Biomedical signal processing is a research field focused on analyzing physiological signals collected from the human body to extract meaningful information for medical diagnosis, monitoring, and treatment. These signals include electrocardiograms (ECG) for heart activity, electroencephalograms (EEG) for brain function, electromyograms (EMG) for muscle activity, and other biosignals such as blood pressure and respiratory rates. Researchers in this field develop advanced algorithms for noise reduction, feature extraction, and pattern recognition to enhance signal interpretation. Techniques such as machine learning, time-frequency analysis, and adaptive filtering help detect abnormalities like heart arrhythmias, epileptic seizures, or neurological disorders. Biomedical signal processing plays a crucial role in wearable health monitoring devices, telemedicine, and automated diagnostic systems, improving healthcare accessibility and accuracy.
Biomedical image processing research involves the development of computational techniques to enhance, analyze, and interpret medical images obtained from modalities such as X-ray, MRI, CT, ultrasound, and microscopy. The goal is to improve image quality, detect abnormalities, and assist in medical decision-making. Researchers in this field work on image segmentation, feature extraction, pattern recognition, and deep learning-based techniques to automate disease diagnosis and treatment planning. Applications include tumor detection in MRI scans, real-time analysis of ultrasound images, and 3D reconstruction of anatomical structures for surgical planning. Biomedical image processing is essential for precision medicine, enabling early disease detection, minimally invasive interventions, and improved patient outcomes.