Biomedical Signal Processing with AI: Applications in Cardiovascular and Neurological Care

Resumen

Biomedical signal processing, powered by Artificial Intelligence (AI), is revolutionizing cardiovascular and neurological healthcare by providing advanced diagnostic and therapeutic solutions. AI algorithms, including machine learning (ML) and deep learning (DL), are being integrated into biomedical signal processing to analyze complex physiological signals such as ECG, EEG, and EMG, enabling earlier detection and improved monitoring of diseases. In cardiovascular care, AI-based systems are used for real-time arrhythmia detection, risk stratification, and heart disease prediction by analyzing electrocardiogram (ECG) signals. Similarly, AI in neurological care has significantly impacted the diagnosis and monitoring of neurological disorders, such as epilepsy, Alzheimer’s, and Parkinson’s disease. These applications utilize electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) signals to identify abnormalities in brain activity, providing insights into disease progression and treatment efficacy. The use of AI in biomedical signal processing also facilitates the development of personalized treatment plans, as these systems can analyze patient-specific data and predict outcomes more accurately than traditional methods. Furthermore, AI algorithms contribute to the development of wearable and implantable devices that continuously monitor patient health, providing real-time data for continuous care. Despite its significant potential, challenges such as data privacy, ethical concerns, and the need for large annotated datasets remain hurdles for widespread adoption. This paper explores the current state of AI in biomedical signal processing, its impact on cardiovascular and neurological care, and the future prospects of these technologies in clinical practice.