Iterative expert-in-the-loop classification of sleep PSG recordings using a hierarchical clustering - Gerla, V; Kremen, V; Macas, M; Dudysova, D; Mladek, A; Sos, P; Lhotska, L (Journal of neuroscience methods, 2019)
BACKGROUND: The classification of sleep signals is a subjective and time consuming task. A large number of automatic classifiers have been published in the past decade but a sleep community has no strong confidence to use them in clinical practice and still remains using a standard manual scoring according standardized rules. NEW METHOD: We developed a semi-supervised data-driven approach for objective and efficient evaluation of polysomnographic (PSG) data. The proposed algorithm finds a representative set of signal segments that are subsequently scored by a sleep neurologist. The remaining part of the recording is then automatically classified using these templates. RESULTS: The method was evaluated on 36 PSG recordings (18 chronic insomniacs, 18 healthy controls). We show a faster and objective evaluation of PSG data compared to the manual scoring that is over-performing automated classifiers (accuracy increases ∼14%). The classification results are comparable on both datasets. COMPARISON WITH EXISTING METHOD(S): The methodology that we propose has not yet been published in the area of sleep PSG data processing. The performance of our method is comparable to various published automated approaches (a typical published classification accuracy is ∼75-95%). The method allows the evaluation of PSG recordings in more general terms and across different recording devices and standards. CONCLUSIONS: The proposed solution is not based on a single-purpose rules or heuristics and training model is not trained on other patient's sleep recordings. The method is applicable to wide range of similar tasks and various types of physiological signals.
Keywords: EEG; poruchy iniciace a udržování spánku; Signal Processing, Computer-Assisted; spánek; mozek; Sleep Initiation and Maintenance Disorders; mozkové vlny; Humans; Algorithms; lidé středního věku; polysomnografie; Female; dospělí; Machine Learning; počítačové zpracování signálu; Cluster Analysis; Polysomnography; Hierarchical clustering; lidé; elektroencefalografie; Sleep; Adult; Brain Waves; ženské pohlaví; strojové učení; Male; Middle Aged; Semi-supervised; Brain; Classification; shluková analýza; algoritmy; Electroencephalography; mužské pohlaví