Semi-Supervised Domain Adaptation for Individual Identification from Electrocardiogram Signals
When acquiring electrocardiogram (ECG) signals, the placement of electrode patches is crucial for acquiring electrocardiographic signals. Constant displacement positions are essential for ensuring the consistency of the ECG signal when used for individual identification. However, achieving constant...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/24/13259 |
| _version_ | 1797382076171812864 |
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| author | Yeong-Hyeon Byeon Keun-Chang Kwak |
| author_facet | Yeong-Hyeon Byeon Keun-Chang Kwak |
| author_sort | Yeong-Hyeon Byeon |
| collection | DOAJ |
| description | When acquiring electrocardiogram (ECG) signals, the placement of electrode patches is crucial for acquiring electrocardiographic signals. Constant displacement positions are essential for ensuring the consistency of the ECG signal when used for individual identification. However, achieving constant placement of ECG electrode patches in every trial for data acquisition is challenging. This is because different individuals may attach patches, and even when the same person attaches them, it may be difficult to specify the exact position. Therefore, gathering ECG data from various locations is necessary. However, this process requires a substantial amount of labor and time, owing to the requirement for multiple attempts. Nonetheless, persisting with these efforts enables the endurance of some ECG differences. To reduce labor and time, we propose a semi-supervised domain adaptation for individual identification using ECG signals. The method operates with a full set of original ECG signals and a small set of ECG signals from different placements to account for the differences between the signals in the generative adversarial network (CycleGAN). Specifically, to train the CycleGAN, the ECG signals were transformed into time–frequency representations, and the trained generator was used to generate ECG signals to expand the small set of ECG signals from different placements. Subsequently, both the original and generated signals were used to train the classifier for individual identification. This scenario can also be applied to the classification of ECG signals from different sensors. The PTB-ECG dataset was used for this experiment. We found that the proposed method showed higher accuracy than when only the original ECG signals were used for the training classifier. |
| first_indexed | 2024-03-08T21:01:14Z |
| format | Article |
| id | doaj.art-0afeb187410f4aeb885ed0c63595dcd9 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T21:01:14Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0afeb187410f4aeb885ed0c63595dcd92023-12-22T13:52:02ZengMDPI AGApplied Sciences2076-34172023-12-0113241325910.3390/app132413259Semi-Supervised Domain Adaptation for Individual Identification from Electrocardiogram SignalsYeong-Hyeon Byeon0Keun-Chang Kwak1Interdisciplinary Program in IT-Bio Convergence System, Department of Electronics Engineering, Chosun University, Gwangju 61452, Republic of KoreaInterdisciplinary Program in IT-Bio Convergence System, Department of Electronics Engineering, Chosun University, Gwangju 61452, Republic of KoreaWhen acquiring electrocardiogram (ECG) signals, the placement of electrode patches is crucial for acquiring electrocardiographic signals. Constant displacement positions are essential for ensuring the consistency of the ECG signal when used for individual identification. However, achieving constant placement of ECG electrode patches in every trial for data acquisition is challenging. This is because different individuals may attach patches, and even when the same person attaches them, it may be difficult to specify the exact position. Therefore, gathering ECG data from various locations is necessary. However, this process requires a substantial amount of labor and time, owing to the requirement for multiple attempts. Nonetheless, persisting with these efforts enables the endurance of some ECG differences. To reduce labor and time, we propose a semi-supervised domain adaptation for individual identification using ECG signals. The method operates with a full set of original ECG signals and a small set of ECG signals from different placements to account for the differences between the signals in the generative adversarial network (CycleGAN). Specifically, to train the CycleGAN, the ECG signals were transformed into time–frequency representations, and the trained generator was used to generate ECG signals to expand the small set of ECG signals from different placements. Subsequently, both the original and generated signals were used to train the classifier for individual identification. This scenario can also be applied to the classification of ECG signals from different sensors. The PTB-ECG dataset was used for this experiment. We found that the proposed method showed higher accuracy than when only the original ECG signals were used for the training classifier.https://www.mdpi.com/2076-3417/13/24/13259individual identificationdomain adaptationconvolutional neural networkelectrocardiogramgenerative adversarial network |
| spellingShingle | Yeong-Hyeon Byeon Keun-Chang Kwak Semi-Supervised Domain Adaptation for Individual Identification from Electrocardiogram Signals Applied Sciences individual identification domain adaptation convolutional neural network electrocardiogram generative adversarial network |
| title | Semi-Supervised Domain Adaptation for Individual Identification from Electrocardiogram Signals |
| title_full | Semi-Supervised Domain Adaptation for Individual Identification from Electrocardiogram Signals |
| title_fullStr | Semi-Supervised Domain Adaptation for Individual Identification from Electrocardiogram Signals |
| title_full_unstemmed | Semi-Supervised Domain Adaptation for Individual Identification from Electrocardiogram Signals |
| title_short | Semi-Supervised Domain Adaptation for Individual Identification from Electrocardiogram Signals |
| title_sort | semi supervised domain adaptation for individual identification from electrocardiogram signals |
| topic | individual identification domain adaptation convolutional neural network electrocardiogram generative adversarial network |
| url | https://www.mdpi.com/2076-3417/13/24/13259 |
| work_keys_str_mv | AT yeonghyeonbyeon semisuperviseddomainadaptationforindividualidentificationfromelectrocardiogramsignals AT keunchangkwak semisuperviseddomainadaptationforindividualidentificationfromelectrocardiogramsignals |