BIOSIGNAL PREPROCESSING METHODS: A COMPREHENSIVE REVIEW OF NOISE REMOVAL, FILTERING AND SEGMENTATION TECHNIQUES FOR AI-BASED PREDICTION SYSTEMS

Abstract

Effective preprocessing is a critical determinant of the accuracy and reliability of artificial intelligence (AI)-based biosignal prediction systems. Raw biosignals acquired through sensors are inevitably contaminated by multiple noise sources including baseline wander, powerline interference, electromyographic artifacts, and electrode motion artifacts. This paper presents a comprehensive review of preprocessing methods for ECG, EEG, and EMG biosignals, systematically covering noise characterization, digital filtering techniques segmentation algorithms (Pan-Tompkins), and normalization approaches.