Wearable Sleep Apnea Detection Method Based on Multi-Task and Multi-AttentionResidualShrinkage Convolutional Neural Network
Shen Qi1,2,3, Wei Keming1,2,3, Liu Guanzheng1,2,3#*
1(School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, Guangdong, China) 2(Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, Guangzhou 510006, China) 3(Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou 510006, China)
Abstract：Sleep apnea syndrome (sleep apnea syndrome, SAS) is a common chronic respiratory disorder, often accompanied by a variety of complications, which seriously plagues human health. The SAS detection method of photoplethysmography (PPG) based on wearable devices has attracted extensive attention because of the advantages of low cost, low load and easy to wear. Aiming at the problem of greater interference of PPG signals based on wearable devices, a sleep apnea detection method based on multi-task multi-attention residual shrinkage convolutional neural network was proposed in this study. First of all, 92 wrist PPG sleep data were collected using smart bracelet devices. Next, a residual multi-attention mechanism convolution block was designed, which efficiently integrated the dual important features of the network in the time domain and the channel domain. Then, the residual shrinkage convolution block was introduced to suppress the signal noise and the redundant features of the network. Through the combination of these two blocks, a backbone network for feature extraction was constructed. The results showed that the accuracy, sensitivity, and specificity of the segment detection achieved 81.82%, 70.27%, and 85.81%, respectively; and the accuracy, sensitivity, and specificity of individual detection achieved 95.65%, 88.89%, and 97.30%, respectively. Compared with peers, the proposed method displayed better performance and was able to integrate to the wearable devices for sleep apnea syndrome detection.
[1] Nieto FJ, Young TB, Lind BK, et al. Association of sleep-disordered breathing, sleep apnea, and hypertension in a large community-based study[J]. JAMA, 2000, 283(14): 1829-1836. [2] Garvey JF, Pengo MF, Drakatos P, et al. Epidemiological aspects of obstructive sleep apnea[J]. Journal of Thoracic Disease, 2015, 7(5): 920. [3] Kushida CA, Littner MR, Morgenthaler T, et al. Practice parameters for the indications for polysomnography and related procedures: an update for 2005[J]. Sleep, 2005, 28(4): 499-523. [4] Mendonca F, Mostafa SS, Ravelo-García AG, et al. A review of obstructive sleep apnea detection approaches[J]. IEEE Journal of Biomedical and Health Informatics, 2018, 23(2): 825-837. [5] Mostafa SS, Carvalho JP, Morgado-Dias F, et al. Optimization of sleep apnea detection using SpO2 and ANN[C]// 2017 XXVI International Conference on Information, Communication And Automation Technologies (ICAT). Sarajevo, Bosnia and Herzegovina: IEEE, 2017: 1-6. [6] Taran S, Bajaj V. Sleep apnea detection using artificial bee colony optimize hermite basis functions for EEG signals[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 69(2): 608-616. [7] Li K, Pan W, Li Y, et al. A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal[J]. Neurocomputing, 2018, 294: 94-101. [8] Avcı C, Akba塂 A. Sleep apnea classification based on respiration signals by using ensemble methods[J]. Bio-Medical Materials and Engineering, 2015, 26(S1): S1703-S1710. [9] Van Steenkiste T, Groenendaal W, Dreesen P, et al. Portable detection of apnea and hypopnea events using bio-impedance of the chest and deep learning[J]. IEEE Journal of Biomedical and Health Informatics, 2020, 24(9): 2589-2598. [10] Baty F, Boesch M, Widmer S, et al. Classification of sleep apnea severity by electrocardiogram monitoring using a novel wearable device[J]. Sensors, 2020, 20(1): 286. [11] Hsu YS, Chen TY, Wu D, et al. Screening of obstructive sleep apnea in patients who snore using a patch-type device with electrocardiogram and 3-axis accelerometer[J]. Journal of Clinical Sleep Medicine, 2020, 16(7): 1149-1160. [12] Papini GB, Fonseca P, van Gilst MM, et al. Wearable monitoring of sleep-disordered breathing: estimation of the apnea-hypopnea index using wrist-worn reflective photoplethysmography[J]. Scientific Reports, 2020, 10(1): 1-15. [13] Mück JE, Ünal B, Butt H, et al. Market and patent analyses of wearables in medicine[J]. Trends in Biotechnology, 2019, 37(6): 563-566. [14] Lázaro J, Gil E, Vergara JM, et al. Pulse rate variability analysis for discrimination of sleep-apnea-related decreases in the amplitude fluctuations of pulse photoplethysmographic signal in children[J]. IEEE Journal of Biomedical and Health Informatics, 2013, 18(1): 240-246. [15] Parrino L, Ferri R, Zucconi M, et al. Commentary from the Italian Association of Sleep Medicine on the AASM manual for the scoring of sleep and associated events: for debate and discussion[J]. Sleep Medicine, 2009, 10(7): 799-808. [16] Elgendi M, Norton I, Brearley M, et al. Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions[J]. PLoS ONE, 2013, 8(10): e76585. [17] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]// Proceedings of the IEEE conference on computer vision and pattern recognition. Salt Lake City: IEEE, 2018: 7132-7141. [18] Zhao M, Zhong S, Fu X, et al. Deep residual shrinkage networks for fault diagnosis[J]. IEEE Transactions on Industrial Informatics, 2019, 16(7): 4681-4690. [19] Schroff F, Kalenichenko D, Philbin J. Facenet: a unified embedding for face recognition and clustering[C]// Proceedings of the IEEE conference on computer vision and pattern recognition. Boston: IEEE, 2015: 815-823. [20] Fan W, Stolfo SJ, Zhang J, et al. AdaCost: misclassification cost-sensitive boosting[C]// International Conference on Machine Learning. Bled: ACM, 1999, 99: 97-105. [21] Wu S, Liang D, Yang Q, et al. Regularity of heart rate fluctuations analysis in obstructive sleep apnea patients using information-based similarity[J]. Biomedical Signal Processing and Control, 2021, 65: 102370. [22] Donoho DL. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 1995, 41(3): 613-627. [23] Jain S, Kotsampasakou E, Ecker GF. Comparing the performance of meta-classifiers—a case study on selected imbalanced data sets relevant for prediction of liver toxicity[J]. Journal of Computer-Aided Molecular Design, 2018, 32(5): 583-590. [24] Song C, Liu K, Zhang X, et al. An obstructive sleep apnea detection approach using a discriminative hidden Markov model from ECG signals[J]. IEEE Transactions on Biomedical Engineering, 2015, 63(7): 1532-1542. [25] Sharma H, Sharma KK. An algorithm for sleep apnea detection from single-lead ECG using Hermite basis functions[J]. Computers in Biology and Medicine, 2016, 77: 116-124. [26] Surrel G, Aminifar A, Rincón F, et al. Online obstructive sleep apnea detection on medical wearable sensors[J]. IEEE Transactions on Biomedical Circuits and Systems, 2018, 12(4): 762-773. [27] Feng K, Qin H, Wu S, et al. A sleep apnea detection method based on unsupervised feature learning and single-lead electrocardiogram[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 70: 1-12. [28] Shen Q, Qin H, Wei K, et al. Multiscale deep neural network for obstructive sleep apnea detection using RR interval from single-lead ECG signal[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-13. [29] Wang T, Lu C, Shen G, et al. Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network[J]. Peer J, 2019, 7: e7731. [30] Chang HY, Yeh CY, Lee CT, et al. A sleep apnea detection system based on a one-dimensional deep convolution neural network model using single-lead electrocardiogram[J]. Sensors, 2020, 20(15): 4157. [31] Feng K, Qin H, Wu S, et al. A sleep apnea detection method based on unsupervised feature learning and single-lead electrocardiogram[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 70: 1-12. [32] Shen Q, Qin H, Wei K, et al. Multiscale deep neural network for obstructive sleep apnea detection using RR interval from single-lead ECG signal[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-13. [33] Yang Q, Zou L, Wei K, et al. Obstructive sleep apnea detection from single-lead electrocardiogram signals using one-dimensional squeeze-and-excitation residual group network[J]. Computers in Biology and Medicine, 2022, 140: 105124. [34] Qin H, Liu G. A dual-model deep learning method for sleep apnea detection based on representation learning and temporal dependence[J]. Neurocomputing, 2022, 473: 24-36.
