Imam Mohammad Hasan, Karmakar Chandan K, Jelinek Herbert F, Palaniswami Marimuthu, Khandoker Ahsan H
IEEE J Biomed Health Inform. 2016 Jan;20(1):64-72. doi: 10.1109/JBHI.2015.2426206. Epub 2015 Apr 24.
In this study, a linear parametric modeling technique was applied to model ventricular repolarization (VR) dynamics. Three features were selected from the surface ECG recordings to investigate the changes in VR dynamics in healthy and cardiac autonomic neuropathy (CAN) participants with diabetes including heart rate variability (calculated from RR intervals), repolarization variability (calculated from QT intervals), and respiration [calculated by ECG-derived respiration (EDR)]. Surface ECGs were recorded in a supine resting position from 80 age-matched participants (40 with no cardiac autonomic neuropathy (NCAN) and 40 with CAN). In the CAN group, 25 participants had early/subclinical CAN (ECAN) and 15 participants were identified with definite/clinical CAN (DCAN). Detecting subclinical CAN is crucial for designing an effective treatment plan to prevent further cardiovascular complications. For CAN diagnosis, VR dynamics was analyzed using linear parametric autoregressive bivariate (ARXAR) and trivariate (ARXXAR) models, which were estimated using 250 beats of derived QT, RR, and EDR time series extracted from the first 5 min of the recorded ECG signal. Results showed that the EDR-based models gave a significantly higher fitting value (p < 0.0001) than models without EDR, which indicates that QT-RR dynamics is better explained by respiratory-information-based models. Moreover, the QT-RR-EDR model fitting values gradually decreased from the NCAN group to ECAN and DCAN groups, which indicate a decoupling of QT from RR and the respiration signal with the increase in severity of CAN. In this study, only the EDR-based model significantly distinguished ECAN and DCAN groups from the NCAN group (p < 0.05) with large effect sizes (Cohen's d > 0.75) showing the effectiveness of this modeling technique in detecting subclinical CAN. In conclusion, the EDR-based trivariate QT-RR-EDR model was found to be better in detecting the presence and severity of CAN than the bivariate QT-RR model. This finding also establishes the importance of adding respiratory information for analyzing the gradual deterioration of normal VR dynamics in pathological conditions, such as diabetic CAN.
在本研究中，应用线性参数建模技术对心室复极（VR）动力学进行建模。从体表心电图记录中选取了三个特征，以研究健康参与者以及患有糖尿病的心脏自主神经病变（CAN）参与者的VR动力学变化，这些特征包括心率变异性（根据RR间期计算）、复极变异性（根据QT间期计算）和呼吸[通过心电图衍生呼吸（EDR）计算]。对80名年龄匹配的参与者（40名无心脏自主神经病变（NCAN），40名患有CAN）在仰卧休息姿势下记录体表心电图。在CAN组中，25名参与者患有早期/亚临床CAN（ECAN），15名参与者被确定为明确/临床CAN（DCAN）。检测亚临床CAN对于设计有效的治疗方案以预防进一步的心血管并发症至关重要。对于CAN诊断，使用线性参数自回归双变量（ARXAR）和三变量（ARXXAR）模型分析VR动力学，这些模型是使用从记录的心电图信号的前5分钟提取的250次搏动的推导QT、RR和EDR时间序列进行估计的。结果表明，基于EDR的模型比没有EDR的模型具有显著更高的拟合值（p < 0.0001），这表明基于呼吸信息的模型能更好地解释QT-RR动力学。此外，QT-RR-EDR模型的拟合值从NCAN组到ECAN组和DCAN组逐渐降低，这表明随着CAN严重程度的增加，QT与RR和呼吸信号解耦。在本研究中，只有基于EDR的模型能显著区分ECAN组和DCAN组与NCAN组（p < 0.05），且效应量较大（科恩d值> 0.75），表明该建模技术在检测亚临床CAN方面的有效性。总之，发现基于EDR的三变量QT-RR-EDR模型在检测CAN的存在和严重程度方面比双变量QT-RR模型更好。这一发现也确立了在分析病理状态（如糖尿病性CAN）下正常VR动力学的逐渐恶化时添加呼吸信息的重要性。
