Sequential Characterization of Atrial Tachyarrhythmias Based on ECG Time-Frequency Analysis

Abstract

A new method for characterization of atrial arrhythmias is presented which is based on the time-frequency distribution of an atrial electrocardiographic signal. A set of parameters are derived which describe fundamental frequency, amplitude, shape, and signal-to-noise ratio. The method uses frequency-shifting of an adaptively updated spectral profile, representing the shape of the atrial waveforms, in order to match each new spectrum of the distribution. The method tracks how well the spectral profile fits each spectrum as well as if a valid atrial signal is present. The results are based on the analysis of a learning database with signals from 40 subjects, of which 24 have atrial arrhythmias, and an evaluation database with 211 patients diagnosed with atrial fibrillation. It is shown that the method robustly estimates fibrillation frequency and amplitude and produces spectral profiles with narrower peaks and more discernible harmonics when compared to the conventional power spectrum. The results suggest that a rather strong correlation exist between atrial fibrillation frequency and f wave shape. The developed set of parameters may be used as a basis for automated classification of different atrial rhythms.

Time–frequency analysis of the surface electrocardiogram for monitoring antiarrhythmic drug effects in atrial fibrillation

“…[1][2][3] Briefly, after analog-to-digital conversion (2000 Hz, 12 bit, 0.05 -300 Hz) electrograms were stored on optical disk and transferred to a personal computer. After high-pass filtering to remove baseline wander, QRST complexes were subtracted using spatiotemporal QRST cancellation.…”

“…Higher harmonic frequency components are consequently associated with a smaller exponential decay and indicate more organized rhythms. [2,3] …”

Signal Quality Assessment of F-waves in Atrial Fibrillation

“…A database with 12-lead ECGs recorded from 189 patients with permanent AF is analyzed, acquired at the University Hospital in Lund [9]. A 5-s segment is extracted from lead V 1 in each ECG.…”

Heartbeat biometrics: a sensing system perspective

“…Within a domain, features are extracted as either raw (Israel et al, 2005), texture (Porta et al, 2001), power spectrum (Barros and Ohnishi, 2001;Stridh et al, 2004), and PCA/ ICA (Garcia et al, 1998;Barros et al, 2000). Afonso et al (1999) integrated the noise reduction and feature extraction by using filterbanks.…”