{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T07:31:01Z","timestamp":1769931061000,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819638628","type":"print"},{"value":"9789819638635","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-3863-5_39","type":"book-chapter","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T03:40:08Z","timestamp":1743824408000},"page":"424-435","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Lightweight Feature Fusion and\u00a0Machine Learning Method for\u00a0Effective Home Monitoring and\u00a0Early Detection of\u00a0Sleep Apnea"],"prefix":"10.1007","author":[{"given":"Subrata","family":"Sarkar","sequence":"first","affiliation":[]},{"given":"Debjit","family":"Dhar","sequence":"additional","affiliation":[]},{"given":"Rajib","family":"Sarkar","sequence":"additional","affiliation":[]},{"given":"Sanjoy K.","family":"Saha","sequence":"additional","affiliation":[]},{"given":"Tapabrata","family":"Chakraborti","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,4]]},"reference":[{"key":"39_CR1","doi-asserted-by":"publisher","first-page":"101337","DOI":"10.1016\/j.smrv.2020.101337","volume":"53","author":"I Umbro","year":"2020","unstructured":"Umbro, I., Fabiani, V., Fabiani, M., Angelico, F., Del Ben, M.: A systematic review on the association between obstructive sleep apnea and chronic kidney disease. Sleep Med. Rev. 53, 101337 (2020)","journal-title":"Sleep Med. Rev."},{"issue":"2","key":"39_CR2","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1378\/chest.09-2954","volume":"138","author":"DA Calhoun","year":"2010","unstructured":"Calhoun, D.A., Harding, S.M.: Sleep and hypertension. Chest 138(2), 434\u2013443 (2010)","journal-title":"Chest"},{"issue":"8","key":"39_CR3","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1016\/S2213-2600(19)30198-5","volume":"7","author":"AV Benjafield","year":"2019","unstructured":"Benjafield, A.V., et al.: Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respiratory Med. 7(8), 687\u2013698 (2019)","journal-title":"Lancet Respiratory Med."},{"key":"39_CR4","doi-asserted-by":"publisher","first-page":"104199","DOI":"10.1016\/j.compbiomed.2020.104199","volume":"130","author":"KN Rajesh","year":"2021","unstructured":"Rajesh, K.N., Dhuli, R., Kumar, T.S.: Obstructive sleep apnea detection using discrete wavelet transform-based statistical features. Comput. Biol. Med. 130, 104199 (2021)","journal-title":"Comput. Biol. Med."},{"key":"39_CR5","doi-asserted-by":"publisher","first-page":"173428","DOI":"10.1109\/ACCESS.2020.3025808","volume":"8","author":"H Azimi","year":"2020","unstructured":"Azimi, H., Xi, P., Bouchard, M., Goubran, R., Knoefel, F.: Machine learning-based automatic detection of central sleep apnea events from a pressure sensitive mat. IEEE Access 8, 173428\u2013173439 (2020)","journal-title":"IEEE Access"},{"issue":"14","key":"39_CR6","doi-asserted-by":"publisher","first-page":"6622","DOI":"10.3390\/app11146622","volume":"11","author":"A Sheta","year":"2021","unstructured":"Sheta, A., et al.: Diagnosis of obstructive sleep apnea from ECG signals using machine learning and deep learning classifiers. Appl. Sci. 11(14), 6622 (2021)","journal-title":"Appl. Sci."},{"issue":"1","key":"39_CR7","first-page":"9768072","volume":"2019","author":"T Wang","year":"2019","unstructured":"Wang, T., Lu, C., Shen, G.: Detection of sleep apnea from single-lead ECG signal using a time window artificial neural network. Biomed. Res. Int. 2019(1), 9768072 (2019)","journal-title":"Biomed. Res. Int."},{"issue":"4","key":"39_CR8","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1109\/JBHI.2017.2734074","volume":"22","author":"M Nazari","year":"2017","unstructured":"Nazari, M., Sakhaei, S.M.: Variational mode extraction: a new efficient method to derive respiratory signals from ECG. IEEE J. Biomed. Health Inform. 22(4), 1059\u20131067 (2017)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"8369","key":"39_CR9","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/S0140-6736(84)90062-X","volume":"323","author":"C Guilleminault","year":"1984","unstructured":"Guilleminault, C., Winkle, R., Connolly, S., Melvin, K., Tilkian, A.: Cyclical variation of the heart rate in sleep apnoea syndrome: mechanisms, and usefulness of 24 h electrocardiography as a screening technique. Lancet 323(8369), 126\u2013131 (1984)","journal-title":"Lancet"},{"issue":"3","key":"39_CR10","doi-asserted-by":"publisher","first-page":"479","DOI":"10.5664\/jcsm.6506","volume":"13","author":"VK Kapur","year":"2017","unstructured":"Kapur, V.K., et al.: Clinical practice guideline for diagnostic testing for adult obstructive sleep apnea: an American academy of sleep medicine clinical practice guideline. J. Clin. Sleep Med. 13(3), 479\u2013504 (2017)","journal-title":"J. Clin. Sleep Med."},{"key":"39_CR11","doi-asserted-by":"publisher","first-page":"103769","DOI":"10.1016\/j.compbiomed.2020.103769","volume":"120","author":"RK Tripathy","year":"2020","unstructured":"Tripathy, R.K., Gajbhiye, P., Acharya, U.R.: Automated sleep apnea detection from cardio-pulmonary signal using bivariate fast and adaptive EMD coupled with cross time-frequency analysis. Comput. Biol. Med. 120, 103769 (2020)","journal-title":"Comput. Biol. Med."},{"key":"39_CR12","doi-asserted-by":"publisher","first-page":"102796","DOI":"10.1016\/j.dsp.2020.102796","volume":"104","author":"H Singh","year":"2020","unstructured":"Singh, H., Tripathy, R.K., Pachori, R.B.: Detection of sleep apnea from heart beat interval and ECG derived respiration signals using sliding mode singular spectrum analysis. Digit. Signal Proces. 104, 102796 (2020)","journal-title":"Digit. Signal Proces."},{"key":"39_CR13","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.compbiomed.2019.03.016","volume":"108","author":"CSSS Viswabhargav","year":"2019","unstructured":"Viswabhargav, C.S.S.S., Tripathy, R.K., Acharya, U.R.: Automated detection of sleep apnea using sparse residual entropy features with various dictionaries extracted from heart rate and EDR signals. Comput. Biol. Med. 108, 20\u201330 (2019)","journal-title":"Comput. Biol. Med."},{"key":"39_CR14","doi-asserted-by":"publisher","first-page":"200477","DOI":"10.1109\/ACCESS.2020.3036024","volume":"8","author":"N Pombo","year":"2020","unstructured":"Pombo, N., Silva, B.M.C., Pinho, A.M., Garcia, N.: Classifier precision analysis for sleep apnea detection using ECG signals. IEEE Access 8, 200477\u2013200485 (2020)","journal-title":"IEEE Access"},{"issue":"8","key":"39_CR15","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1021\/ac60214a047","volume":"36","author":"A Savitzky","year":"1964","unstructured":"Savitzky, A., Golay, M.J.E.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8), 1627\u20131639 (1964)","journal-title":"Anal. Chem."},{"issue":"9","key":"39_CR16","doi-asserted-by":"publisher","first-page":"4218","DOI":"10.3390\/app12094218","volume":"12","author":"J Zhu","year":"2022","unstructured":"Zhu, J., Zhou, A., Qiong Gong, Yu., Zhou, J.H., Chen, Z.: Detection of sleep apnea from electrocardiogram and pulse oximetry signals using random forest. Appl. Sci. 12(9), 4218 (2022)","journal-title":"Appl. Sci."},{"issue":"9","key":"39_CR17","doi-asserted-by":"publisher","first-page":"e0274225","DOI":"10.1371\/journal.pone.0274225","volume":"17","author":"M Arun Kumar","year":"2022","unstructured":"Arun Kumar, M., Chakrapani, A.: Classification of ECG signal using FFT based improved AlexNet classifier. PLOS One 17(9), e0274225 (2022)","journal-title":"PLOS One"},{"issue":"4","key":"39_CR18","doi-asserted-by":"publisher","first-page":"1689","DOI":"10.3758\/s13428-020-01516-y","volume":"53","author":"D Makowski","year":"2021","unstructured":"Makowski, D., et al.: NeuroKit2: a python toolbox for neurophysiological signal processing. Behav. Res. Methods 53(4), 1689\u20131696 (2021)","journal-title":"Behav. Res. Methods"},{"key":"39_CR19","unstructured":"Cumpston, E., Chen, P.: Sleep\u00a0apnea syndrome. In: StatPearls [Internet] (2024). Updated 4 Sept 2023"},{"key":"39_CR20","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"39_CR21","doi-asserted-by":"crossref","unstructured":"Penzel, T., Moody, G.B., Mark, R.G., Goldberger, A.L., Peter, J.H.: The apnea-ECG database. In: Computers in Cardiology 2000 (Cat. 00CH37163), vol. 27, pp. 255\u2013258. IEEE (2000)","DOI":"10.1109\/CIC.2000.898505"},{"issue":"2","key":"39_CR22","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1046\/j.1440-1819.1999.00527.x","volume":"53","author":"Y Ichimaru","year":"1999","unstructured":"Ichimaru, Y., Moody, G.B.: Development of the polysomnographic database on CD-ROM. Psychiatry Clin. Neurosci. 53(2), 175\u2013177 (1999)","journal-title":"Psychiatry Clin. Neurosci."},{"key":"39_CR23","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.neucom.2018.03.011","volume":"294","author":"K Li","year":"2018","unstructured":"Li, K., Pan, W., Li, Y., Jiang, Q., Liu, G.: A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal. Neurocomputing 294, 94\u2013101 (2018)","journal-title":"Neurocomputing"},{"issue":"04","key":"39_CR24","doi-asserted-by":"publisher","first-page":"1950026","DOI":"10.1142\/S021951941950026X","volume":"19","author":"SA Singh","year":"2019","unstructured":"Singh, S.A., Majumder, S.: A novel approach OSA detection using single-lead ECG scalogram based on deep neural network. J. Mech. Med. Biol. 19(04), 1950026 (2019)","journal-title":"J. Mech. Med. Biol."},{"key":"39_CR25","doi-asserted-by":"publisher","first-page":"e7731","DOI":"10.7717\/peerj.7731","volume":"7","author":"T Wang","year":"2019","unstructured":"Wang, T., Changhua, L., Shen, G., Hong, F.: Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified lenet-5 convolutional neural network. PeerJ 7, e7731 (2019)","journal-title":"PeerJ"},{"issue":"15","key":"39_CR26","doi-asserted-by":"publisher","first-page":"4157","DOI":"10.3390\/s20154157","volume":"20","author":"H-Y Chang","year":"2020","unstructured":"Chang, H.-Y., Yeh, C.-Y., Lee, C.-T., Lin, C.-C.: A sleep apnea detection system based on a one-dimensional deep convolution neural network model using single-lead electrocardiogram. Sensors 20(15), 4157 (2020)","journal-title":"Sensors"},{"key":"39_CR27","first-page":"1","volume":"71","author":"M Bahrami","year":"2022","unstructured":"Bahrami, M., Forouzanfar, M.: Sleep apnea detection from single-lead ECG: a comprehensive analysis of machine learning and deep learning algorithms. IEEE Trans. Instrum. Meas. 71, 1\u201311 (2022)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"39_CR28","doi-asserted-by":"publisher","first-page":"104401","DOI":"10.1016\/j.bspc.2022.104401","volume":"80","author":"PK Tyagi","year":"2023","unstructured":"Tyagi, P.K., Agrawal, D.: Automatic detection of sleep apnea from single-lead ECG signal using enhanced-deep belief network model. Biomed. Signal Process. Control 80, 104401 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"39_CR29","doi-asserted-by":"publisher","first-page":"104581","DOI":"10.1016\/j.bspc.2023.104581","volume":"82","author":"H Liu","year":"2023","unstructured":"Liu, H., Cui, S., Zhao, X., Cong, F.: Detection of obstructive sleep apnea from single-channel ECG signals using a CNN-transformer architecture. Biomed. Signal Process. Control 82, 104581 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"39_CR30","doi-asserted-by":"publisher","first-page":"102928","DOI":"10.1016\/j.bspc.2021.102928","volume":"69","author":"Y Taghizadegan","year":"2021","unstructured":"Taghizadegan, Y., Dabanloo, N.J., Maghooli, K., Sheikhani, A.: Obstructive sleep apnea event prediction using recurrence plots and convolutional neural networks (RP-CNNs) from polysomnographic signals. Biomed. Signal Process. Control 69, 102928 (2021)","journal-title":"Biomed. Signal Process. Control"}],"container-title":["Lecture Notes in Electrical Engineering","Proceedings of 2024 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-3863-5_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T09:08:55Z","timestamp":1757149735000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-3863-5_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819638628","9789819638635"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-3863-5_39","relation":{},"ISSN":["1876-1100","1876-1119"],"issn-type":[{"value":"1876-1100","type":"print"},{"value":"1876-1119","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"4 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Imaging and Computer-Aided Diagnosis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Manchester","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micad2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.micad.org\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}