An expert system for automated classification of phases in cyclic alternating patterns of sleep using optimal wavelet-based entropy features

Humans spend a significant portion of their time in the state of sleep, and therefore one's'sleep health' is an important indicator of the overall health of an individual. Non-invasive methods such as electroencephalography (EEG) are used to evaluate the 'sleep health' as well as associated disorders such as nocturnal front lobe epilepsy, insomnia, and narcolepsy. A long-duration and repetitive activity, known as a cyclic alternating pattern (CAP), is observed in the EEG waveforms which reflect the cortical electrical activity during non-rapid eye movement (NREM) sleep. The CAP sequences involve various, continuing periods of phasic activation (phase-A) and deactivation (phase-B). The manual analysis of these signals performed by clinicians are prone to errors, and may lead to the wrong diagnosis. Hence, automated systems that can classify the two phases (viz. Phase A and Phase B accurately can eliminate any human involvement in the diagnosis. The pivotal aim of this study is to evaluate the usefulness of stopband energy minimized biorthogonal wavelet filter bank (BOWFB) based entropy features in the identification of CAP phases. We have employed entropy features obtained from six wavelet subbands of EEG signals to develop a machine learning (ML) based model using various supervised ML algorithms. The proposed model by us yielded an average classification accuracy of 74.40% with 10% hold-out validation with the balanced dataset, and maximum accuracy of 87.83% with the unbalanced dataset using ensemble bagged tree classifier. The developed expert system can assist the medical practitioners to assess the person's cerebral activity and quality of sleep accurately.

Automated summary

Humans' sleep health is an important indicator of overall health, and non-invasive methods like EEG are used to evaluate it. A study developed a model based on machine learning algorithms to accurately identify CAP phases using entropy features extracted by a biorthogonal wavelet filter bank. The model achieved high classification accuracy and can assist medical practitioners in assessing cerebral activity and sleep quality accurately.