Circulation detection using the electrocardiogram and the thoracic impedance acquired by defibrillation pads

Abstract

Aim: To develop and evaluate a method to detect circulation in the presence of organized rhythms (ORs) during resuscitation using signals acquired by defibrillation pads.

Methods: Segments containing electrocardiogram (ECG) and thoracic impedance (TI) signals free of artifacts were used. The ECG corresponded to ORs classified as pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A first dataset containing 1091 segments was split into training and test sets to develop and validate the circulation detector. The method processed ECG and TI to obtain the impedance circulation component (ICC). Morphological features were extracted from ECG and ICC, and combined into a classifier to discriminate between PEA and PR. The performance of the method was evaluated in terms of sensitivity (PR) and specificity (PEA). A second dataset (86 segments from different patients) was used to assess two application of the method: confirmation of arrest by recognizing absence of circulation during ORs and detection of return of spontaneous circulation (ROSC) during resuscitation. In both cases, time to confirmation of arrest/ROSC was determined.

Results: The method showed a sensitivity/specificity of 92.1%/90.3% and 92.2%/91.9% for training and test sets respectively. The method confirmed cardiac arrest with a specificity of 93.3% with a median delay of 0s after the first OR annotation. ROSC was detected with a sensitivity of 94.4% with a median delay of 57s from ROSC onset.

Conclusion: The method showed good performance, and can be reliably used to distinguish perfusing from non-perfusing ORs.

Keywords: Circulation detection; Defibrillator; Pulse; Pulse-generating rhythm; Pulseless electrical activity; Thoracic impedance.

Figure 1

Overview of the circulation detector consisting of three different stages. First, the signal processing stage where the ECG signal and the TI signal are digitally processed to detect the instants of the QRS complexes, and to extract the ICC signal. Second, the feature extraction stage where features characterizing the waveform of the ECG, ICC, and its first derivative are extracted. Finally, the classifier distinguishes between PEA and PR based on the extracted features.

Figure 2

Examples of PEA (panel a) and PR segments (panel b) where the ECG and ICC signals are depicted from top to bottom.

Figure 3

First (panel a), second (panel b) and third (panel c) PEA segments of the patient of the case study where the first PEA segment was misclassified. The ECG signals are presented at the top, while the ICC signals are depicted at the bottom.

Figure 4

First (panel a), second (panel b) and third (panel c) PR segments of the patient of the case study where the first PR segment was misclassified. The ECG signals are presented at the top, while the ICC signals are depicted at the bottom.

Figure 5

Diagram of the adaptive scheme applying a RLS algorithm used to extract the $\widehat{\mathit{icc}[n]}$ from the filtered TI signal, z_p[n].