Key result
The choice of samples for training and testing arrhythmia classifiers impacts their performance in terms of accuracy, sensitivity, and specificity.
The methodology used to select training and testing samples is critical in evaluating the true performance of ECG arrhythmia classification algorithms.
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ECG arrhythmia classifier metrics warrant cautious interpretation; leaves open standardized validation protocols for reliable clinical use.
Arrhythmia (i.e., irregular cardiac beat) classification in electrocardiogram (ECG) signals is an important issue for heart disease diagnosis due to the non-invasive nature of the ECG exam. In this paper, we analyze and criticize the results of some arrhythmia classification methods presented in the literature in terms of how the samples are chosen for training/testing the classifier and the impact this choice has on their performance (i.e., accuracy/sensitivity/specificity). From our implementation, we also report new accuracies for these methods, establishing a new state-of-the-art method, in terms of results.
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Luz et al. (2011) studied Arrhythmia. Sample selection methods for training/testing classifiers was evaluated on Classifier performance (accuracy/sensitivity/specificity). The choice of samples for training and testing arrhythmia classifiers impacts their performance in terms of accuracy, sensitivity, and specificity.
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