Summary of A Systematic Review on Sleep Stage Classification and Sleep Disorder Detection Using Artificial Intelligence, by Tayab Uddin Wara et al.
A Systematic Review on Sleep Stage Classification and Sleep Disorder Detection Using Artificial Intelligence
by Tayab Uddin Wara, Ababil Hossain Fahad, Adri Shankar Das, Md. Mehedi Hasan Shawon
First submitted to arxiv on: 17 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive review of recent literature on AI-based approaches for sleep stage classification and disorder detection. It analyzes 80 research articles published between 2016 and 2023, focusing on brain wave signals (29% used exclusively) and combined signals (77%). The convolutional neural network (CNN) is the most widely used model, comprising 27% of the total models employed. Other AI models include LSTM, SVM, RF, and RNN, which accounted for 11%, 6%, 6%, and 5% respectively. Performance metrics such as accuracy, F1 score, Kappa, sensitivity, and specificity are also discussed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how artificial intelligence (AI) is used to understand sleep patterns and disorders. Researchers studied over 80 articles published in the last few years about different AI methods for classifying sleep stages and detecting sleep disorders. They found that most studies used brain wave signals, with some combining these signals with others. The most popular AI model was convolutional neural networks (CNN), which was used by almost a third of the researchers. Other models like LSTM, SVM, RF, and RNN were also used, but to a lesser extent. The paper also talks about how well each AI method performed in detecting sleep disorders. |
Keywords
» Artificial intelligence » Classification » Cnn » F1 score » Lstm » Neural network » Rnn
