Atrial fibrillation detection using stationary wavelet transform analysis

Atrial fibrillation (AF) is a common cardiac arrythmia that is usually developed for elder people with aging. AF may result in complications such as chest pain or even heart failure in later stage. Based on the characteristics of surface ECG, AF can be detected by several methods. A particular investigation on the fibrillatory waveform reveals the inherent structure of AF signals. As opposed to traditional frequency domain methods, we utilize the stationary wavelet transform to extract the information from ECG signal which differentiates AF and non-AF cases based on some feature extraction and selection processes. A linear classifier is then designed for computational efficiency. The proposed method eliminates the need for QRST cancellation step which is required for frequency domain methods and provides a more systematic approach for AF detection. Extensive experiments are tested on signals from the MIT-BIH Atrial Fibrillation Database to show the superior performance of the proposed algorithm.