyperparameters, requiring unnecessarily long training time. The Setting hyperparameters remains a black box which requires considerable experience to acquire. This study proposes several efficient ways to adjust hyperparameters that significantly reduce training time and improve model performance. Hyperparameters are used for the classification of arrhythmias. Classification is used for 16 classes that get an accuracy value 98.88%. Apart from tuning learning rate and batch size, this research also tried several scenarios of optimizer, ratio training set, validation set, and testing set; where the ratio 70 : 10 : 20 makes a significant contribution to the accuracy value.

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