Atrial fibrillation analysis using Bessel Kernel based time frequency distribution technique

We propose a Bessel kernel based time frequency distribution technique for identification and tracking of spectrum of atrial fibrillation (AF) in ECG. The algorithm shows a good frequency resolution and a low RMS error even when the noise dominates the signal which is critical for detecting the low amplitude AF activity within the ECG. In comparison with other time frequency distributions, the Bessel kernel reduces cross terms between frequencies in the multi-component ECG signal. Superiority of the Bessel kernel method over the short time Fourier transform (STFT) is demonstrated using a frequency modulated sinusoidal model and using real AF data. At low signal to noise levels the Bessel distribution outperforms the STFT and at an SNR of -5 dB the RMS error is reduced from 1.8 Hz to 0.8 Hz. Also it achieves a frequency resolution of 0.5 Hz at an SNR of 0 dB which is four times better than that of the STFT.