Automatic identification of compressions and ventilations during CPR based on the fuzzy c-means clustering and deep belief network

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.