Analysis of Biomedical Signal for Health Diagnosis Using AI

Biomedical signal analysis plays a crucial role in early disease detection and continuous health monitoring. This paper presents an AI-based system for the analysis of biomedical signals, particularly Electrocardiogram (ECG) signals, to assist in health diagnosis. The proposed system focuses on detecting abnormalities by applying machine learning techniques to physiological signal data. Initially, raw ECG signals are collected from publicly available datasets and preprocessed to remove noise and unwanted artifacts using filtering techniques. Important features such as heart rate variability, QRS complex characteristics, and statistical parameters are extracted from the cleaned signals. These features are then used to train and test machine learning models for classifying normal and abnormal cardiac conditions. The experimental results demonstrate that the proposed model achieves high accuracy in detecting abnormalities, making it suitable for early diagnosis and remote health monitoring applications. The system can support healthcare professionals by providing automated, reliable, and real-time analysis of biomedical signals. Future enhancements may include deep learning integration and multi-signal analysis for improved diagnostic performance.