Identifying the Mislabeled Training Samples of ECG Signals using Machine Learning
This addresses mislabeled data issues in ECG signal classification, which is an incremental improvement for medical diagnostics.
The paper tackled the problem of mislabeled ECG training samples affecting classification accuracy by using cross-validation and multiple classifiers as a filter to remove mislabeled data, resulting in higher classification accuracies demonstrated on the MIT-BIH arrhythmia database.
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. In order to mitigate this negative effect, the method of cross validation is introduced to identify the mislabeled samples. The method utilizes the cooperative advantages of different classifiers to act as a filter for the training samples. The filter removes the mislabeled training samples and retains the correctly labeled ones with the help of 10-fold cross validation. Consequently, a new training set is provided to the final classifiers to acquire higher classification accuracies. Finally, we numerically show the effectiveness of the proposed method with the MIT-BIH arrhythmia database.