In this paper, we present an automated approach for the classification of CAP phases (A and B) using Wigner–Ville Distribution (WVD) and Rényi entropy (RE) features. The WVD provides a high-resolution time–frequency analysis of the signals whereas RE provides least time–frequency uncertainty with WVD. The classification is performed using medium Gaussian kernel-based support vector machine with 10-fold cross-validation technique. We have presented the results for randomly sampled balanced data sets. The proposed approach does not require any pre-processing or post-processing stages, making it simple as compared to the existing techniques. The proposed method is able to achieve an average classification accuracy of 72.35% and 87.45% for balanced and unbalanced data sets respectively. The proposed method can aid the medical experts to analyze the cerebral stability as well as the sleep quality of a person.
Recommended citation: Shivani Dhok, Varad Pimpalkhute, Ambarish Chandurkar, Ankit A. Bhurane, Manish Sharma, & U. Rajendra Acharya, "e;Automated phase classification in cyclic alternating patterns in sleep stages using Wigner–Ville Distribution based features,"e; in Computers in Biology and Medicine, vol. 119, 103691, 2020, doi: 10.1016/j.compbiomed.2020.103691.