This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ech T Press Science Computers, Materials & Continua DOI:10.32604/cmc.2022.021154 Article Convolutional Neural Network-Based Identity Recognition Using ECG at Different Water Temperatures During Bathing Jianbo Xu and Wenxi Chen * Biomedical Information Engineering Laboratory, Graduate School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, 965-8580, Japan *Corresponding Author: Wenxi Chen. Email: wenxi@u-aizu.ac.jp Received: 25 June 2021; Accepted: 13 August 2021 Abstract: This study proposes a convolutional neural network (CNN)-based identity recognition scheme using electrocardiogram (ECG) at different water temperatures (WTs) during bathing, aiming to explore the impact of ECG length on the recognition rate. ECG data was collected using non-contact electrodes at five different WTs during bathing. Ten young student subjects (seven men and three women) participated in data collection. Three ECG recordings were collected at each preset bathtub WT for each subject. Each recording is 18 min long, with a sampling rate of 200 Hz. In total, 150 ECG recordings and 150 WT recordings were collected. The R peaks were detected based on the processed ECG (baseline wandering eliminated, 50-Hz hum removed, ECG smoothing and ECG normalization) and the QRS complex waves were segmented. These segmented waves were then transformed into binary images, which served as the datasets. For each subject, the training, validation, and test data were taken from the first, second, and third ECG recordings, respectively. The number of training and validation images was 84297 and 83734, respectively. In the test stage, the preliminary classification results were obtained using the trained CNN model, and the finer classifica- tion results were determined using the majority vote method based on the preliminary results. The validation rate was 98.71%. The recognition rates were 95.00% and 98.00% when the number of test heartbeats was 7 and 17, respectively, for each subject. Keywords: Electrocardiogram; QRS; recognition rate; water temperatures; convolutional neural network; majority vote 1 Introduction With improvements in living standards, people have started to pay more attention to personal hygiene and health in daily life, and bathing has become increasingly popular. However, the number of drowning accidents while bathing has increased in recent years, with survey data showing more than 5,398 such accidents in Japan in 2018 [1]. Victims of fatal drowning accidents tend to die in the bathtub because they cannot be rescued in time. Also, some survivors suffer long-term psychological