{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:21:48Z","timestamp":1742912508416,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031063671"},{"type":"electronic","value":"9783031063688"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-06368-8_21","type":"book-chapter","created":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T09:47:09Z","timestamp":1654508829000},"page":"316-328","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Two-Stream Model Combining ResNet and Bi-LSTM Networks for Non-contact Dynamic Electrocardiogram Signal Quality Assessment"],"prefix":"10.1007","author":[{"given":"Guoqiang","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yonglin","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Zhikun","family":"Lie","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,7]]},"reference":[{"issue":"9","key":"21_CR1","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1088\/0967-3334\/33\/9\/1479","volume":"33","author":"L Johannesen","year":"2012","unstructured":"Johannesen, L., Galeotti, L.: Automatic ECG quality scoring methodology: mimicking human annotators. Physiol. Meas. 33(9), 1479 (2012). https:\/\/doi.org\/10.1088\/0967-3334\/33\/9\/1479","journal-title":"Physiol. Meas."},{"issue":"9","key":"21_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1088\/0967-3334\/33\/9\/e01","volume":"33","author":"GD Clifford","year":"2012","unstructured":"Clifford, G.D., Moody, G.B.: Signal quality in cardiorespiratory monitoring. Physiol. Meas. 33(9), 1\u20136 (2012). https:\/\/doi.org\/10.1088\/0967-3334\/33\/9\/e01","journal-title":"Physiol. Meas."},{"issue":"9","key":"21_CR3","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1088\/0967-3334\/33\/9\/1463","volume":"33","author":"I Jekova","year":"2012","unstructured":"Jekova, I., Krasteva, V., Christov, I., Ab\u00e4cherli, R.: Threshold-based system for noise detection in multilead ECG recordings. Physiol. Meas. 33(9), 1463\u20131477 (2012). https:\/\/doi.org\/10.1088\/0967-3334\/33\/9\/1463","journal-title":"Physiol. Meas."},{"key":"21_CR4","unstructured":"Liu, C., Li, P., Zhao, L., Liu, F., Wang, R.: Real-time signal quality assessment for ECGs collected using mobile phones. In: Proceedings of the 2011 Computing in Cardiology, Hangzhou, China, pp. 357\u2013360. IEEE (2011)"},{"issue":"4","key":"21_CR5","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1088\/0967-3334\/17\/4\/002","volume":"17","author":"J Allen","year":"1996","unstructured":"Allen, J., Murray, A.: Assessing ECG signal quality on a coronary care unit. Physiol. Meas. 17(4), 249\u2013258 (1996)","journal-title":"Physiol. Meas."},{"issue":"9","key":"21_CR6","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1088\/0967-3334\/33\/9\/1419","volume":"33","author":"GD Clifford","year":"2012","unstructured":"Clifford, G.D., Behar, J., Li, Q., Rezek, I.: Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms. Physiol. Meas. 33(9), 1419\u20131433 (2012). https:\/\/doi.org\/10.1088\/0967-3334\/33\/9\/1419","journal-title":"Physiol. Meas."},{"key":"21_CR7","unstructured":"Silva, I., Moody, G.B., Celi, L.: Improving the quality of ECGs collected using mobile phones: the physionet\/computing in cardiology challenge 2011. In: Proceedings of the 2011 Computing in Cardiology, Hangzhou, China, pp. 273\u2013276. IEEE (2011)"},{"issue":"9","key":"21_CR8","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1088\/0967-3334\/33\/9\/1535","volume":"33","author":"H Xia","year":"2012","unstructured":"Xia, H., Garcia, G.A., Bains, J., Wortham, D.C., Zhao, X.: Matrix of regularity for improving the quality of ECGs. Physiol. Meas. 33(9), 1535\u20131548 (2012). https:\/\/doi.org\/10.1088\/0967-3334\/33\/9\/1535","journal-title":"Physiol. Meas."},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Shi, Y., et al.: Robust assessment of ECG signal quality for wearable devices. In: Proceedings of the IEEE International Conference on Healthcare Informatics (ICHI), Xi\u2019an, China, pp. 1\u20133 (2019)","DOI":"10.1109\/ICHI.2019.8904810"},{"issue":"2","key":"21_CR10","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1049\/htl.2015.0062","volume":"3","author":"U Satija","year":"2016","unstructured":"Satija, U., Ramkumar, B., Manikandan, M.S.: Robust cardiac event change detection method for long-term healthcare monitoring applications. Healthc. Technol. Lett. 3(2), 116\u2013123 (2016)","journal-title":"Healthc. Technol. Lett."},{"issue":"3","key":"21_CR11","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1007\/s40846-018-0411-0","volume":"39","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Wei, S., Zhang, L., Liu, C.: Comparing the performance of random forest, SVM and their variants for computational and mathematical methods in medicine 11 ECG quality assessment combined with nonlinear features. J. Med. Biol. Eng. 39(3), 381\u2013392 (2019)","journal-title":"J. Med. Biol. Eng."},{"issue":"7553","key":"21_CR12","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"issue":"9","key":"21_CR13","doi-asserted-by":"publisher","first-page":"2352","DOI":"10.1162\/neco_a_00990","volume":"29","author":"W Rawat","year":"2017","unstructured":"Rawat, W., Wang, Z.: Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput. 29(9), 2352\u20132449 (2017)","journal-title":"Neural Comput."},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Xiong, W., Wu, L., Alleva, F., Droppo, J., Huang, X., Stolcke, A.: The Microsoft 2017 conversational speech recognition system. In: Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada (2018)","DOI":"10.1109\/ICASSP.2018.8461870"},{"issue":"1\u20132","key":"21_CR15","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10590-017-9194-2","volume":"31","author":"F Hill","year":"2017","unstructured":"Hill, F., Cho, K., Jean, S., Bengio, Y.: The representational geometry of word meanings acquired by neural machine translation models. Mach. Transl. 31(1\u20132), 3\u201318 (2017). https:\/\/doi.org\/10.1007\/s10590-017-9194-2","journal-title":"Mach. Transl."},{"issue":"1","key":"21_CR16","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/JSEN.2020.3012697","volume":"21","author":"S Peng","year":"2020","unstructured":"Peng, S., et al.: Flexible electrodes based smart mattress for monitoring physiological signals of heart and autonomic nerves in a non-contact way. IEEE Sensors J. 21(1), 6\u201315 (2020)","journal-title":"IEEE Sensors J."},{"key":"21_CR17","unstructured":"Nawab, S.H., Quatieri, T.F.: Short-time Fourier transform. In: Advanced Topics in Signal Processing, pp. 289\u2013337, Prentice-Hall, Upper Saddle River (1987)"},{"issue":"7","key":"21_CR18","doi-asserted-by":"publisher","first-page":"3244","DOI":"10.1109\/TII.2018.2799928","volume":"14","author":"Z Liu","year":"2018","unstructured":"Liu, Z., Wu, Z., Li, T., Li, J., Shen, C.: GMM and CNN hybrid method for short utterance speaker recognition. IEEE Trans. Industr. Inform. 14(7), 3244\u20133252 (2018)","journal-title":"IEEE Trans. Industr. Inform."},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, H., McLoughlin, I., Song, Y.: Robust sound event recognition using convolutional neural networks. In: Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 559\u2013563, South Brisbane, Australia (2015)","DOI":"10.1109\/ICASSP.2015.7178031"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Fu, S.W., Tsao, Y., Lu, X.: SNR-aware convolutional neural network modeling for speech enhancement. In: Proceedings of the Interspeech 2016, San Francisco, CA, pp. 3768\u20133772 (2016)","DOI":"10.21437\/Interspeech.2016-211"},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Satt, A., Rozenberg, S., Hoory, R.: Efficient emotion recognition from speech using deep learning on spectrograms. In: Proceedings of the Interspeech 2017, Stockholm, Sweden, pp. 1089\u20131093 (2017)","DOI":"10.21437\/Interspeech.2017-200"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"21_CR23","unstructured":"Cui, Z., Ke, R., Pu, Z., Wang, Y.: Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. CoRR arXiv preprint arXiv:1801.02143 (2018)"},{"key":"21_CR24","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"21_CR25","doi-asserted-by":"publisher","unstructured":"Zhou, X., Zhu, X., Nakamura, K., Mahito, N.: ECG quality assessment using 1D-convolutional neural network. In: 2018 14th IEEE International Conference on Signal Processing (ICSP), pp. 780\u2013784, IEEE (2018). https:\/\/doi.org\/10.1109\/ICSP.2018.8652479","DOI":"10.1109\/ICSP.2018.8652479"},{"key":"21_CR26","doi-asserted-by":"publisher","unstructured":"Huerta, A., Mart\u00ednez-Rodrigo, A., Gonz\u00e1lez, V.B., Quesada, A., Rieta, J.J., Alcaraz, R.: Quality assessment of very long-term ecg recordings using a convolutional neural network. In: 2019 E-Health and Bioengineering Conference (EHB), pp. 1\u20134 (2019). https:\/\/doi.org\/10.1109\/EHB47216.2019.8970077","DOI":"10.1109\/EHB47216.2019.8970077"},{"key":"21_CR27","doi-asserted-by":"publisher","unstructured":"Zhu, Z., Liu, W., Yao, Y., Chen, X., Sun, Y., Xu, L.: Adaboost based ECG signal quality evaluation. In: 2019 Computing in Cardiology (CinC), pp. 1\u20134 (2019). https:\/\/doi.org\/10.23919\/CinC49843.2019.9005515","DOI":"10.23919\/CinC49843.2019.9005515"},{"key":"21_CR28","first-page":"7095137","volume":"2019","author":"Q Zhang","year":"2019","unstructured":"Zhang, Q., Fu, L., Gu, L.: A cascaded convolutional neural network for assessing signal quality of dynamic ECG. Comput. Math. Methods Med. 2019, 7095137 (2019)","journal-title":"Comput. Math. Methods Med."}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Wireless Mobile Communication and Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06368-8_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T09:56:51Z","timestamp":1654509411000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06368-8_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031063671","9783031063688"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06368-8_21","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"7 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MobiHealth","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Wireless Mobile Communication and Healthcare","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mobihealth2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy+","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}