Nilsson Frida, Stridh Martin, Sörnmo Leif
Conf Proc IEEE Eng Med Biol Soc. 2006;2006:1406-9. doi: 10.1109/IEMBS.2006.259677.
A Hidden Markov Model (HMM) is used to improve the robustness to noise when tracking the atrial fibrillation (AF) frequency in the ECG. Each frequency interval corresponds to a state in the HMM. Following QRST cancellation, a sequence of observed states is obtained from the residual ECG, using the short time Fourier transform. Based on the observed state sequence, the Viterbi algorithm, which uses a state transition matrix, an observation matrix and an initial state vector, is employed to obtain the optimal state sequence. The state transition matrix incorporates knowledge of intrinsic AF characteristics, e.g., frequency variability, while the observation matrix incorporates knowledge of the frequency estimation method and SNRs. An evaluation is performed using simulated AF signals where noise obtained from ECG recordings have been added at different SNR. The results show that the use of HMM considerably reduces the average RMS error associated with the frequency tracking: at 5 dB SNR the RMS error drops from 1.2 Hz to 0.2 Hz.
隐马尔可夫模型（HMM）用于在跟踪心电图（ECG）中的房颤（AF）频率时提高对噪声的鲁棒性。每个频率区间对应于HMM中的一个状态。在进行QRST消除之后，使用短时傅里叶变换从残余心电图中获得一系列观察到的状态。基于观察到的状态序列，采用维特比算法，该算法使用状态转移矩阵、观察矩阵和初始状态向量来获得最优状态序列。状态转移矩阵纳入了房颤固有特征的知识，例如频率变异性，而观察矩阵纳入了频率估计方法和信噪比的知识。使用模拟的房颤信号进行评估，其中已添加从心电图记录中获得的不同信噪比的噪声。结果表明，使用HMM大大降低了与频率跟踪相关的平均均方根误差：在5 dB信噪比时，均方根误差从1.2 Hz降至0.2 Hz。
