{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T05:47:30Z","timestamp":1745300850064,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811557873"},{"type":"electronic","value":"9789811557880"}],"license":[{"start":{"date-parts":[[2020,9,9]],"date-time":"2020-09-09T00:00:00Z","timestamp":1599609600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,9]],"date-time":"2020-09-09T00:00:00Z","timestamp":1599609600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-15-5788-0_71","type":"book-chapter","created":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T08:02:45Z","timestamp":1599552165000},"page":"755-763","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Performance Improvement of Deep Residual Skip Convolution Neural Network for Atrial Fibrillation Classification"],"prefix":"10.1007","author":[{"family":"Sanjana K.","sequence":"first","affiliation":[]},{"given":"V.","family":"Sowmya","sequence":"additional","affiliation":[]},{"given":"E. A.","family":"Gopalakrishnan","sequence":"additional","affiliation":[]},{"given":"K. P.","family":"Soman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,9]]},"reference":[{"key":"71_CR1","unstructured":"Andreotti, F., Carr, O., Pimentel, M.A.F., Mahdi, A., De Vos, M.: Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG. Comput. Cardiol. (CinC) Rennes 2017, 1\u20134 (2017)"},{"key":"71_CR2","unstructured":"Datta, S., et al.: Identifying normal, AF and other abnormal ECG rhythms using a cascaded binary classifier. Comput. Cardiol. (CinC) Rennes 2017, 1\u20134 (2017)"},{"key":"71_CR3","doi-asserted-by":"crossref","unstructured":"Ganesan, A.N., et al.: Long-term outcomes of catheter ablation of atrial fibrillation: a systematic review and meta-analysis. J. Am. Heart Assoc. 2(2), e004549 (2013)","DOI":"10.1161\/JAHA.112.004549"},{"key":"71_CR4","doi-asserted-by":"crossref","unstructured":"Go, A.S., et al.: Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the Anticoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. JAMA 285(18), 2370\u20132375 (2001)","DOI":"10.1001\/jama.285.18.2370"},{"key":"71_CR5","doi-asserted-by":"crossref","unstructured":"Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.Ch., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circ. 101(23), e215\u2013e220 (2003)","DOI":"10.1161\/01.CIR.101.23.e215"},{"key":"71_CR6","doi-asserted-by":"crossref","unstructured":"Gopika, P., et al.: Performance improvement of residual skip convolutional neural network for myocardial disease classification. In: International Conference on Intelligent Computing and Communication Technologies. Springer, Singapore (2019)","DOI":"10.1007\/978-981-13-8461-5_25"},{"key":"71_CR7","doi-asserted-by":"crossref","unstructured":"Gopika, P., Sowmya, V., et al.: Transferable approach for cardiac disease classification using deep learning. Deep Learn. Biomed. Health Inform. (BHI) (2019, in press)","DOI":"10.1016\/B978-0-12-819061-6.00012-4"},{"key":"71_CR8","doi-asserted-by":"crossref","unstructured":"Hong, S., et al.: ENCASE: an ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks. Comput. Cardiol. (CinC) Rennes 2017, 1\u20134 (2017)","DOI":"10.22489\/CinC.2017.178-245"},{"key":"71_CR9","doi-asserted-by":"crossref","unstructured":"Kachuee, M., Fazeli, S., Sarrafzadeh, M.: ECG heartbeat classification: a deep transferable representation. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI), New York, NY, pp. 443\u2013444 (2018)","DOI":"10.1109\/ICHI.2018.00092"},{"key":"71_CR10","doi-asserted-by":"crossref","unstructured":"Kamaleswaran, R., Mahajan, R., Akbilgic, O.: A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length. Physiol. Meas. 39(3), 035006 (2018)","DOI":"10.1088\/1361-6579\/aaaa9d"},{"key":"71_CR11","doi-asserted-by":"crossref","unstructured":"Kropf, M., et al.: Cardiac anomaly detection based on time and frequency domain features using tree-based classifiers. Physiol. Meas. 39(11), 114001 (2018)","DOI":"10.1088\/1361-6579\/aae13e"},{"key":"71_CR12","doi-asserted-by":"crossref","unstructured":"McManus, D.D., et al.: A novel application for the detection of an irregular pulse using an iPhone 4S in patients with atrial fibrillation. Heart Rhythm 10(3), 315\u2013319 (2013)","DOI":"10.1016\/j.hrthm.2012.12.001"},{"key":"71_CR13","doi-asserted-by":"crossref","unstructured":"Plesinger, F., et al.: Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG. Physiol. Meas. 39(9), 094002 (2018)","DOI":"10.1088\/1361-6579\/aad9ee"},{"issue":"12","key":"71_CR14","doi-asserted-by":"publisher","first-page":"124007","DOI":"10.1088\/1361-6579\/aaf35b","volume":"39","author":"Muhammed Rizwan","year":"2018","unstructured":"Rizwan, Muhammed, Whitaker, Bradley M., Anderson, David V.: AF detection from ECG recordings using feature selection, sparse coding, and ensemble learning. Physiol. Meas. 39(12), 124007 (2018)","journal-title":"Physiol. Meas."},{"key":"71_CR15","doi-asserted-by":"crossref","unstructured":"Shaffer, F., Ginsberg, J.P.: An overview of heart rate variability metrics and norms. Front. Public Health 5, 258 (2017)","DOI":"10.3389\/fpubh.2017.00258"},{"key":"71_CR16","doi-asserted-by":"crossref","unstructured":"Sujadevi, V.G., Soman, K.P., Vinayakumar, R.: Real-time detection of atrial fibrillation from short time single lead ECG traces using recurrent neural networks. Intelligent Systems Technologies and Applications, pp. 212\u2013221. Springer, Cham (2018)","DOI":"10.1007\/978-3-319-68385-0_18"},{"key":"71_CR17","unstructured":"Teijeiro, T., Garca, C.A., Castro, D., Flix, P.: Arrhythmia classification from the abductive interpretation of short single-lead ECG records. Comput. Cardiol. (CinC) Rennes 2017, 1\u20134 (2017)"},{"key":"71_CR18","unstructured":"Zabihi, M., Rad, A.B., Katsaggelos, A.K., Kiranyaz, S., Narkilahti, S., Gabbouj, M.: Detection of atrial fibrillation in ECG hand-held devices using a random forest classifier. Comput. Cardiol. (CinC) Rennes 2017, 1\u20134 (2017)"}],"container-title":["Advances in Intelligent Systems and Computing","Evolution in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-5788-0_71","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T08:16:24Z","timestamp":1599552984000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-5788-0_71"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,9]]},"ISBN":["9789811557873","9789811557880"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-5788-0_71","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2020,9,9]]},"assertion":[{"value":"9 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}