ECG Features Extraction to Semiautomatic Detection of Atrial Fibrillation

Cardiovascular diseases are heart abnormalities that gradually lead to decreased oxygen supply to all body's vital parts, affecting other organs such as the brain and lungs due to the heart's poor functioning. While specialists diagnose these diseases using ECG waveforms and their physical properties, the sporadic nature of cardiac events often results in delayed detection. Numerous scientists have focused on digital signal processing of ECG to facilitate semi-automatic diagnosis. This paper introduces an algorithm that blends wavelet transform with digital filtering techniques to identify features indicative of atrial fibrillation. Clinical Relevance— Our algorithm paves the way for a semiautomatic cardiac diagnostic tool for emergency settings, enabling rapid 10-second signal analyses without the need for expert visual assessment, reducing error chances.