Cuesta Pedro, Lado María J, Vila Xosé A, Alonso Raúl
Department of Computer Science, ESEI, University of Vigo, Ourense, Spain.
Technol Health Care. 2014;22(4):651-6. doi: 10.3233/THC-140818.
Premature ventricular contractions (PVCs) are cardiac abnormalities that may occur in subjects with/without cardiovascular disorder. Detection is usually performed from electrocardiograms (ECGs); heart activity for a long period of time must be recorded at hospital or with ambulatory electrocardiography. An alternative with a common mobile device would be very interesting, because a simple heart rate sensor should be sufficient.
To develop an algorithm to detect PVCs using the RR-interval (distance between consecutive beats) extracted from ECGs or from the heart rate signal captured by mobile devices.
Feature extraction and classification techniques were included: 1) two timing interval features (prematurity and compensatory pause) were extracted. 2) A linear classifier was applied. To validate the method, the MIT-BIH Arrhythmia Database was used. Considering the existence of unbalanced classes (normal beats and PVCs) at different decision costs, validation was performed with receiver operating characteristic (ROC) analysis.
A sensitivity of 90.13% and a specificity percentage of 82.52% were achieved. The area under the ROC curve (AUC) was 0.928.
The method is advantageous since it only uses the RR-interval signal for PVC detection, and results compare well with more complex methods that use ECG recording.
室性早搏（PVCs）是一种心脏异常情况，可发生于有/无心血管疾病的患者。通常通过心电图（ECGs）进行检测；必须在医院或使用动态心电图记录长时间的心脏活动。使用普通移动设备进行检测是一种很有吸引力的替代方法，因为一个简单的心率传感器应该就足够了。
开发一种算法，利用从心电图或移动设备捕获的心率信号中提取的RR间期（连续心跳之间的距离）来检测室性早搏。
包括特征提取和分类技术：1）提取两个时间间隔特征（提前性和代偿间歇）。2）应用线性分类器。为验证该方法，使用了麻省理工学院-比哈尔心律失常数据库。考虑到在不同决策成本下存在不平衡类别（正常心跳和室性早搏），采用受试者操作特征（ROC）分析进行验证。
灵敏度达到90.13%，特异性百分比为82.52%。ROC曲线下面积（AUC）为0.928。
该方法具有优势，因为它仅使用RR间期信号进行室性早搏检测，且结果与使用心电图记录的更复杂方法相比具有良好的可比性。
