{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T20:01:48Z","timestamp":1780776108739,"version":"3.54.1"},"reference-count":39,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,4,10]],"date-time":"2020-04-10T00:00:00Z","timestamp":1586476800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The automatic detection of atrial fibrillation (AF) is crucial for its association with the risk of embolic stroke. Most of the existing AF detection methods usually convert 1D time-series electrocardiogram (ECG) signal into 2D spectrogram to train a complex AF detection system, which results in heavy training computation and high implementation cost. This paper proposes an AF detection method based on an end-to-end 1D convolutional neural network (CNN) architecture to raise the detection accuracy and reduce network complexity. By investigating the impact of major components of a convolutional block on detection accuracy and using grid search to obtain optimal hyperparameters of the CNN, we develop a simple, yet effective 1D CNN. Since the dataset provided by PhysioNet Challenge 2017 contains ECG recordings with different lengths, we also propose a length normalization algorithm to generate equal-length records to meet the requirement of CNN. Experimental results and analysis indicate that our method of 1D CNN achieves an average F1 score of 78.2%, which has better detection accuracy with lower network complexity, as compared with the existing deep learning-based methods.<\/jats:p>","DOI":"10.3390\/s20072136","type":"journal-article","created":{"date-parts":[[2020,4,13]],"date-time":"2020-04-13T10:41:52Z","timestamp":1586774512000},"page":"2136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":106,"title":["Detection of Atrial Fibrillation Using 1D Convolutional Neural Network"],"prefix":"10.3390","volume":"20","author":[{"given":"Chaur-Heh","family":"Hsieh","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, Yango University, Fuzhou 350015, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan-Shuo","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer and Communication Engineering, Ming Chuan University, Taoyuan 333, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bor-Jiunn","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Computer and Communication Engineering, Ming Chuan University, Taoyuan 333, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ching-Hua","family":"Hsiao","sequence":"additional","affiliation":[{"name":"Department of Computer and Communication Engineering, Ming Chuan University, Taoyuan 333, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,10]]},"reference":[{"key":"ref_1","unstructured":"(2019, October 18). 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