Explainable AI for Sleep Disorder Diagnostics

Nirubama Vijayakumarand N.Arivazhagan
SRM Institute of Science and Technology, Chengalpattu, 603203, India
nm1203@srmist.edu.in
arivazhn@srmist.edu.in
DOI: 10.46793/BISEC25.454V

 

ABSTRACT: Millions of people in worldwide suffer from sleep problems, which will affect total quality of life. Due to the recent advancements in artificial intel-ligence (AI), it has introduced innovative diagnostic tools for sleep disorders, the machine learning models are used to analyse complex datasets from wearable devices, polysomnography (PSG), and other sleep-monitoring technologies. But, the “black-box” nature of these AI models limits their adoption in clinical settings because of the absence of interpretability and transparency. These challenges are addressed by Explainable AI (XAI) by giving insights for the decision-making processes of AI models. This review explores the state-of-the-art XAI techniques, their benefits, limitations, and future directions applied to sleep disorder diagnos-tics. The article also discusses the integration of XAI with wearable tech-nology and clinical progress to increase diagnostic accuracy and thereby increasing patient trust.

KEYWORDS: Machine learning, Deep learning Explainable AI

REFERENCES:

  1. Xu, S., Faust, O., Seoni, S., Chakraborty, S., Barua, P.D., Loh, H.W., Elphick, H., Moli-nari, F., Acharya, U.R.: A review of automated sleep disorder detection. Computers in Biol-ogy and Medicine 150, 106100 (2022)
  2. Vaquerizo-Villar, F., Gutiérrez-Tobal, G.C., Calvo, E., Álvarez, D., Kheirandish-Gozal, L., Del Campo, F., Gozal, D., Hornero, R.: An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea. Computers in Biol-ogy and Medicine 165, 107419 (2023)
  3. Ingle, M., Sharma, M., Verma, S., Sharma, N., Bhurane, A., Acharya, U.R.: Automated explainable wavelet-based sleep scoring system for a population suspected with insomnia, ap-nea and periodic leg movement. Medical Engineering & Physics 130, 104208 (2024)
  4. Parbat, D., Chakraborty, M.: Multiscale entropy analysis of single lead ECG and ECG derived respiration for AI-based prediction of sleep apnea events. Biomedical Signal Processing and Control 87, 105444 (2024)
  5. Sharma, M., Lodhi, H., Yadav, R., Sampathila, N., Swathi, K.S., Acharya, U.R.: Automated explainable detection of cyclic alternating pattern (CAP) phases and sub-phases using wave-let-based single-channel EEG signals. IEEE Access 11, 50946–50961 (2023)
  6. Yook, S., Kim, D., Gupte, C., Joo, E.Y., Kim, H.: Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical se-verity. Sleep Medicine 114, 211–219 (2024)
  7. Jiménez-García, J., García, M., Gutiérrez-Tobal, G.C., Kheirandish-Gozal, L., Vaquerizo-Villar, F., Álvarez, D., Del Campo, F., Gozal, D., Hornero, R.: An explainable deep-learning architecture for pediatric sleep apnea identification from overnight airflow and oxi-metry signals. Biomedical Signal Processing and Control 87, 105490 (2024)
  8. Rossi, M., Sala, D., Bovio, D., Salito, C., Alessandrelli, G., Lombardi, C., Mainardi, L., Cerveri, P.: Sleep-see-through: Explainable deep learning for sleep event detection and quantification from wearable somnography. IEEE Journal of Biomedical and Health Informatics 27(7), 3129–3140 (2023)
  9. Baek, S., Baek, J., Yu, H., Lee, C., Park, C.: Explainable sleep staging algorithm using a single-channel electroencephalogram. IEIE Transactions on Smart Processing & Computing 11(1), 8–13 (2022)
  10. Singh, K., Mehta, A., Chaudhary, A.: AI-driven sleep apnea detection and prediction. In: Proceedings of the 2024 International Conference on Computational Intelligence and Com-puting Applications (ICCICA 2024), vol. 1, pp. 237–241. IEEE, New York (2024)
  11. Kaur, A., Neeru, N.: Automatic detection and classification of sleep disorders using AI-learning models. In: Proceedings of the 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE 2024), pp. 1017–1022. IEEE, New York (2024)
  12. La Fiscal, L., Jennebauffe, C., Bruyneel, M., Ris, L., Lefebvre, L., Siebert, X., Gosselin, B.: Explainable AI for EEG biomarkers identification in obstructive sleep apnea severity scoring task. In: Proceedings of the 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER 2023), pp. 1–6. IEEE, San Diego (2023)

 

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