ECG Signal Processing: Classification Techniques and Feature Extraction Methods

ECG signal processing represents a critical biomedical engineering field that involves numerous classification algorithms and feature extraction methodologies. In practical implementations, ECG processing typically employs digital filtering techniques (such as Butterworth or Chebyshev filters) for noise reduction, followed by feature extraction using algorithms like Pan-Tompkins for QRS complex detection or wavelet transforms for multi-resolution analysis. Classification often leverages machine learning approaches including Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), or deep learning architectures like Convolutional Neural Networks (CNN) for arrhythmia detection. Beyond classification and feature extraction, comprehensive ECG processing pipelines incorporate additional techniques such as adaptive filtering for motion artifact removal, time-frequency analysis using Short-Time Fourier Transform (STFT) or Continuous Wavelet Transform (CWT), and signal quality assessment algorithms. The field offers substantial research opportunities to enhance processing accuracy and reliability through improved feature selection methods, hybrid classification models, and real-time processing optimizations using embedded systems or GPU acceleration.