Zhang He-Hua, Yang Li, Wei An-Hai, Duan Ao-Wen, Li Yong-Ming, Zhao Ping, Li Yong-Qin
Department of Medical Engineering, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
College of Communication Engineering of Chongqing University, Chongqing, China.
Ann Transl Med. 2020 Sep;8(18):1165. doi: 10.21037/atm-20-5906.
A transthoracic impedance (TTI) signal is an important indicator of the quality of chest compressions (CCs) during cardiopulmonary resuscitation (CPR). We proposed an automatic detection algorithm including the wavelet decomposition, fuzzy c-means (FCM) clustering, and deep belief network (DBN) to identify the compression and ventilation waveforms for evaluating the quality of CPR.
TTI signals were collected from a cardiac arrest model that electrically induced cardiac arrest in pigs. All signals were denoised using the wavelet and morphology method. The potential compression and ventilation waveforms were marked using an algorithm with a multi-resolution window. The compressions and ventilations in these waveforms were identified and classified using the FCM clustering and DBN methods.
Using the FCM clustering method, the positive predictive values (PPVs) for compressions and ventilations were 99.7% and 95.7%, respectively. The sensitivities of recognition were 99.8% for compressions and 95.1% for ventilations. The DBN approach exhibited similar PPV and sensitivity results to the FCM clustering method. The time cost was satisfactory using either of these techniques.
Our findings suggest that FCM clustering and DBN can be utilized to effectively and accurately evaluate CPR quality, and provide information for improving the success rate of CPR. Our real-time algorithms using FCM clustering and DBN eliminated most distortions and noises effectively, and correctly identified the compression and ventilation waveforms with a low time cost.
经胸阻抗（TTI）信号是心肺复苏（CPR）期间胸外按压（CCs）质量的重要指标。我们提出了一种自动检测算法，包括小波分解、模糊C均值（FCM）聚类和深度信念网络（DBN），以识别按压和通气波形，从而评估CPR质量。
从电诱导猪心脏骤停的心脏骤停模型中收集TTI信号。所有信号均使用小波和形态学方法进行去噪。使用具有多分辨率窗口的算法标记潜在的按压和通气波形。使用FCM聚类和DBN方法对这些波形中的按压和通气进行识别和分类。
使用FCM聚类方法，按压和通气的阳性预测值（PPV）分别为99.7%和95.7%。识别的敏感度对于按压为99.8%，对于通气为95.1%。DBN方法的PPV和敏感度结果与FCM聚类方法相似。使用这两种技术中的任何一种，时间成本都令人满意。
我们的研究结果表明，FCM聚类和DBN可用于有效且准确地评估CPR质量，并为提高CPR成功率提供信息。我们使用FCM聚类和DBN的实时算法有效消除了大部分失真和噪声，并以较低的时间成本正确识别了按压和通气波形。
