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Classification of arrhythmia patterns is an open field of research. One of the most widely used biological signals crucial for the detection of heart conditions is the electrocardiogram (ECG). Within the processing of ECG signals, one of the most critical aspects is the interpretation and acquisition of the QRS complex. Both the diagnosis of cardiac rhythm abnormalities and the assessment of heart rate variability depend heavily on the R wave (HRV). In this paper, the modified Pan-Tompkins method with an FIR filter is used for baseline wandering and noise reduction. Temporal statistical features are extracted using the QRS peak detection algorithm. The QRS interval, RR peak interval, QRS deviation, kurtosis, and skewness of the QRS are utilized as the features extracted from the ECG under test. This paper compares the classification accuracy of the standard QRS interval thresholding-based binary approach and the proposed support vector machine (SVM)-based classification method. The proposed optimized K-nearest neighbors (KNN) algorithm outperforms tree and thresholding-based classifiers.

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Classification of Arrhythmia Data an QRS Peak Detection For Feature Extraction Using SVM Classifiers

  • 会议论文

  • 发表日期：

    2025年

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  • Tanuja Goswami,
  • Amit Saxena

摘要

Classification of arrhythmia patterns is an open field of research. One of the most widely used biological signals crucial for the detection of heart conditions is the electrocardiogram (ECG). Within the processing of ECG signals, one of the most critical aspects is the interpretation and acquisition of the QRS complex. Both the diagnosis of cardiac rhythm abnormalities and the assessment of heart rate variability depend heavily on the R wave (HRV). In this paper, the modified Pan-Tompkins method with an FIR filter is used for baseline wandering and noise reduction. Temporal statistical features are extracted using the QRS peak detection algorithm. The QRS interval, RR peak interval, QRS deviation, kurtosis, and skewness of the QRS are utilized as the features extracted from the ECG under test. This paper compares the classification accuracy of the standard QRS interval thresholding-based binary approach and the proposed support vector machine (SVM)-based classification method. The proposed optimized K-nearest neighbors (KNN) algorithm outperforms tree and thresholding-based classifiers.