{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T06:21:30Z","timestamp":1771050090651,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T00:00:00Z","timestamp":1679270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MSIT (Ministry of Science and ICT)","award":["IITP-2022-RS-2022-00156345"],"award-info":[{"award-number":["IITP-2022-RS-2022-00156345"]}]},{"name":"MSIT (Ministry of Science and ICT)","award":["2021-0-02067"],"award-info":[{"award-number":["2021-0-02067"]}]},{"name":"MSIT (Ministry of Science and ICT)","award":["2020R1C1C1008068"],"award-info":[{"award-number":["2020R1C1C1008068"]}]},{"name":"Next Generation AI for Multi-purpose Video Search","award":["IITP-2022-RS-2022-00156345"],"award-info":[{"award-number":["IITP-2022-RS-2022-00156345"]}]},{"name":"Next Generation AI for Multi-purpose Video Search","award":["2021-0-02067"],"award-info":[{"award-number":["2021-0-02067"]}]},{"name":"Next Generation AI for Multi-purpose Video Search","award":["2020R1C1C1008068"],"award-info":[{"award-number":["2020R1C1C1008068"]}]},{"DOI":"10.13039\/501100014188","name":"Korean government (MSIT)","doi-asserted-by":"publisher","award":["IITP-2022-RS-2022-00156345"],"award-info":[{"award-number":["IITP-2022-RS-2022-00156345"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Korean government (MSIT)","doi-asserted-by":"publisher","award":["2021-0-02067"],"award-info":[{"award-number":["2021-0-02067"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Korean government (MSIT)","doi-asserted-by":"publisher","award":["2020R1C1C1008068"],"award-info":[{"award-number":["2020R1C1C1008068"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study proposes an electrocardiogram (ECG) signal stitching scheme to detect arrhythmias in drivers during driving. When the ECG is measured through the steering wheel during driving, the data are always exposed to noise caused by vehicle vibrations, bumpy road conditions, and the driver\u2019s steering wheel gripping force. The proposed scheme extracts stable ECG signals and transforms them into full 10 s ECG signals to classify arrhythmias using convolutional neural networks (CNN). Before the ECG stitching algorithm is applied, data preprocessing is performed. To extract the cycle from the collected ECG data, the R peaks are found and the TP interval segmentation is applied. An abnormal P peak is very difficult to find. Therefore, this study also introduces a P peak estimation method. Finally, 4 \u00d7 2.5 s ECG segments are collected. To classify arrhythmias with stitched ECG data, each time series\u2019 ECG signal is transformed via the continuous wavelet transform (CWT) and short-time Fourier transform (STFT), and transfer learning is performed for classification using CNNs. Finally, the parameters of the networks that provide the best performance are investigated. According to the classification accuracy, GoogleNet with the CWT image set shows the best results. The classification accuracy is 82.39% for the stitched ECG data, while it is 88.99% for the original ECG data.<\/jats:p>","DOI":"10.3390\/s23063257","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T03:30:31Z","timestamp":1679283031000},"page":"3257","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["An ECG Stitching Scheme for Driver Arrhythmia Classification Based on Deep Learning"],"prefix":"10.3390","volume":"23","author":[{"given":"Do Hoon","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5776-0156","authenticated-orcid":false,"given":"Gwangjin","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seong Han","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1093\/eurheartj\/ehq278","article-title":"Guidelines for the management of atrial fibrillation The Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC)","volume":"31","author":"Camm","year":"2010","journal-title":"Eur. 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