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

ABSTRACT:

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

SUBMITTER: Zhang HH 

PROVIDER: S-EPMC7576062 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature