Convolutional Neural Network Hyperparameter Tuning with Adam Optimizer for ECG Classification

In this research, Adaptive Moment Estimation (Adam) optimization technique has been examined on ECG arrhythmia data that rely on deep neural networks. The proposed method indicates that Adam has great importance to solve deep learning problems. According to the proposed method, the heartbeats are classified as normal (N), left bundle branch block (LBBB) and right bundle branch block (RBBB) considering the hyper-parameter tuning of the convolutional neural network (CNN). The heartbeats are transformed into spectrogram images and directly given into CNN without any feature extraction method but bounded with a specific frequency/time-resolution rate. The most important point of the study is the examination of the moment estimation coefficients of Adam optimizer such as first moment and second moments. Other tuned parameters are adaptive learning rate and epsilon value. The hyperparameters, such as the learning rate and the moment estimation are investigated by grid search method. The effect of the parameters to validation loss were presented and analyzed as a result of this study.