An automatic sleep-scoring system in elderly women with osteoporosis fractures using frequency localized finite orthogonal quadrature Fejer Korovkin kernels. (February 2023)

• Record Type: Journal Article
• Authors: Dakhale, Bharti Jogi; Sharma, Manish; Arif, Mohammad; Asthana, Kushagra; Bhurane, Ankit A.; Kothari, Ashwin G.; Rajendra Acharya, U.
• Is Part Of: Medical engineering & physics. Volume 112(2023)
• Publication Date: 2023-02
• Subjects: Sleep stages -- Scoring -- EEG -- PSG (polysomnogram) -- Wavelet filters -- Machine learning -- Sleep disorders; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2023.103956
• Languages: English
• ISSNs: 1350-4533

Abstract

Abstract: Healthy sleep signifies a good physical and mental state of the body. However, factors such as inappropriate work schedules, medical complications, and others can make it difficult to get enough sleep, leading to various sleep disorders. The identification of these disorders requires sleep stage classification. Visual evaluation of sleep stages is time intensive, placing a significant strain on sleep experts and prone to human errors. As a result, it is crucial to develop machine learning algorithms to score sleep stages to acquire an accurate diagnosis. Hence, a new methodology for automated sleep stage classification is suggested using machine learning and filtering electroencephalogram (EEG) signals. The national sleep research resource's (NSRR) study of osteoporotic fractures (SOF) dataset comprising 453 subjects' polysomnograph (PSG) data is used in this study. Only two unipolar EEG derivations C4-A1 and C3-A2 are employed individually and jointly in this work. The EEG signals are decomposed into sub-bands using a frequency-localized finite orthogonal quadrature Fejer Korovkin wavelet filter bank. The wavelet-based entropy features are extracted from sub-bands. Subsequently, extracted features are classified using machine learning techniques. Our developed model obtained the highest classification accuracy of 81.3%, using an ensembled bagged trees classifier with a 10-fold cross-validation method and Cohen's Kappa coefficient of 0.72. The proposed model is accurate, dependable, and easy to implement and can be employed as an alternative to a PSG-based system at home with minimal resources. It is also ready to be tested on other EEG data to evaluate the sleep stages of healthy and unhealthy subjects. Highlights: Novel method for scoring sleep stages in women with osteoporotic fractures. New frequency localized orthogonal quadrature Fejer Korovkin Kernel-based features are used. Only EEG signals are used. A large osteoporotic fractures (SOF) containing PSG of elderly women is employed. The algorithm produces a high classification accuracy.