{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:01:09Z","timestamp":1782313269227,"version":"3.54.5"},"reference-count":41,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T00:00:00Z","timestamp":1707436800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Information &amp; Communications Technology Planning &amp; Evaluation (IITP)","award":["RS-2022-00155885"],"award-info":[{"award-number":["RS-2022-00155885"]}]},{"name":"Institute of Information &amp; Communications Technology Planning &amp; Evaluation (IITP)","award":["IITP-2024-2020-0-01741"],"award-info":[{"award-number":["IITP-2024-2020-0-01741"]}]},{"name":"MSIT, Korea","award":["RS-2022-00155885"],"award-info":[{"award-number":["RS-2022-00155885"]}]},{"name":"MSIT, Korea","award":["IITP-2024-2020-0-01741"],"award-info":[{"award-number":["IITP-2024-2020-0-01741"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Obstructive sleep apnea (OSA), a prevalent sleep disorder, is intimately associated with various other diseases, particularly cardiovascular conditions. The conventional diagnostic method, nocturnal polysomnography (PSG), despite its widespread use, faces challenges due to its high cost and prolonged duration. Recent developments in electrocardiogram-based diagnostic techniques have opened new avenues for addressing these challenges, although they often require a deep understanding of feature engineering. In this study, we introduce an innovative method for OSA classification that combines a composite deep convolutional neural network model with a multimodal strategy for automatic feature extraction. This approach involves transforming the original dataset into scalogram images that reflect heart rate variability attributes and Gramian angular field matrix images that reveal temporal characteristics, aiming to enhance the diversity and richness of data features. The model comprises automatic feature extraction and feature enhancement components and has been trained and validated on the PhysioNet Apnea-ECG database. The experimental results demonstrate the model\u2019s exceptional performance in diagnosing OSA, achieving an accuracy of 96.37%, a sensitivity of 94.67%, a specificity of 97.44%, and an AUC of 0.96. These outcomes underscore the potential of our proposed model as an efficient, accurate, and convenient tool for OSA diagnosis.<\/jats:p>","DOI":"10.3390\/s24041159","type":"journal-article","created":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T11:42:05Z","timestamp":1707478925000},"page":"1159","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Multi-Feature Automatic Extraction for Detecting Obstructive Sleep Apnea Based on Single-Lead Electrocardiography Signals"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9766-2067","authenticated-orcid":false,"given":"Yu","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Major in Bio Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6587-7044","authenticated-orcid":false,"given":"Kyungtae","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1024\/1661-8157\/a003198","article-title":"[Obstruktives Schlafapnoe-Syndrom]","volume":"108","author":"Lichtblau","year":"2019","journal-title":"Praxis"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1007\/s11325-018-1745-0","article-title":"Implication of Mixed Sleep Apnea Events in Adult Patients with Obstructive Sleep Apnea-Hypopnea Syndrome","volume":"23","author":"Yang","year":"2019","journal-title":"Sleep Breath."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1111\/resp.13838","article-title":"Global Burden of Sleep-Disordered Breathing and Its Implications","volume":"25","author":"Lyons","year":"2020","journal-title":"Respirology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1016\/j.jacc.2016.11.069","article-title":"Sleep Apnea: Types, Mechanisms, and Clinical Cardiovascular Consequences","volume":"69","author":"Javaheri","year":"2017","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/B978-0-444-64032-1.00025-4","article-title":"Chapter 25\u2014Polysomnography","volume":"Volume 160","author":"Levin","year":"2019","journal-title":"Handbook of Clinical Neurology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/TBME.2015.2498199","article-title":"An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model from ECG Signals","volume":"63","author":"Song","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Srivastava, G., Chauhan, A., Kargeti, N., Pradhan, N., and Dhaka, V.S. (2023). ApneaNet: A Hybrid 1DCNN-LSTM Architecture for Detection of Obstructive Sleep Apnea Using Digitized ECG Signals. Biomed. Signal Process. Control., 84.","DOI":"10.1016\/j.bspc.2023.104754"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, R., Li, W., Yue, K., and Li, Y. (2023). Convolutional Neural Network for Screening of Obstructive Sleep Apnea Using Snoring Sounds. Biomed. Signal Process. Control, 86.","DOI":"10.1016\/j.bspc.2023.104966"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lin, X., Cheng, H., Lu, Y., Luo, H., Li, H., Qian, Y., Zhou, L., Zhang, L., and Wang, M. (2022). Contactless Sleep Apnea Detection in Snoring Signals Using Hybrid Deep Neural Networks Targeted for Embedded Hardware Platform with Real-Time Applications. Biomed. Signal Process. Control, 77.","DOI":"10.1016\/j.bspc.2022.103765"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, H., Cui, S., Zhao, X., and Cong, F. (2023). Detection of Obstructive Sleep Apnea from Single-Channel ECG Signals Using a CNN-Transformer Architecture. Biomed. Signal Process. Control, 82.","DOI":"10.1016\/j.bspc.2023.104581"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1504\/IJBET.2020.107756","article-title":"Automated Recognition of Obstructive Sleep Apnea Using Ensemble Support Vector Machine Classifier","volume":"33","author":"Kalaivani","year":"2020","journal-title":"Int. J. Biomed. Eng. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.compbiomed.2016.08.012","article-title":"An Algorithm for Sleep Apnea Detection from Single-Lead ECG Using Hermite Basis Functions","volume":"77","author":"Sharma","year":"2016","journal-title":"Comput. Biol. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.inffus.2021.02.012","article-title":"A Review of Multimodal Image Matching: Methods and Applications","volume":"73","author":"Jiang","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhou, Y., He, Y., and Kang, K. (2022, January 6\u20138). OSA-CCNN: Obstructive Sleep Apnea Detection Based on a Composite Deep Convolution Neural Network Model Using Single-Lead ECG Signal. Proceedings of the 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Las Vegas, NV, USA.","DOI":"10.1109\/BIBM55620.2022.9995675"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e13274","DOI":"10.1111\/jsr.13274","article-title":"Heart Rate Variability and Obstructive Sleep Apnea: Current Perspectives and Novel Technologies","volume":"30","author":"Ucak","year":"2021","journal-title":"J. Sleep Res."},{"key":"ref_16","first-page":"5","article-title":"Detection of Obstructive Sleep Apnea from ECG Signal Using SVM Based Grid Search","volume":"67","author":"Valavan","year":"2021","journal-title":"Int. J. Electron. Telecommun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.compbiomed.2019.03.016","article-title":"Automated Detection of Sleep Apnea Using Sparse Residual Entropy Features with Various Dictionaries Extracted from Heart Rate and EDR Signals","volume":"108","author":"Viswabhargav","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neucom.2018.03.011","article-title":"A Method to Detect Sleep Apnea Based on Deep Neural Network and Hidden Markov Model Using Single-Lead ECG Signal","volume":"294","author":"Li","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tripathy, R.K., Gajbhiye, P., and Acharya, U.R. (2020). Automated Sleep Apnea Detection from Cardio-Pulmonary Signal Using Bivariate Fast and Adaptive EMD Coupled with Cross Time\u2013Frequency Analysis. Comput. Biol. Med., 120.","DOI":"10.1016\/j.compbiomed.2020.103769"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e7731","DOI":"10.7717\/peerj.7731","article-title":"Sleep Apnea Detection from a Single-Lead ECG Signal with Automatic Feature-Extraction through a Modified LeNet-5 Convolutional Neural Network","volume":"7","author":"Wang","year":"2019","journal-title":"PeerJ"},{"key":"ref_21","unstructured":"McNames, J.N., and Fraser, A.M. (2000, January 24\u201327). Obstructive Sleep Apnea Classification Based on Spectrogram Patterns in the Electrocardiogram. Proceedings of the Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163), Cambridge, MA, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Singh, S.A., and Majumder, S. (2019). A Novel Approach Osa Detection Using Single-Lead Ecg Scalogram Based on Deep Neural Network. J. Mech. Med. Biol., 19.","DOI":"10.1142\/S021951941950026X"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Niroshana, S.M.I., Zhu, X., Nakamura, K., and Chen, W. (2021). A Fused-Image-Based Approach to Detect Obstructive Sleep Apnea Using a Single-Lead ECG and a 2D Convolutional Neural Network. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0250618"},{"key":"ref_24","unstructured":"Penzel, T., Moody, G.B., Mark, R.G., Goldberger, A.L., and Peter, J.H. (2000, January 24\u201327). The Apnea-ECG Database. Proceedings of the Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163), Cambridge, MA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"115950","DOI":"10.1016\/j.eswa.2021.115950","article-title":"Detection of Sleep Apnea Using Machine Learning Algorithms Based on ECG Signals: A Comprehensive Systematic Review","volume":"187","author":"Salari","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Basu, S., and Mamud, S. (2020, January 5\u20136). Comparative Study on the Effect of Order and Cut off Frequency of Butterworth Low Pass Filter for Removal of Noise in ECG Signal. Proceedings of the 2020 IEEE 1st International Conference for Convergence in Engineering (ICCE), Kolkata, India.","DOI":"10.1109\/ICCE50343.2020.9290646"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e12302","DOI":"10.1002\/inf2.12302","article-title":"Ultra-Robust Stretchable Electrode for e-Skin: In Situ Assembly Using a Nanofiber Scaffold and Liquid Metal to Mimic Water-to-Net Interaction","volume":"4","author":"Cao","year":"2022","journal-title":"InfoMat"},{"key":"ref_28","first-page":"1","article-title":"Signal Processing Techniques for Removing Noise from ECG Signals","volume":"3","author":"Kher","year":"2019","journal-title":"J. Biomed. Eng. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1109\/TASE.2014.2345667","article-title":"An Automatic Screening Approach for Obstructive Sleep Apnea Diagnosis Based on Single-Lead Electrocardiogram","volume":"12","author":"Chen","year":"2015","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, T., Lu, C., Sun, Y., Yang, M., Liu, C., and Ou, C. (2021). Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network. Entropy, 23.","DOI":"10.3390\/e23010119"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"170820","DOI":"10.1109\/ACCESS.2019.2956050","article-title":"A Novel Method to Detect Multiple Arrhythmias Based on Time-Frequency Analysis and Convolutional Neural Networks","volume":"7","author":"Wu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.compbiomed.2016.07.008","article-title":"Assessing Heart Rate Variability through Wavelet-Based Statistical Measures","volume":"77","author":"Wachowiak","year":"2016","journal-title":"Comput. Biol. Med."},{"key":"ref_33","unstructured":"Wang, Z., and Oates, T. (2015). Imaging Time-Series to Improve Classification and Imputation. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"12777","DOI":"10.1007\/s11042-019-08453-9","article-title":"Dropout vs. Batch Normalization: An Empirical Study of Their Impact to Deep Learning","volume":"79","author":"Garbin","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014). Going Deeper with Convolutions. arXiv.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhou, Z.-H. (2012). Ensemble Methods: Foundations and Algorithms, CRC Press.","DOI":"10.1201\/b12207"},{"key":"ref_38","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognitio. arXiv."},{"key":"ref_39","first-page":"802","article-title":"Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting","volume":"Volume 1","author":"Shi","year":"2015","journal-title":"Proceedings of the 28th International Conference on Neural Information Processing Systems"},{"key":"ref_40","unstructured":"Powers, D.M.W. (2020). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1109\/TKDE.2019.2912815","article-title":"Reliable Accuracy Estimates from K-Fold Cross Validation","volume":"32","author":"Wong","year":"2020","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/4\/1159\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:58:04Z","timestamp":1760104684000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/4\/1159"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,9]]},"references-count":41,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24041159"],"URL":"https:\/\/doi.org\/10.3390\/s24041159","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,9]]}}}