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Using electrocardiogram (ECG) data, this study presents an algorithmic framework for identifying cardiac arrhythmias, specifically classifying Normal, Ventricular Tachycardia, and Fibrosis. The algorithm consists of several steps, such as preprocessing the data, visualizing the data using a histogram, and using Gaussian parameters to model data distributions. Finally, Bhattacharyya distance computations are performed to evaluate the degree of class separation. The system uses a variety of methods in the classification phase, including a 1-D convolutional deep residual neural networks, Principal Component Analysis (PCA) for reduction of size, Linear Discriminant Function (LDF), and K-Nearest Neighbhours (K-NN). The study is noteworthy for examining comparative Receiver Operating Characteristic (ROC) curves, which emphasize the performance of the approach with particular attention to sensitivity and specificity. This all-inclusive method integrates machine learning, statistical analysis, and visualization techniques in advancing the efficacy of arrhythmia diagnosis.

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ECG Arrhythmia Classification Through Synergy of Pattern and Machine Learning with Fisher’s Criterion

  • 会议论文

  • 发表日期：

    2026年

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  • K. Chandrahasa Reddy,
  • Y. Pavan Kumar Reddy,
  • Y. Sunanda,
  • K. Shankar,
  • M. Ravi Kishore

摘要

Using electrocardiogram (ECG) data, this study presents an algorithmic framework for identifying cardiac arrhythmias, specifically classifying Normal, Ventricular Tachycardia, and Fibrosis. The algorithm consists of several steps, such as preprocessing the data, visualizing the data using a histogram, and using Gaussian parameters to model data distributions. Finally, Bhattacharyya distance computations are performed to evaluate the degree of class separation. The system uses a variety of methods in the classification phase, including a 1-D convolutional deep residual neural networks, Principal Component Analysis (PCA) for reduction of size, Linear Discriminant Function (LDF), and K-Nearest Neighbhours (K-NN). The study is noteworthy for examining comparative Receiver Operating Characteristic (ROC) curves, which emphasize the performance of the approach with particular attention to sensitivity and specificity. This all-inclusive method integrates machine learning, statistical analysis, and visualization techniques in advancing the efficacy of arrhythmia diagnosis.