Zhu Junjiang, Lv Jintao, Kong Dongdong
School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
School of Mechatronic Engineering and Automation, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai 200444, China.
Entropy (Basel). 2022 Jun 10;24(6):812. doi: 10.3390/e24060812.
(1) Background: A typical cardiac cycle consists of a P-wave, a QRS complex, and a T-wave, and these waves are perfectly shown in electrocardiogram signals (ECG). When atrial fibrillation (AF) occurs, P-waves disappear, and F-waves emerge. F-waves contain information on the cause of atrial fibrillation. Therefore it is essential to extract F-waves from the ECG signal. However, F-waves overlap the QRS complex and T-waves in both the time and frequency domain, causing this matter to be a difficult one. (2) Methods: This paper presents an optimized resonance-based signal decomposition method for detecting F-waves in single-lead ECG signals with atrial fibrillation (AF). It represents the ECG signal utilizing morphological component analysis as a linear combination of a finite number of components selected from the high-resonance and low-resonance dictionaries, respectively. The linear combination of components in the low-resonance dictionary reconstructs the oscillatory part (F-wave) of the ECG signal. In contrast, the linear combination of components in the high-resonance dictionary reconstructs the transient components part (QRST wave). The tunable Q-factor wavelet transform generates the high and low resonance dictionaries, with a high Q-factor producing a high resonance dictionary and a low Q-factor producing a low resonance dictionary. The different Q-factor settings affect the dictionaries' characteristics, hence the F-wave extraction. A genetic algorithm was used to optimize the Q-factor selection to select the optimal Q-factor. (3) Results: The presented method helps reduce RMSE between the extracted and the simulated F-waves compared to average beat subtraction (ABS) and principal component analysis (PCA). According to the amplitude of the F-wave, RMSE is reduced by 0.24-0.32. Moreover, the dominant frequency of F-waves extracted by the presented method is clearer and more resistant to interference. The presented method outperforms the other two methods, ABS and PCA, in F-wave extraction from AF-ECG signals with the ventricular premature heartbeat. (4) Conclusion: The proposed method can potentially improve the accuracy of F-wave extraction for mobile ECG monitoring equipment, especially those with fewer leads.
(1) 背景：典型的心电周期由P波、QRS复合波和T波组成，这些波形在心电图信号（ECG）中能完美显示。当发生心房颤动（AF）时，P波消失，F波出现。F波包含心房颤动病因的信息。因此，从ECG信号中提取F波至关重要。然而，F波在时域和频域上均与QRS复合波和T波重叠，使得此事颇具难度。(2) 方法：本文提出一种基于优化共振的信号分解方法，用于检测伴有心房颤动（AF）的单导联ECG信号中的F波。它利用形态成分分析将ECG信号表示为分别从高共振字典和低共振字典中选取的有限数量成分的线性组合。低共振字典中成分的线性组合重构了ECG信号的振荡部分（F波）。相反，高共振字典中成分的线性组合重构了瞬态成分部分（QRST波）。可调Q因子小波变换生成高共振字典和低共振字典，高Q因子产生高共振字典，低Q因子产生低共振字典。不同的Q因子设置会影响字典的特性，进而影响F波提取。采用遗传算法优化Q因子选择以选取最优Q因子。(3) 结果：与平均搏动减法（ABS）和主成分分析（PCA）相比，所提方法有助于降低提取的F波与模拟F波之间的均方根误差（RMSE）。根据F波的幅度，RMSE降低了0.24 - 0.32。此外，所提方法提取的F波的主导频率更清晰且抗干扰能力更强。在所提方法从伴有室性早搏的AF - ECG信号中提取F波方面，其性能优于其他两种方法，即ABS和PCA。(4) 结论：所提方法有可能提高移动ECG监测设备，尤其是导联较少的设备的F波提取精度。
