Visual Explanations of Deep Learning Architectures in Predicting Cyclic Alternating Patterns Using Wavelet Transforms

Abstract

Cyclic Alternating Pattern (CAP) is a sleep instability marker defined based on the amplitude and frequency of the electroencephalogram signal. Because of the time and intensive process of labeling the data, different machine learning and automatic approaches are proposed. However, due to the low accuracy of the traditional approach and the black box approach of the machine learning approach, the proposed systems remain untrusted by the physician. This study contributes to accurately estimating CAP in a Frequency-Time domain by A-phase and its subtypes prediction by transforming the monopolar deviated electroencephalogram signals into corresponding scalograms. Subsequently, various computer vision classifiers were tested for the A-phase using scalogram images. It was found that MobileNetV2 outperformed all other tested classifiers by achieving the average accuracy, sensitivity, and specificity values of 0.80, 0.75, and 0.81, respectively. The MobileNetV2 trained model was further fine-tuned for A-phase subtypes prediction. To further verify the visual ability of the trained models, Gradcam++ was employed to identify the targeted regions by the trained network. It was verified that the areas identified by the model match the regions focused on by the sleep experts for A-phase predictions, thereby proving its clinical viability and robustness. This motivates the development of novel deep learning based methods for CAP patterns predictions.