Department of Computing, University of Turku, Vesilinnantie 5, 20500 Turku, Finland.
Heart Center, Turku University Hospital, Hämeentie 11, 20520 Turku, Finland.
Sensors (Basel). 2022 Jun 9;22(12):4384. doi: 10.3390/s22124384.
Novel means to minimize treatment delays in patients with ST elevation myocardial infarction (STEMI) are needed. Using an accelerometer and gyroscope on the chest yield mechanocardiographic (MCG) data. We investigated whether STEMI causes changes in MCG signals which could help to detect STEMI. The study group consisted of 41 STEMI patients and 49 control patients referred for elective coronary angiography and having normal left ventricular function and no valvular heart disease or arrhythmia. MCG signals were recorded on the upper sternum in supine position upon arrival to the catheterization laboratory. In this study, we used a dedicated wearable sensor equipped with 3-axis accelerometer, 3-axis gyroscope and 1-lead ECG in order to facilitate the detection of STEMI in a clinically meaningful way. A supervised machine learning approach was used. Stability of beat morphology, signal strength, maximum amplitude and its timing were calculated in six axes from each window with varying band-pass filters in 2-90 Hz range. In total, 613 features were investigated. Using logistic regression classifier and leave-one-person-out cross validation we obtained a sensitivity of 73.9%, specificity of 85.7% and AUC of 0.857 (SD = 0.005) using 150 best features. As a result, mechanical signals recorded on the upper chest wall with the accelerometers and gyroscopes differ significantly between STEMI patients and stable patients with normal left ventricular function. Future research will show whether MCG can be used for the early screening of STEMI.
需要寻找新的方法来尽量减少 ST 段抬高型心肌梗死(STEMI)患者的治疗延误。利用胸部的加速计和陀螺仪可以获得力学心电图(MCG)数据。我们研究了 STEMI 是否会导致 MCG 信号发生变化,这些变化有助于检测 STEMI。研究组包括 41 名 STEMI 患者和 49 名因择期冠状动脉造影而就诊的对照患者,他们的左心室功能正常,没有瓣膜性心脏病或心律失常。MCG 信号在到达导管室时以上胸部仰卧位记录。在这项研究中,我们使用了一种专用的可穿戴传感器,该传感器配备了 3 轴加速度计、3 轴陀螺仪和 1 导联心电图,以便以有临床意义的方式方便地检测 STEMI。使用了监督机器学习方法。在 2-90 Hz 范围内的不同带通滤波器下,从每个窗口的六个轴计算了节律形态、信号强度、最大振幅及其时间的稳定性。总共研究了 613 个特征。使用逻辑回归分类器和留一法交叉验证,我们使用 150 个最佳特征获得了 73.9%的敏感性、85.7%的特异性和 0.857(SD=0.005)的 AUC。结果表明,STEMI 患者与左心室功能正常的稳定患者的胸壁上部机械信号记录存在显著差异。未来的研究将表明 MCG 是否可用于 STEMI 的早期筛查。