European heart journal. Digital health · 2024
Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms.
СтатьяНазвание и аннотация: Перевод в очереди
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История · было → стало
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Mangold Kathryn E, Carter Rickey E, Siontis Konstantinos C, Noseworthy Peter A, Lopez-Jimenez Francisco, Asirvatham Samuel J, Friedman Paul A, Attia Zachi I
Аннотация
AIMS: Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record.
METHODS AND RESULTS: We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively.
CONCLUSION: The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.
METHODS AND RESULTS: We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively.
CONCLUSION: The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.