Systematic Review of the Use of the MIT-BIH Polysomnography Database for the Detection and Classification of Sleep Disorders

Akbar Akbar, Ema Utami

Abstract


The MIT-BIH Polysomnography Database (SLPDB) is a widely adopted benchmark for the development of automated methods for sleep disorder detection and sleep stage classification. This study presents a Systematic Literature Review of 35 articles that utilize the SLPDB, examining research focus areas, types of physiological signals employed, and the computational approaches applied. Five major methodological categories were identified: Sleep Apnea Detection, Sleep Staging, Signal Processing Enhancement, Multichannel Fusion Methods, and Interpretable Artificial Intelligence, with the first two categories being the most dominant. Four groups of physiological signals—EEG, ECG, respiratory signals, and multichannel data—form the basis for model development, where EEG is predominantly used for sleep staging and ECG for sleep apnea detection. Deep learning approaches, particularly CNNs, LSTMs, and hybrid models, are the most frequently employed techniques. Reported model accuracies range from 78% to over 99%, depending on the signal modality and modeling strategy. Future research should prioritize the development of more interpretable hybrid models and broader clinical validation to enhance reproducibility and implementation readiness.

Keywords


MIT-BIH Polysomnography; systematic review; sleep apnea; machine learning; biomedical signal analysis

Full Text:

PDF

References


B. Jafari and V. Mohsenin, “Polysomnography,” Jun. 2010. DOI: 10.1016/j.ccm.2010.02.005.

Ichimaru Y, Moody GB. Development of the polysomnographic database on CD-ROM. Psychiatry Clin Neurosci. 1999 Apr;53(2):175-7. DOI: 10.1046/j.1440-1819.1999.00527.x. PMID: 10459681.

J. M. Lee, D. J. Kim, I. Y. Kim, K. S. Park, and S. I. Kim, “Detrended Fluctuation Analysis of EEG in Sleep Apnea using MIT/BIH Polysomnography Data,” Comput. Biol. Med., Vol. 32, No. 1, pp. 37–47, 2002, DOI: 10.1016/S0010-4825(01)00031-2.

Z. Zhang, Y. Xue, A. Slowik, and Z. Yuan, “Sle-CNN: A Novel Convolutional Neural Network for Sleep Stage Classification,” Neural Comput. Appl., Vol. 35, No. 23, pp. 17201–17216, Aug. 2023, DOI: 10.1007/s00521-023-08598-7.

B. Bark, B. Nam, and I. Y. Kim, “SelANet: Decision-Assisting Selective Sleep Apnea Detection based on Confidence score,” BMC Med. Inform. Decis. Mak., Vol. 23, No. 1, 2023, DOI: 10.1186/s12911-023-02292-3.

Kim, S., Choi, H.-S., Kim, D., Kim, M., Lee, S.-Y., Kim, J.-K., Kim, Y., & Lee, W. H. (2024). A Comprehensive Study on a Deep-Learning-Based Electrocardiography Analysis for Estimating the Apnea-Hypopnea Index. Diagnostics, 14(11), 1134. https://doi.org/10.3390/diagnostics14111134

Zovko, K., Sadowski, Y., Perković, T., Šolić, P., Pavlinac Dodig, I., Pecotić, R., & Đogaš, Z. (2025). Advanced Data Framework for Sleep Medicine Applications: Machine Learning-based Detection of Sleep Apnea Events. Applied Sciences, 15(1), 376. https://doi.org/10.3390/app15010376

A Abd-Alrazaq A, Aslam H, AlSaad R, Alsahli M, Ahmed A, Damseh R, Aziz S, Sheikh J. Detection of Sleep Apnea using Wearable AI: Systematic Review and Meta-Analysis. J Med Internet Res. 2024 Sep 10;26:e58187. DOI: 10.2196/58187. PMID: 39255014; PMCID: PMC11422752.

Page M J, McKenzie J E, Bossuyt P M, Boutron I, Hoffmann T C, Mulrow C D et al. "The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews" BMJ 2021; 372 :n71 DOI:10.1136/bmj.n71.

Y. Paul, R. Singh, S. Sharma, S. Singh, and I. H. Ra, “Efficient Sleep Stage Identification using Piecewise Linear EEG Signal Reduction: A Novel Algorithm for Sleep Disorder Diagnosis,” Sensors, Vol. 24, No. 16, 2024, DOI: 10.3390/s24165265.

M. Z. Suboh, R. Jaafar, N. A. Nayan, N. H. Harun, and M. S. F. Mohamad, “Analysis on Four Derivative Waveforms of Photoplethysmogram (PPG) for Fiducial Point Detection,” Front. Public Heal., Vol. 10, 2022, DOI: 10.3389/fpubh.2022.920946.

A. J. Khalaf and S. J. Mohammed, “Verification and Comparison of MIT-BIH Arrhythmia Database based on Number of Beats,” Int. J. Electr. Comput. Eng., Vol. 11, No. 6, pp. 4950–4961, Dec. 2021, DOI: 10.11591/ijece.v11i6.pp4950-4961.

L. D. Barnes, K. Lee, A. W. Kempa-Liehr, and L. E. Hallum, “Detection of Sleep Apnea from Single-Channel Electroencephalogram (EEG) using an Explainable Convolutional Neural Network (CNN),” PLoS One, Vol. 17, No. 9 September, 2022, DOI: 10.1371/journal.pone.0272167.

P. Guyot, M. Eveilleau, T. Bastogne, C. Ayav, N. Carpentier, and B. Chenuel, “Robust Performances of a Nocturnal Long-Term ECG Algorithm for the Evaluation of Sleep Apnea Syndrome: A Pilot Study,” PLoS One, Vol. 20, No. 5 May, pp. 1–15, 2025, DOI: 10.1371/journal.pone.0318622.

V. Gurrala, P. Yarlagadda, and P. Koppireddi, “Detection of Sleep Apnea based on the Analysis of Sleep Stages Data using Single Channel EEG,” Trait. du Signal, Vol. 38, No. 2, pp. 431–436, 2021, DOI: 10.18280/TS.380221.

C. G. Lucas, P. Abry, H. Wendt, and G. Didier, “Drowsiness Detection from Polysomnographic Data using Multivariate Selfsimilarity and Eigen-Wavelet Analysis,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2022. DOI: 10.1109/EMBC48229.2022.9871363.

A. Guillet, A. Arneodo, and F. Argoul, “Tracking Rhythms Coherence from Polysomnographic Records: A Time-Frequency Approach,” Front. Appl. Math. Stat., Vol. 7, 2021, DOI: 10.3389/fams.2021.624456.

A. K. Singh and S. Krishnan, “Trends in EEG Signal Feature Extraction Applications,” 2023. DOI: 10.3389/frai.2022.1072801.

D. Padovano, A. Martinez-Rodrigo, J. M. Pastor, J. J. Rieta, and R. Alcaraz, “Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea,” Appl. SCI., Vol. 15, No. 1, pp. 1–22, 2025, DOI: 10.3390/app15010433.

H. Dong, H. Wu, G. Yang, and J. Zhang, “A Convolutional Neural Network for Automatic Detection of Sleep-Breathing Events using Single-Channel ECG Signals,” Biomed. Signal Process. Control, Vol. 109, p. 107943, Nov. 2025, DOI: 10.1016/J.BSPC.2025.107943.

Z. Benkő, T. Bábel, and Z. Somogyvári, “Model-Free Detection of Unique Events in Time Series,” SCI. Rep., Vol. 12, No. 1, 2022, DOI: 10.1038/s41598-021-03526-y.

T. Zorick and M. A. Mandelkern, “Discrete Scale Invariance of Human Large EEG Voltage Deflections is More Prominent in Waking than Sleep Stage 2,” Front. Hum. Neurosci., Vol. 9, No. DEC, 2015, DOI: 10.3389/fnhum.2015.00638.

C. Usha Kumari, S. K, M. K, and P. T, “Detection of Sleep Apnea using Polysomnographic Signals,” F1000Research, Vol. 11, 2022, DOI: 10.12688/f1000research.124642.1.

Y. Que, P. Jiang, T. Zhang, and Y. Cheng, “Automated Sleep Staging based on Multi-Module Neural Network using Simpler Signal: Respiratory Signal,” 2023. DOI: 10.1117/12.2688854.

S. Rashidi and B. M. Asl, “Strength of Ensemble Learning in Automatic Sleep Stages Classification using Single-Channel EEG and ECG Signals,” Med. Biol. Eng. Comput., Vol. 62, No. 4, pp. 997–1015, Apr. 2024, DOI: 10.1007/s11517-023-02980-2.

Fan, J., Yang, S., Liu, J., Zhu, Z., Xiao, J., Chang, L., Lin, S., & Zhou, J. (2022). A High Accuracy & Ultra-Low Power ECG-Derived Respiration Estimation Processor for Wearable Respiration Monitoring Sensor. Biosensors, 12(8), 665. https://doi.org/10.3390/bios12080665.

W. Yang, J. Fan, X. Wang, and Q. Liao, “Sleep Apnea and Hypopnea Events Detection based on Airflow Signals using LSTM Network,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., Jul. 2019, pp. 2576–2579. DOI: 10.1109/EMBC.2019.8857558.

O. K. Utomo, N. Surantha, S. M. Isa, and B. Soewito, “Automatic Sleep Stage Classification using Weighted ELM and PSO on Imbalanced Data from Single Lead ECG,” in Procedia Computer Science, Elsevier B.V., 2019, pp. 321–328. DOI: 10.1016/j.procs.2019.08.173.

U. Budak, V. Bajaj, Y. Akbulut, O. Atila, and A. Sengur, “An effective Hybrid Model for EEG-based Drowsiness Detection,” IEEE Sens. J., Vol. 19, No. 17, pp. 7624–7631, Sep. 2019, DOI: 10.1109/JSEN.2019.2917850.

A. Adami, R. Boostani, F. Marzbanrad, and P. H. Charlton, “A New Framework to Estimate Breathing Rate from Electrocardiogram, Photoplethysmogram, and Blood Pressure Signals,” IEEE Access, Vol. 9, 2021, DOI: 10.1109/ACCESS.2021.3066166.

J. of Healthcare Engineering, “Retracted: Online Automatic Diagnosis System of Cardiac Arrhythmias based on MIT‐BIH ECG Database,” J. Healthc. Eng., Vol. 2023, No. 1, Jan. 2023, DOI: 10.1155/2023/9873656.

I. Belakhdar, W. Kaaniche, R. Djemal, and B. Ouni, “Single-Channel-based Automatic Drowsiness Detection Architecture with a Reduced Number of EEG Features,” Microprocess. Microsyst., Vol. 58, pp. 13–23, Apr. 2018, DOI: 10.1016/j.micpro.2018.02.004.

R. Wei, X. Zhang, J. Wang, and X. Dang, “The Research of Sleep Staging based on Single-Lead Electrocardiogram and Deep Neural Network,” Biomed. Eng. Lett., Vol. 8, No. 1, pp. 87–93, Feb. 2018, DOI: 10.1007/s13534-017-0044-1.

Y. Taghizadegan, N. Jafarnia Dabanloo, K. Maghooli, and A. Sheikhani, “Obstructive Sleep Apnea Event Prediction using Recurrence Plots and Convolutional Neural Networks (RP-CNNs) from Polysomnographic Signals,” Biomed. Signal Process. Control, Vol. 69, Aug. 2021, DOI: 10.1016/j.bspc.2021.102928.

C. Intelligence and Neuroscience, “Retracted: Empirical Analysis of Apnea Syndrome using an Artificial Intelligence-based Granger Panel Model Approach,” Comput. Intell. Neurosci., Vol. 2024, No. 1, p. 9861346, Jan. 2024, DOI: https://doi.org/10.1155/2024/9861346.

M. A. Atianashie, E. D. Armah, and N. Mohammed, “A Portable GUI based Sleep Disorder System Classification based on Convolution Neural Networks (CNN) in Raspberry PI,” J. Eng. Appl. SCI. Humanit., Vol. 6, No. 1, pp. 13–23, 2021.

W. B. Wang, D. Qin, Y. Fang, C. Zhou, and Y. Zheng, “Automatic Multi-Class Sleep Staging Method based on Novel Hybrid Features,” J. Electr. Eng. Technol., Vol. 19, No. 1, pp. 709–722, Jan. 2024, DOI: 10.1007/s42835-023-01570-4.

Q. Li, Q. Li, C. Liu, S. P. Shashikumar, S. Nemati, and G. D. Clifford, “Deep Learning in the Cross-Time Frequency Domain for Sleep Staging from a Single-Lead Electrocardiogram,” Physiol. Meas., Vol. 39, No. 12, Dec. 2018, DOI: 10.1088/1361-6579/aaf339.

M. Sharma, P. Makwana, R. S. Chad, and U. R. Acharya, “A Novel Automated Robust Dual-Channel EEG-based Sleep Scoring System using Optimal Half-Band Pair Linear-Phase Biorthogonal Wavelet Filter Bank,” Appl. Intell., Vol. 53, No. 15, pp. 18681–18699, Aug. 2023, DOI: 10.1007/s10489-022-04432-0.

C. C. Peng and C. Y. Kou, “Sleep Disorder Classification using Convolutional Neural Networks,” in IFIP Advances in Information and Communication Technology, Springer Science and Business Media Deutschland GmbH, 2023, pp. 539–548. DOI: 10.1007/978-3-031-34111-3_45.

A. Chatterjee and N. D. Jana, “Classification of Sleep Apnea Event Type using Imbalanced Labelled EEG Signal,” in 2022 IEEE Region 10 Symposium, TENSYMP 2022, Institute of Electrical and Electronics Engineers Inc., 2022. DOI: 10.1109/TENSYMP54529.2022.9864566.

A. K. Rykhalska, K. O. Ivanko, N. H. Ivanushkina, and D. O. Ivanko, “Detection of Episodes of Sleep Apnea and Hypopnea in ECG and EEG Signals by Machine Learning,” Microsystems, Electron. Acoust., Vol. 27, No. 1, pp. 251487-1-251487–11, Apr. 2022, DOI: 10.20535/2523-4455.mea.251487.

C. Brunner and F. Hofer, “SleepECG: A Python Package for Sleep Staging based on Heart Rate,” J. Open Source Softw., Vol. 8, No. 86, p. 5411, Jun. 2023, DOI: 10.21105/joss.05411.




DOI: https://doi.org/10.32520/stmsi.v15i1.5819

Article Metrics

Abstract view : 5 times
PDF - 0 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.