Key result
A novel stretchable, wireless seven-lead ECG patch coupled with a machine learning model achieved 99.93% accuracy and 99.98% sensitivity for myocardial infarction identification and location.
Why the study?
There are very few reports on stretchable multilead ECG patches integrated with myocardial infarction location capability.
A novel stretchable multilead ECG patch combined with a machine learning model demonstrates high accuracy and sensitivity for dynamic myocardial infarction monitoring and location.
Requires prospective clinical validation before use; leaves open wearable ECG utility for real-time MI monitoring.
Wearable electronics for cardiac monitoring have been widely developed in the field of routine vital sign monitoring and arrhythmia determination due to their convenience and continuity. However, there are very few reports on the demonstration of a stretchable multilead electrocardiogram (ECG) patch integrated with myocardial infarction (MI) location capability. Here, we first propose a wearable dynamic cardiac monitoring patch, which can acquire seven-lead ECG signals continuously. A novel stretchable bioelectrode is mounted on the patch, which is strain-insensitive in the 100% tensile strain range. Moreover, the bioelectrode maintains good adhesion to the skin at more than 0.4 N/cm. This soft and wireless multilead ECG patch is designed for long-term, all-round real-time cardiac monitoring. For MI classification, a machine learning model for MI identification and location is trained with accuracy (99.93%) and sensitivity (99.98%). In addition, we also propose a new framework for the automated annotation of MI abnormal segments, which simultaneously addresses the recognition of abnormal waveforms and the integration of interlead relationships. This study contributes to the realization of personalized medical monitoring and intervention as well as early warning for MI.
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Zhang et al. (2025) studied Myocardial Infarction. Wearable dynamic cardiac monitoring patch (seven-lead ECG) was evaluated on Myocardial infarction identification and location accuracy. A novel stretchable, wireless seven-lead ECG patch coupled with a machine learning model achieved 99.93% accuracy and 99.98% sensitivity for myocardial infarction identification and location.
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