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. 2020 Sep;8(18):1165.
doi: 10.21037/atm-20-5906.

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

He-Hua Zhang  1 , Li Yang  1 , An-Hai Wei  1   2 , Ao-Wen Duan  1 , Yong-Ming Li  2   3 , Ping Zhao  4   5 , Yong-Qin Li  6
Affiliations

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

He-Hua Zhang et al. Ann Transl Med. 2020 Sep.

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 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.

Keywords: Transthoracic impedance (TTI); automatic detection; deep belief network (DBN); fuzzy c-means (FCM) clustering method; wavelet decomposition.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/atm-20-5906). The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
The segment examples of TTI signals collected in this experiment. (A) The waveforms of a standard signal; (B) an abnormal signal; (C) a signal affected by noise and baseline drift. TTI, transthoracic impedance.
Figure 2
Figure 2
Detailed wavelet coefficients. (A) The 1st level; (B) the 2nd level; (C) the 3rd level; (D) the 4th level; (E) the 5th level; (F) the 6th level; (G) the 7th level; (H): the 8th level; (I) the 9th level.
Figure 3
Figure 3
Feature curve of discrete wavelet transform Dw; the ratio of the original waveform’s amplitude to the energy of the 5th wavelet coefficient.
Figure 4
Figure 4
Flow chart of automatic detection algorithm of TTI signals. This algorithm is conducted with preprocessing, waveform marking, wavelet decomposition and other steps. TTI, transthoracic impedance.
Figure 5
Figure 5
Classification results of original data. (A) An abnormal signal (original data: Figure 1B); (B) a signal affected by noise and baseline drift (original data: Figure 1C). The circles and stars represent the peaks and troughs of compression waveforms, respectively. The triangles and squares represent the peaks and troughs of ventilation waveforms, respectively. TTI, transthoracic impedance.
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