PLETHSOMNet: automated identification of insomnia using deep neural network technique with photoplethysmography (PPG) signals.

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中文解读

中文解读待补充：本站会优先为失眠研究、睡眠质量改善、昼夜节律等高价值睡眠研究添加中文说明。

Original abstract

<h4>Purpose</h4>Insomnia is a common sleep disorder, that causes difficulty in sleeping, staying asleep, or having non-restorative sleep. It often leads to daytime fatigue and impacts individuals' well-being and daily functioning. Effective detection of insomnia is crucial for proper diagnosis and treatment planning. This paper proposes an accurate noninvasive detection of insomnia using photoplethysmography (PPG) signals. PPG signals offer a convenient and accessible method for continuous sleep monitoring without the need for specialized equipment. This study proposes PLETHSOMNet an automated insomnia detection using PPG and several deep learning (DL) models. To the best of our understanding, this is the first study to use PPG signals coupled with DL techniques for insomnia detection automatically.<h4>Methods</h4>The proposed approach utilizes PPG signals extracted from the Cyclic Alternating Pattern (CAP) sleep database. Several deep-learning architectures were explored to classify individuals with insomnia and healthy sleepers. Model performance was evaluated based on different segment lengths of PPG signals, specifically 2  -second and 30  -second segments, to assess the system's adaptability.<h4>Results</h4>For detecting insomnia automatically the proposed model has achieved the classification accuracy of 95  .89  % for a 2  -second and 95  .70  % for a 30  -second PPG segment. Experimental results indicate that the model is highly effective in distinguishing individuals with insomnia from healthy sleepers. Overall, the findings highlight the promising potential of PPG-based approaches as a reliable and efficient tool for sleep disorder diagnosis.<h4>Conclusions</h4>The experiment results indicate the potential of applying PPG-derived deep learning models in noninvasive insomnia diagnosis. As good as the results are, more validation on a larger dataset would be required before incorporating this technology into home-wearable PPG-based sleep-tracking systems.

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• Official / arXiv page

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