Key result
Using a Hidden Markov Model for frequency tracking of simulated atrial fibrillation signals reduced the average RMS error from 1.2 Hz to 0.2 Hz at 5 dB SNR compared to tracking without HMM.
Absolute Event Rate: 0.2% vs 1.2%
Applying a hidden Markov model to ECG signals improves noise robustness and significantly reduces error when tracking the dominant frequency of atrial fibrillation.
May aid noisy AF frequency tracking; leaves open clinical translation from simulated animal data.
A hidden Markov model (HMM) is employed to improve noise robustness when tracking the dominant frequency of atrial fibrillation (AF) in the electrocardiogram (ECG). Following QRST cancellation, a sequence of observed frequency states is obtained from the residual ECG, using the short-time Fourier transform. Based on the observed state sequence, the Viterbi algorithm retrieves the optimal state sequence by exploiting the state transition matrix, incorporating knowledge on AF characteristics, and the observation matrix, incorporating knowledge of the frequency estimation method and signal-to-noise ratio (SNR). The tracking method is evaluated with simulated AF signals to which noise, obtained from ECG recordings, has been added at different SNRs. The results show that the use of HMM improves performance considerably by reducing the rms error associated with frequency tracking: at 4-dB SNR, the rms error drops from 0.2 to 0.04 Hz.
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Sandberg et al. (2008) studied Atrial fibrillation (simulated). Hidden Markov Model (HMM) for frequency tracking vs. Frequency tracking without HMM was evaluated on Average RMS error of estimated frequencies at 5 dB SNR. Using a Hidden Markov Model for frequency tracking of simulated atrial fibrillation signals reduced the average RMS error from 1.2 Hz to 0.2 Hz at 5 dB SNR compared to tracking without HMM.
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