Key result
A novel ECG rhythm classification framework without R-peak detection achieved a 97.56% F1 score for ventricular flutter and average accuracies of 99.28% and 88.83% on external validation databases.
Why the study?
Most ECG rhythm classification methods rely on R-peak detection, but detection algorithms affect performance and arrhythmias lacking distinct R-peaks remain difficult to identify.
A novel ECG classification framework independent of R-peak detection demonstrates high accuracy in identifying arrhythmias, including those lacking distinct R-peaks like ventricular flutter.
R-peak detection choice may affect arrhythmia classification accuracy; leaves open robust methods for flutter and similar rhythms.
As a primary indicator for cardiovascular diseases, the electrocardiogram (ECG) is commonly used for arrhythmia classification. Many related studies emphasize that the R-peaks of the ECG signal are essential for extracting features or signal segmentation. Thus, the chosen R-peak detection algorithm affects classification performance. Furthermore, the lack of distinct R-peaks in arrhythmias like ventricular flutter makes these rhythms difficult to identify, regardless of the detection algorithm. Therefore, this study proposes a novel ECG rhythm classification framework that does not depend on R-peak detection. First, the n number of beat segments is acquired by sliding a window over the ECG signal. A scalogram is then produced from each segment and fed into a pre-trained beat classifier to generate n beat score vectors. These vectors are concatenated chronologically to establish an n-beat score map (n-BSM), which serves as input for our rhythm classification model. The n-BSM of a rhythm conveys information regarding its constituent n beats by sequentially arranging their characteristics, each captured by a score distribution over various beat types. Experimental results from the MIT-BIH arrhythmia database (MITDB) demonstrate that the proposed method improves overall performance in classifying ten ECG rhythms. Moreover, we achieved a 97.56% F1 score for a ventricular flutter rhythm lacking distinct R-peaks. We also utilized the MIT-BIH Malignant Ventricular Ectopy (VFDB) and the Chapman-Shaoxing 12-lead ECG databases (SPH) to verify the proposed method’s robustness and generalizability. The average accuracy for different rhythms was 99.28% and 88.83%, respectively.
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Lee et al. (2024) studied Arrhythmia. ECG rhythm classification framework without R-peak detection was evaluated on Classification performance (accuracy and F1 score). A novel ECG rhythm classification framework without R-peak detection achieved a 97.56% F1 score for ventricular flutter and average accuracies of 99.28% and 88.83% on external validation databases.
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