(untitled) (Comprehensive Journal of Science, 2025)
Artificial Intelligence (AI) and advanced computational tools such as MATLAB have become essential in biomedical research and clinical practice. Real-time biomedical signal processing plays a critical role in improving patient monitoring, early diagnosis, and therapeutic interventions. Biomedical signals such as electrocardiograms (ECG), electroencephalograms (EEG), and electromyograms (EMG) contain rich diagnostic information, but they are often contaminated by various sources of noise including power line interference, muscle artifacts, and baseline wander. Real-time signal processing ensures that physicians and automated systems can interpret these signals accurately and without delay, enabling prompt medical decisions This paper presents an overview of assisted real-time biomedical signal processing, wherein the stress is particular on ECG analysis. MATLAB offers an integrated environment with specialty toolboxes such as Signal Processing Toolbox and DSP System Toolbox, where a researcher can design, simulate, and implement algorithms in a real-time setting. MATLAB supports fast prototyping and testing of biomedical systems by the use of heavy filtering techniques and an active real-time visualization tool. With this case study, the ECG signals are filtered through a fourth-order Butterworth bandpass filter within the range of 0.5–40 Hz. With baseline drift and high-frequency noise being filtered out, the focus is so much on the clarity of the QRS complex and other important features of the ECG waveform. MATLAB allows for the continuous acquisition of signals, instantaneous signal processing, and coordination with clinical monitoring systems for displaying both raw and filtered signals. Hence, the results show that good filtering could reduce false alarms and ensure early detection of arrhythmias, thereby largely improving diagnostic accuracy and patient safety