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ECG Identification under Exercise and Rest Situations via Various Learning Methods
By applying various existing learning methods to our ECG dataset, we find that current methods which can well support the identification of individuals under rests, do not suffice to present satisfying ECGID performance under exercise situations, therefore exposing the deficiency of existing ECG identification methods.
Identifying the Mislabeled Training Samples of ECG Signals using Machine Learning
The classification accuracy of electrocardiogram signal is often affected by diverse factors in which mislabeled training samples issue is one of the most influential problems.
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