Cardiac Spectrum

Cardiac spectrum analysis focuses on extracting meaningful information from the complex electrical signals of the heart to improve diagnosis and understanding of cardiac health. Current research employs advanced signal processing techniques, including phase space analysis and fractal dimension calculations, alongside machine learning algorithms like Gaussian process regression and convolutional neural networks, to analyze electrocardiogram (ECG) data and identify patterns indicative of normal and abnormal heart function. These methods offer the potential for more accurate and efficient cardiac diagnostics, leading to improved patient care and reduced healthcare costs. The development of robust and efficient algorithms for analyzing these complex datasets is a key focus of ongoing research.