{
    "abstract": "Designing an ECG sensor circuit requires a comprehensive approach to detect, amplify, filter, and condition the weak electrical signals produced by the heart. To evaluate sensor performance under realistic conditions, diverse ECG signals with embedded watermarks are generated, enabling an assessment of how effectively the sensor and its signal-conditioning circuitry handle these modified signals. A Variational Autoencoder (VAE) framework is employed to generate the watermarked ECG signals, addressing critical concerns in the digital era, such as data security, authenticity, and copyright protection. Three watermarking strategies are examined in this study: embedding watermarks in the mean (\u03bc) of the VAE's latent space, embedding them through the latent variable (z), and using post-reconstruction watermarking in the frequency domain. Experimental results demonstrate that watermarking applied through the mean (\u03bc) and in the frequency domain achieves a low Mean Squared Error (MSE) while maintaining stable signal fidelity across varying watermark strengths (\u03b1), latent space dimensions, and noise levels. These findings indicate that the mean (\u03bc) and frequency domain methods offer robust performance and are minimally affected by changes in these parameters, making them particularly suitable for preserving ECG signal quality. By contrasting these methods, this study provides insights into selecting the most appropriate watermarking technique for ECG sensor applications. Incorporating watermarking into sensor design not only strengthens data security and authenticity but also supports reliable signal acquisition in modern healthcare environments. Overall, the results underscore the effectiveness of combining VAEs with watermarking strategies to produce high-fidelity, resilient ECG signals for both sensor performance evaluation and the protection of digital content.",
    "authors": [
        {
            "affiliation": "School of Transportation, Fujian University of Technology, Fuzhou 350118, China.",
            "firstname": "Chih-Yu",
            "initials": "CY",
            "lastname": "Hsu"
        },
        {
            "affiliation": "Department of Medical Informatics, Chung Shan Medical University, Taichung 40201, Taiwan.",
            "firstname": "Chih-Yin",
            "initials": "CY",
            "lastname": "Chang"
        },
        {
            "affiliation": "Department of Medical Informatics, Chung Shan Medical University, Taichung 40201, Taiwan.",
            "firstname": "Yin-Chi",
            "initials": "YC",
            "lastname": "Chen"
        },
        {
            "affiliation": "Kang Chiao International School, Linkou Campus, New Taipei City 244, Taiwan.",
            "firstname": "Jasper",
            "initials": "J",
            "lastname": "Wu"
        },
        {
            "affiliation": "Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan.",
            "firstname": "Shuo-Tsung",
            "initials": "ST",
            "lastname": "Chen"
        }
    ],
    "conclusions": null,
    "copyrights": null,
    "doi": "10.3390/s25072321\n10.1016/j.sysarc.2022.102431\n10.1007/s11390-021-1406-9\n10.1109/TCAD.2020.3013050\n10.1007/s13246-019-00838-2\n10.1109/ITC.2010.96\n10.3390/s23229199\n10.1016/j.bspc.2022.103540\n10.3390/s20102941\n10.1007/s10916-005-6126-0\n10.1109/TMM.2019.2907475\n10.1016/j.dsp.2022.103648\n10.1109/IRANIANCEE.2010.5507111\n10.1109/ICIINFS.2016.8262949\n10.1109/TBME.2013.2264539\n10.1038/nature14539\n10.3390/app13020804\n10.1038/s41591-018-0306-1\n10.1109/72.557662\n10.1007/s13534-021-00185-w\n10.1109/TCBB.2020.2976981\n10.5555/2969033.2969125\n10.1016/j.compbiomed.2022.105948\n10.3390/s22218341\n10.1109/ICACCI.2014.6968312\n10.1145/1390156.1390294\n10.18637/jss.v031.i07\n10.1007/s40846-021-00641-z\n10.3389/fnagi.2022.870844",
    "journal": "Sensors (Basel, Switzerland)",
    "keywords": [
        "Fourier-simulated ECG dataset",
        "latent variable space",
        "variational AutoEncoder",
        "watermarking technology"
    ],
    "methods": null,
    "publication_date": "2025-04-12",
    "pubmed_id": "40218832\n38005585\n32455935\n16235811\n23708767\n26017442\n30617324\n18255629\n34150350\n32142452\n35994931\n36366039\n35527738",
    "results": null,
    "title": "ECG Sensor Design Assessment with Variational Autoencoder-Based Digital Watermarking.",
    "xml": "<Element 'PubmedArticle' at 0x7885054ba610>"
}