Sleep Apnea Detection Model Using Time Window and One-Dimensional Convolutional Neural Network on Single-Lead Electrocardiogram

Fadil Pratama; Wiharto Wiharto; Umi Salamah

doi:10.35882/jeeemi.v7i1.573

Fadil Pratama Department of Informatics, Faculty of Information Technology and Data Science, Universitas Sebelas Maret, Surakarta, Indonesia https://orcid.org/0009-0001-9378-1576
Wiharto Wiharto Department of Informatics, Faculty of Information Technology and Data Science, Universitas Sebelas Maret, Surakarta, Indonesia https://orcid.org/0000-0001-7014-7620
Umi Salamah Department of Informatics, Faculty of Information Technology and Data Science, Universitas Sebelas Maret, Surakarta, Indonesia https://orcid.org/0000-0002-5077-1191

DOI: https://doi.org/10.35882/jeeemi.v7i1.573

Keywords: Electrocardiogram, One-Dimensional Convolutional Neural Network, Sleep Apnea, Stratified K-Fold Cross-Validation, Time Window

Abstract

Sleep apnea is an important disorder that involves frequent disruptions in breathing during sleep, which can result in numerous serious health issues, such as cognitive deterioration, cardiovascular illness, and heightened mortality risk. This study introduces a detailed model designed for the detection of sleep apnea using single-lead electrocardiogram signals, providing an accurate detection method. We can use single-lead ECG signals to get ECG-Derived Respiration (EDR). EDR combines important respiratory signals with RR intervals to help find sleep apnea more accurately. We structure the research process into seven systematic stages, ensuring a comprehensive approach to the issue. The process commences with the acquisition of data from the "Apnea-ECG Database" accessible on the PhysioNet platform, which underpins the ensuing analysis. Subsequent to data collection, we execute a sequence of preprocessing procedures, including segmentation, filtering, and R-peak detection, to enhance the ECG data for analysis. After that, we do feature extraction, which gives us 12 unique features from the RR interval and 6 features from the R-peak amplitude, which are both necessary for the model to work. The research subsequently utilizes feature engineering, implementing a Time Window methodology to encapsulate the temporal dynamics of the data. To ensure the results are robust, we conduct model evaluation using stratified K-fold cross-validation with five folds. The modeling technique employs a 1D Convolutional Neural Network (1D-CNN) utilizing the Adam optimizer. Ultimately, the performance assessment shows an accuracy score reaching 89.87%, sensitivity at 86.16%, specificity at 92.30%, and an AUC score of 0.96, attained with a Time Window size of 15. This model signifies a substantial improvement in performance relative to previous studies and serves as a feasible option for the detection of sleep apnea

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References

S. Taran and V. Bajaj, “Sleep Apnea Detection Using Artificial Bee Colony Optimize Hermite Basis Functions for EEG Signals,” IEEE Trans Instrum Meas, vol. 69, no. 2, pp. 608–616, Feb. 2020, doi: 10.1109/TIM.2019.2902809.

J. Vanek et al., “Obstructive sleep apnea, depression and cognitive impairment,” Aug. 01, 2020, Elsevier B.V. doi: 10.1016/j.sleep.2020.03.017.

C. H. Lin, R. C. Lurie, and O. D. Lyons, “Sleep Apnea and Chronic Kidney Disease: A State-of-the-Art Review,” Mar. 01, 2020, Elsevier Inc. doi: 10.1016/j.chest.2019.09.004.

C. Song, K. Liu, X. Zhang, L. Chen, and X. Xian, “An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model from ECG Signals,” IEEE Trans Biomed Eng, vol. 63, no. 7, pp. 1532–1542, Jul. 2016, doi: 10.1109/TBME.2015.2498199.

S. Q. Ali, S. Khalid, and S. B. Belhaouari, “A Novel Technique to Diagnose Sleep Apnea in Suspected Patients Using Their ECG Data,” IEEE Access, vol. 7, pp. 35184–35194, 2019, doi: 10.1109/ACCESS.2019.2904601.

M. Bahrami and M. Forouzanfar, “Detection of sleep apnea from single-lead ECG: Comparison of deep learning algorithms,” in 2021 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2021 - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., Jun. 2021. doi: 10.1109/MeMeA52024.2021.9478745.

M. De Zambotti et al., “Impact of evening alcohol consumption on nocturnal autonomic and cardiovascular function in adult men and women: A dose-response laboratory investigation,” 2020, doi: 10.1093/sleep/zsaa135/5871424.

R. B. Shouldice, L. M. O’Brien, C. O’Brien, P. De Chazal, D. Gozal, and C. Heneghan, “Detection of Obstructive Sleep Apnea in Pediatric Subjects using Surface Lead Electrocardiogram Features,” Sleep, vol. 27, no. 4, pp. 784–792, 2004, doi: 10.1093/sleep/27.4.784.

A. Rizal, F. D. A. A. Siregar, and H. T. Fauzi, “Obstructive Sleep Apnea (OSA) Classification Based on Heart Rate Variability (HRV) on Electrocardiogram (ECG) Signal Using Support Vector Machine (SVM),” Traitement du Signal, vol. 39, no. 2, pp. 469–474, Apr. 2022, doi: 10.18280/ts.390208.

T. Wang, C. Lu, and G. Shen, “Detection of Sleep Apnea from Single-Lead ECG Signal Using a Time Window Artificial Neural Network,” Biomed Res Int, vol. 2019, 2019, doi: 10.1155/2019/9768072.

A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “CNN features off-the-shelf: An astounding baseline for recognition,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE Computer Society, Sep. 2014, pp. 512–519. doi: 10.1109/CVPRW.2014.131.

Y. Wei et al., “HCP: A flexible CNN framework for multi-label image classification,” IEEE Trans Pattern Anal Mach Intell, vol. 38, no. 9, pp. 1901–1907, Sep. 2016, doi: 10.1109/TPAMI.2015.2491929.

B. Sedighi, I. Palit, X. S. Hu, J. Nahas, and M. Niemier, “A CNN-Inspired Mixed Signal Processor Based on Tunnel Transistors,” EDA Consortium, 2015. doi: 10.7873/DATE.2015.0053.

Y. H. Kwon, S. B. Shin, and S. D. Kim, “Electroencephalography based fusion two-dimensional (2D)-convolution neural networks (CNN) model for emotion recognition system,” Sensors (Switzerland), vol. 18, no. 5, May 2018, doi: 10.3390/s18051383.

M. Matsugu, K. Mori, Y. Mitari, and Y. Kaneda, “Subject independent facial expression recognition with robust face detection using a convolutional neural network,” in Neural Networks, Elsevier Ltd, 2003, pp. 555–559. doi: 10.1016/S0893-6080(03)00115-1.

R. Haidar, I. Koprinska, and B. Jeffries, “Feature Learning and Data Compression of Biosignals Using Convolutional Autoencoders for Sleep Apnea Detection,” International Conference on Neural Information Processing, pp. 162–174, 2019, doi: 10.1007/978-3-030-36708-4_14.

U. Erdenebayar, Y. J. Kim, J. U. Park, E. Y. Joo, and K. J. Lee, “Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram,” Comput Methods Programs Biomed, vol. 180, Oct. 2019, doi: 10.1016/j.cmpb.2019.105001.

K. McClure, B. Erdreich, J. H. T. Bates, R. S. McGinnis, A. Masquelin, and S. Wshah, “Classification and detection of breathing patterns with wearable sensors and deep learning,” Sensors (Switzerland), vol. 20, no. 22, pp. 1–13, Nov. 2020, doi: 10.3390/s20226481.

M. Bahrami and M. Forouzanfar, “Sleep Apnea Detection from Single-Lead ECG: A Comprehensive Analysis of Machine Learning and Deep Learning Algorithms,” IEEE Trans Instrum Meas, vol. 71, 2022, doi: 10.1109/TIM.2022.3151947.

S. Widodo, H. Brawijaya, and S. Samudi, “Stratified K-fold cross validation optimization on machine learning for prediction,” Sinkron, vol. 7, no. 4, pp. 2407–2414, Oct. 2022, doi: 10.33395/sinkron.v7i4.11792.

T. Wang, C. Lu, G. Shen, and F. Hong, “Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network,” PeerJ, vol. 2019, no. 9, 2019, doi: 10.7717/peerj.7731.

T. Penzel’, G. B. Moody2j3, R. G. Mark2j3, A. L. Goldberges, and J. H. Peter’, “The Apnea-ECG Database,” 2000. doi: 10.1109/CIC.2000.898505.

A. L. Goldberger et al., “PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.,” Circulation, vol. 101, no. 23, 2000, doi: 10.1161/01.cir.101.23.e215.

W. H. Ali and E. S. Kohiwole, “Design and implementation of low-pass, high-pass and band-pass Finite Impulse Response (FIR) filters using FPGA,” in Digital Signal Processing (DSP): Fundamentals, Techniques and Applications, Nova Science Publishers, Inc., 2016, pp. 131–151. doi: 10.4236/cs.2015.62004.

P. Hamilton and E. P. Limited, “Open Source ECG Analysis,” 2002. doi: 10.1109/CIC.2002.1166717.

L. Chen, X. Zhang, and C. Song, “An automatic screening approach for obstructive sleep apnea diagnosis based on single-lead electrocardiogram,” IEEE Transactions on Automation Science and Engineering, vol. 12, no. 1, pp. 106–115, Jan. 2015, doi: 10.1109/TASE.2014.2345667.

B. Hu, J. Liu, R. Zhao, Y. Xu, and T. Huo, “A New Fault Diagnosis Method for Unbalanced Data Based on 1DCNN and L2-SVM,” Applied Sciences (Switzerland), vol. 12, no. 19, Oct. 2022, doi: 10.3390/app12199880.

K. Yang, Z. Huang, X. Wang, and X. Li, “A Blind Spectrum Sensing Method Based on Deep Learning,” Sensors, vol. 19, no. 10, May 2019, doi: 10.3390/s19102270.

X. Li, Y. Li, Y. Cao, S. Duan, X. Wang, and Z. Zhao, “Fault Diagnosis Method for Aircraft EHA Based on FCNN and MSPSO Hyperparameter Optimization,” Applied Sciences (Switzerland), vol. 12, no. 17, Sep. 2022, doi: 10.3390/app12178562.

F. Xu, Z. Sui, J. Ye, and J. Xu, “Ternary Precursor Centrifuge Rolling Bearing Fault Diagnosis Based on Adaptive Sample Length Adjustment of 1DCNN-SeNet,” Processes, vol. 12, no. 4, Apr. 2024, doi: 10.3390/pr12040702.

D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” Dec. 2014.

Ž. Đ. Vujović, “Classification Model Evaluation Metrics,” IJACSA) International Journal of Advanced Computer Science and Applications, vol. 12, no. 6, p. 2021, 2021, doi: 10.14569/IJACSA.2021.0120670.

R. Shoghi, L. Morand, D. Helm, and A. Hartmaier, “Optimizing machine learning yield functions using query-by-committee for support vector classification with a dynamic stopping criterion,” Comput Mech, vol. 74, pp. 447–466, 2024, doi: 10.1007/s00466-023-02440-6.

V. R. Prasetyo, M. Mercifia, A. Averina, L. Sunyoto, and Budiarjo, “Prediksi Rating Film pada Website IMDB Menggunakan Metode Neural Network,” NERO (Networking Engineering Research Operation), vol. 7, no. 1, pp. 1–8, 2022.