Publications by authors named "Ankit Bhurane"

2 Publications

  • Page 1 of 1

Automated classification of cyclic alternating pattern sleep phases in healthy and sleep-disordered subjects using convolutional neural network.

Comput Biol Med 2022 07 10;146:105594. Epub 2022 May 10.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, 599491, Singapore. Electronic address:

Sleep contributes to more than a third of a person's life, making sleep monitoring essential for overall well-being. Cyclic alternating patterns (CAP) are crucial in monitoring sleep quality and associated illnesses such as insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, etc. However, traditionally medical specialists practice manual division techniques of CAP phases which are sensitive to human weariness and inaccuracies. This might result in a false sleep stage diagnosis. This study proposes an automated approach using a deep learning model based on a 1-dimensional convolutional neural network for classifying CAP phases (A and B). The proposed model uses single-channel standardized electroencephalogram (EEG) recordings provided by the CAP sleep database. The model was created with the help of healthy participants and patients suffering from five distinct sleep disorders, which includes narcolepsy, rapid eye movement behaviour disorder (RBD), periodic leg movement disorder (PLM), NFLE, and insomnia. The developed model has achieved the highest automated classification accuracy of 78.84% for the healthy dataset and 82.21%, 79.48%, 78.73%, 76.68%, and 70.88% for narcolepsy, RBD, PLM, NFLE, and insomnia subjects, respectively in categorizing phases A and B. The proposed approach can help medical professionals monitor sleep and examine a person's brain stability.
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http://dx.doi.org/10.1016/j.compbiomed.2022.105594DOI Listing
July 2022

Automated phase classification in cyclic alternating patterns in sleep stages using Wigner-Ville Distribution based features.

Comput Biol Med 2020 04 4;119:103691. Epub 2020 Mar 4.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, Japan. Electronic address:

Sleep is one of the most important body mechanisms responsible for the proper functioning of human body. Cyclic alternating patterns (CAP) play an indispensable role in the analysis of sleep quality and related disorders like nocturnal front lobe epilepsy, insomnia, narcolepsy etc. The traditional manual segregation methods of CAP phases by the medical experts are prone to human fatigue and errors which may lead to inaccurate diagnosis of sleep stages. 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.
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http://dx.doi.org/10.1016/j.compbiomed.2020.103691DOI Listing
April 2020
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