Removal of Cardiopulmonary Resuscitation Artifacts with an Enhanced Adaptive Filtering Method: An Experimental Trial

Abstract

Current automated external defibrillators mandate interruptions of chest compression to avoid the effect of artifacts produced by CPR for reliable rhythm analyses. But even seconds of interruption of chest compression during CPR adversely affects the rate of restoration of spontaneous circulation and survival. Numerous digital signal processing techniques have been developed to remove the artifacts or interpret the corrupted ECG with promising result, but the performance is still inadequate, especially for nonshockable rhythms. In the present study, we suppressed the CPR artifacts with an enhanced adaptive filtering method. The performance of the method was evaluated by comparing the sensitivity and specificity for shockable rhythm detection before and after filtering the CPR corrupted ECG signals. The dataset comprised 283 segments of shockable and 280 segments of nonshockable ECG signals during CPR recorded from 22 adult pigs that experienced prolonged cardiac arrest. For the unfiltered signals, the sensitivity and specificity were 99.3% and 46.8%, respectively. After filtering, a sensitivity of 93.3% and a specificity of 96.0% were achieved. This animal trial demonstrated that the enhanced adaptive filtering method could significantly improve the detection of nonshockable rhythms without compromising the ability to detect a shockable rhythm during uninterrupted CPR.

Adaptive Filtering Improved Apnea Detection Performance Using Tracheal Sounds in Noisy Environment: A Simulation Study

“…Adaptive filtering (AF), which can deal with various kinds of signals in unknown statistical environment or in nonstationary environment, is a promising method of signal processing in adaptive noise cancellation [16]. It is usually better than a fixed filter designed through conventional methods and has been widely used [17], such as removing artifacts in electroencephalography (EEG) [18,19], electrocardiography (ECG) [20], impedance cardiography [21], and photoplethysmography (PPG) [22]. However, the accuracy and reliability of the apnea detection algorithm using tracheal sounds after AF have not been examined.…”

Adult Basic Life Support

“…Fourteen full-text papers were identified and reviewed, 219 but none assessed any critical or important patient-related outcomes. Most of these studies use previously collected electrocardiographs, electric impedance, and/or accelerometer signals recorded during CPR for cardiac arrest to evaluate the ability of various algorithms 220 , 221 , 222 , 223 , 224 , 225 , 226 , 227 , 228 , 229 or machine learning 230 to detect shockable rhythms during chest compressions.…”

Minimizing pre-shock chest compression pauses in a cardiopulmonary resuscitation cycle by performing an earlier rhythm analysis

“…There have been many attempts to filter out chest compression artefacts from the ECG to enable reliable analysis during chest compressions, but so far the sensitivity and specificity have not been sufficiently high. (133)(134)(135)(136)(137) One large retrospective study with a new algorithm discriminated recently between shockable and non-shockable cardiac rhythms during chest compressions with very high sensitivity and specificity. (138) No studies have looked at the effect of such filtering technologies on outcomes such as ROSC or survival in humans and ERC and AHA therefore advise against the use of such filtering technologies outside the scope of a research program.…”