## 5 Conclusions  
We conducted an ensemble deep neural network-based person identification using ECG signals collected on different days. ECG signals capture minute electrical impulses produced during the cardiac cycle and serve as biometric signals during human activity. These signals are susceptible to various types of noise depending on the data acquisition conditions, including sensor placement and the subject's physiological and mental state, resulting in diverse signal shapes. During a single session of data acquisition, the environmental variables remained relatively constant, leading to consistent ECG signals. However, across multiple sessions, environmental changes can alter the signal morphology, posing challenges for ECG-based person identification over different days. To address this issue, this study proposed an ensemble deep neural network that evaluates and compares ECG recognition performance under diverse conditions. The ensemble deep neural network comprised three streams that incorporate two established pretrained models. Each network stream takes the time-frequency representation of ECG signals as input, with one stream reusing the same network architecture under varied learning conditions, including with and without data augmentation. The ensemble included ResNet-101 and Inception-ResNet-v2 as stream members. Experimental validation was conducted using the PTB dataset, demonstrating that the proposed ensemble deep neural network resulted in a 3.39% performance enhancement in accuracy compared to existing methodologies. This approach, while potentially increasing model stability by learning various features independently, requires more computing and time resources. Although the recognition rate was low when using ECG data from different days for validation, including data from multiple sessions in the training set is expected to improve the recognition rate. In future research, we will explore the robust features extracted from ECG signals acquired on different days and investigate ensemble methods to integrate these features more effectively.  
Conceptualization, Y.-H.B. and K.-C.K.; Methodology, Y.-H.B. and K.-C.K.; Software, Y.-H.B. and K.-C.K.; Validation, Y.-H.B. and K.-C.K.; Formal Analysis, Y.-H.B. and K.-C.K.; Investigation, Y.-H.B. and K.-C.K.; Resources, K.-C.K.; Data Curation, K.-C.K.; Writing--Original Draft Preparation, Y.-H.B.; Writing--Review and Editing, K.-C.K.; Visualization, Y.-H.B. and K.-C.K.; Supervision, K.-C.K.; Project Administration, K.-C.K.; Funding Acquisition, K.-C.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2017R1A6A1A03015496).  
Not applicable.  
Data Availability Statement: Data are available in a publicly accessible repository. The data presented in this study are openly available in PhysioNet at [[https://www.physionet.org/content/ptbdb/1.0.0/](https://www.physionet.org/content/ptbdb/1.0.0/)]([https://www.physionet.org/content/ptbdb/1.0.0/](https://www.physionet.org/content/ptbdb/1.0.0/)) (accessed on 10 June 2024), reference number [48].  
The authors declare no conflicts of interest.