###### Abstract  
Electrocardiogram (ECG) signals are a measure minute electrical signals generated during the cardiac cycle, a biometric signal that occurs during vital human activity. ECG signals are susceptible to various types of noise depending on the data acquisition conditions, with factors such as sensor placement and the physiological and mental states of the subject contributing to the diverse shapes of these signals. When the data are acquired in a single session, the environmental variables are relatively similar, resulting in similar ECG signals; however, in subsequent sessions, even for the same person, changes in the environmental variables can alter the signal shape. This phenomenon poses challenges for person identification using ECG signals acquired on different days. To improve the performance of individual identification, even when ECG data is acquired on different days, this paper proposes an ensemble deep neural network for person identification by comparing and analyzing the ECG recognition performance under various conditions. The proposed ensemble deep neural network comprises three streams that incorporate two well-known pretrained models. Each network receives the time-frequency representation of ECG signals as input, and a stream reuses the same network structure under different learning conditions with or without data augmentation. The proposed ensemble deep neural network was validated on the Physikalisch-Technische Bundesansaltalt dataset, and the results confirmed a 3.39% improvement in accuracy compared to existing methods. convolutional neural network; individual identification; electrocardiogram; pretrained neural network; wavelet transform +
Footnote †: journal: Medical  
based on the principle of capacitive coupling. However, since ECG signals measure the tiny voltage differences generated by the heart, even non-contact methods are limited to distances of a few millimeters from the skin. Various simplified ECG measurement methods have been developed, such as measuring ECGs by simply touching the wearable watch with the opposite finger while wearing the watch [9,10]. However, ECG signals can be easily distorted by factors such as subject movement during measurement, inevitable breathing, muscle relaxation state, patch placement, and sensor power, among others. To capture high-quality ECG signals, subjects should minimize their movement and remain in a relaxed muscle state. The accurate analysis of ECG data requires expertise in biophysics, and there may be limitations in fully processing the data using computer algorithms alone. As ECG signals contain personal medical information, adherence to data protection regulations is essential. ECG-based identification systems are prone to overfitting the subset of data used in training, making them difficult to generalize to newly collected data [11,12]. Obtaining new ECG signals implies that the electrode patch placement, physical condition, and movements may differ from previous acquisitions, potentially resulting in varying ECG signal shapes even for the same individual. To acquire new ECG signals effectively, it is necessary to do so in a similar environment to ensure stable individual identification based on obtaining similar signals [13, 14, 15, 16]. However, achieving consistent patch placement, physical conditions, and movements during each data acquisition attempt is difficult. Different individuals may apply the patches differently, and accurately locating the exact position can be challenging even for the same person. Therefore, using data collected across multiple sessions for training purposes can aid generalization [17, 18, 19, 20, 21]. However, such data collection requires additional time and effort [22, 23, 24]. The differences in ECG signals, owing to their acquisition in different sessions, pose challenges for neural networks to accurately classify signals.