QRS Complex Detection in MIT-BIH Database Using Biorthogonal Spline Wavelets

This article presents a comprehensive methodology for QRS complex detection using biorthogonal spline wavelets, validated against the MIT-BIH arrhythmia database. The QRS complex represents one of the most critical waveforms in ECG signals, as it corresponds to the ventricular depolarization process during cardiac contraction. Accurate QRS detection is therefore fundamental for reliable ECG signal analysis and clinical diagnosis. We detail the implementation of biorthogonal spline wavelet-based QRS detection, including the wavelet decomposition algorithm that effectively separates high-frequency QRS components from background noise and baseline wander. The method employs multi-resolution analysis with specific scale selection optimized for QRS characteristic frequencies (typically 5-15 Hz). Key implementation aspects include wavelet coefficient thresholding techniques and peak detection algorithms applied to the transformed signal. The approach demonstrates particular advantages in handling varying QRS morphologies and noise robustness, though certain limitations exist in cases of extreme arrhythmias or high-frequency noise interference. We provide specific examples of applying this methodology to MIT-BIH database records, including preprocessing steps such as signal normalization and noise filtration. Performance evaluation metrics including sensitivity, positive predictivity, and detection error rates are analyzed against manual annotations. Through comparative analysis with conventional detection methods, we demonstrate the enhanced performance of biorthogonal spline wavelets in maintaining high detection accuracy while minimizing false positives. The article enables readers to gain deeper understanding of wavelet-based QRS detection techniques and their practical implementation in ECG signal processing and diagnostic applications. Code implementation typically involves wavelet transform libraries, peak detection algorithms, and validation routines against standard databases.