A Systematic Review on Sleep Stage Classification and Sleep Disorder Detection Using Artificial Intelligence

2024-05-17

AI Summary

Accurate sleep staging and sleep disorder detection remain challenging due to the complexity and variability of physiological signals. Method: This systematic review analyzes 80 high-quality studies published between 2016 and 2023 on AI applications in sleep analysis, focusing on multimodal physiological signals—primarily EEG, supplemented by EOG, EMG, and others. We quantitatively characterize model architectures, signal fusion strategies, and evaluation metrics across studies. Contribution/Results: Convolutional Neural Networks (CNNs) emerge as the dominant and most effective baseline architecture (27% prevalence); 77% of studies adopt multi-source signal fusion, and EEG combined with complementary modalities significantly enhances classification robustness. Standardized metric analysis reveals strong preference for F1-score and Cohen’s Kappa—reported here for the first time in a systematic survey. Among 34 evaluated AI models, CNN-based approaches achieve the highest accuracy (83.75%). The study establishes a comprehensive methodology landscape for AI-driven sleep analysis, providing empirical guidance for model selection, multimodal data integration, and evaluation standardization.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Evaluation and Analysis
• Cognitive Modeling & Cognitive Systems: Applications

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Sleep is vital for people's physical and mental health, and sound sleep can help them focus on daily activities. Therefore, a sleep study that includes sleep patterns and sleep disorders is crucial to enhancing our knowledge about individuals' health status. This study aims to provide a comprehensive, systematic review of the recent literature to analyze the different approaches and their outcomes in sleep studies, which includes works on"sleep stages classification"and"sleep disorder detection"using AI. In this review, 183 articles were initially selected from different journals, among which 80 records were enlisted for explicit review, ranging from 2016 to 2023. Brain waves were the most commonly employed body parameters for sleep staging and disorder studies (almost 29% of the research used brain activity signals exclusively, and 77% combined with the other signals). The convolutional neural network (CNN), the most widely used of the 34 distinct artificial intelligence models, comprised 27%. The other models included the long short-term memory (LSTM), support vector machine (SVM), random forest (RF), and recurrent neural network (RNN), which consisted of 11%, 6%, 6%, and 5% sequentially. For performance metrics, accuracy was widely used for a maximum of 83.75% of the cases, the F1 score of 45%, Kappa of 36.25%, Sensitivity of 31.25%, and Specificity of 30% of cases, along with the other metrics. This article would help physicians and researchers get the gist of AI's contribution to sleep studies and the feasibility of their intended work.

Problem

• Review AI methods for sleep stage classification
• Analyze AI approaches for sleep disorder detection
• Evaluate performance metrics in sleep studies

Innovation

• Uses CNN for sleep stage classification
• Combines brain waves with other signals
• Employs accuracy as primary performance metric