IEEE J Biomed Health Inform. 2022 May;26(5):2124-2135. doi: 10.1109/JBHI.2021.3130275. Epub 2022 May 5.
Based on cybernetics, a large system can be divided into subsystems, and the stability of each can determine the overall properties of the system. However, this stability analysis perspective has not yet been employed in electrocardiogram (ECG) signals. This is the first study to attempt to evaluate whether the stability of decomposed ECG subsystems can be analyzed in order to effectively investigate the overall performance of ECG signals, and aid in disease diagnosis.
We used seven different cardiac pathologies (myocardial infarction, cardiomyopathy, bundle branch block, dysrhythmia, hypertrophy, myocarditis, and valvular heart disease) to illustrate our method. Dynamic mode decomposition (DMD) was first used to decompose ECG signals into dynamic modes (DMs) which can be regarded as ECG subsystems. Then, the features related to the DMs stabilities were extracted, and nine common classifiers were implemented for classification of these pathologies.
Most features were significant for differentiating the above-mentioned groups (p value<0.05 after Bonferroni correction). In addition, our method outperformed all existing methods for cardiac pathology classification.
We have provided a new spatial and temporal decomposition method, namely DMD, to study ECG signals.
Our method can reveal new cardiac mechanisms, which can contribute to the comprehensive understanding of its underlying mechanisms and disease diagnosis, and thus, can be widely used for ECG signal analysis in the future.
基于控制论，一个大系统可以被分为子系统，并且每个子系统的稳定性可以决定系统的整体性质。然而，这种稳定性分析的视角尚未被应用于心电图（ECG）信号。这是首次尝试评估分解后的 ECG 子系统的稳定性是否可以进行分析，以便有效地研究 ECG 信号的整体性能，并辅助疾病诊断。
我们使用七种不同的心脏病理学（心肌梗死、心肌病、束支传导阻滞、心律失常、肥大、心肌炎和瓣膜性心脏病）来说明我们的方法。首先使用动态模态分解（DMD）将 ECG 信号分解为可以看作 ECG 子系统的动态模态（DM）。然后，提取与 DM 稳定性相关的特征，并实现了九种常用的分类器来对这些病理进行分类。
大多数特征在区分上述组之间具有统计学意义（经过 Bonferroni 校正后 p 值<0.05）。此外，我们的方法在心脏病理学分类方面优于所有现有的方法。
我们提供了一种新的时空分解方法，即 DMD，用于研究 ECG 信号。
我们的方法可以揭示新的心脏机制，有助于全面理解其潜在机制和疾病诊断，因此可以在未来广泛用于 ECG 信号分析。
