Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China.
Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Fudan University, Shanghai 200032, China.
Sensors (Basel). 2021 May 19;21(10):3524. doi: 10.3390/s21103524.
Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.
心室颤动（VF）是一种致命性心律失常，可在数分钟内导致猝死。研究 VF 检测算法具有重要的临床意义。本研究旨在开发一种基于非接触式传感器获取与心脏机械活动相关信号（即心冲击图（BCG））的 VF 自动检测算法。通过在猪身上进行电除颤实验，采集了包括 VF、窦性心律和运动伪影在内的 BCG 信号。通过自相关和 S 变换，获得了具有明显心脏节律活动信息的时频图，并通过统计分析和层次聚类构建了每个 7s 段的 13 个元素特征集。然后，使用随机森林分类器对 VF 和非-VF 进行分类，并采用患者内和患者间两种范例来评估性能。结果表明，在 10 折交叉验证下，灵敏度和特异性分别为 0.965 和 0.958，在留一受试者外验证下，灵敏度和特异性分别为 0.947 和 0.946。总之，该算法结合特征提取和机器学习可以有效地检测 BCG 中的 VF，为家庭长期自主心脏监测和 VF 实时检测报警系统的开发奠定了基础。
