Towards automatic EEG cyclic alternating pattern analysis: a systematic review

Abstract

This study conducted a systematic review to determine the feasibility of automatic Cyclic Alternating Pattern (CAP) analysis. Specifically, this review followed the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to address the formulated research question: is automatic CAP analysis viable for clinical application? From the identified 1,280 articles, the review included 35 studies that proposed various methods for examining CAP, including the classification of A phase, their subtypes, or the CAP cycles. Three main trends were observed over time regarding A phase classification, starting with mathematical models or features classified with a tuned threshold, followed by using conventional machine learning models and, recently, deep learning models. Regarding the CAP cycle detection, it was observed that most studies employed a finite state machine to implement the CAP scoring rules, which depended on an initial A phase classifier, stressing the importance of developing suitable A phase detection models. The assessment of A-phase subtypes has proven challenging due to various approaches used in the state-of-the-art for their detection, ranging from multiclass models to creating a model for each subtype. The review provided a positive answer to the main research question, concluding that automatic CAP analysis can be reliably performed. The main recommended research agenda involves validating the proposed methodologies on larger datasets, including more subjects with sleep-related disorders, and providing the source code for independent confirmation.

Sleep Analysis by Evaluating the Cyclic Alternating Pattern A Phases

“…Different authors have proposed several solutions concerning automatic sleep analysis through CAP. Mendonça et al [17] identified three primary methods in state-of-the-art.…”

“…Such is essential for clinical analysis, unlike the third state-of-the-art approach, which uses deep learning models [29]. The works that follow deep learning-based methodologies were reviewed by Mendonça et al [17] and are out of scope for this work.…”

“…Despite CAP having immense potential for clinical application, it is still unfeasible until proper automation arises. A recent systematic review [17] between 1998 and January 2023 found that the thresholds used were either mathematical models or features to identify A phases. These were recently followed by conventional machine learning, which has been dominated by deep learning models.…”

Multi‐centre arousal scoring agreement in the Sleep Revolution

“…Lastly, the ArIs between scorers can vary not only due to uncertainties in the arousal scoring itself, but also in the sleep stage scoring (Nikkonen et al, 2024), as the latter affects the TST required in ArI computation. Approaches without relying on manually scored discrete sleep stages, for example, odds ratio product (Younes et al, 2015) and Cyclic Alternating Pattern (CAP) (Mendonça et al, 2023;Parrino et al, 2012), can provide novel insight into the assessment of sleep depth and level of arousal.…”