Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
PLoS One. 2022 Feb 25;17(2):e0264405. doi: 10.1371/journal.pone.0264405. eCollection 2022.
Differentiating between shockable and non-shockable Electrocardiogram (ECG) signals would increase the success of resuscitation by the Automated External Defibrillators (AED). In this study, a Deep Neural Network (DNN) algorithm is used to distinguish 1.4-second segment shockable signals from non-shockable signals promptly. The proposed technique is frequency-independent and is trained with signals from diverse patients extracted from MIT-BIH, MIT-BIH Malignant Ventricular Ectopy Database (VFDB), and a database for ventricular tachyarrhythmia signals from Creighton University (CUDB) resulting, in an accuracy of 99.1%. Finally, the raspberry pi minicomputer is used to load the optimized version of the model on it. Testing the implemented model on the processor by unseen ECG signals resulted in an average latency of 0.845 seconds meeting the IEC 60601-2-4 requirements. According to the evaluated results, the proposed technique could be used by AED's.
区分可电击性和不可电击性心电图 (ECG) 信号将提高自动体外除颤器 (AED) 复苏的成功率。在这项研究中，使用深度神经网络 (DNN) 算法来快速区分 1.4 秒段的可电击信号和不可电击信号。所提出的技术是频率独立的，并使用来自麻省理工学院-生物医学工程系统研究所 (MIT-BIH)、麻省理工学院-生物医学工程系统研究所恶性室性心律失常数据库 (VFDB) 和克瑞顿大学 (CUDB) 的不同患者的信号进行训练，准确率达到 99.1%。最后，使用树莓派微型计算机将模型的优化版本加载到它上面。通过未知的 ECG 信号在处理器上测试实现的模型，平均延迟为 0.845 秒，满足 IEC 60601-2-4 的要求。根据评估结果，该技术可用于 AED。
