Abstract
Cardiopulmonary resuscitation (CPR) corrupts the morphology of the electrocardiogram (ECG) signal, resulting in an inaccurate automated external defibrillator (AED) rhythm analysis. Consequently, most current AEDs prohibit CPR during the rhythm analysis period, thereby decreasing the survival rate. To overcome this limitation, we designed a condition-based filtering algorithm that consists of three stop-band filters which are turned either ‘on’ or ‘off’ depending on the ECG’s spectral characteristics. Typically, removing the artifact’s higher frequency peaks in addition to the highest frequency peak eliminates most of the ECG’s morphological disturbance on the non-shockable rhythms. However, the shockable rhythms usually have dynamics in the frequency range of (3–6) Hz, which in certain cases coincide with CPR compression’s harmonic frequencies, hence, removing them may lead to destruction of the shockable signal’s dynamics. The proposed algorithm achieves CPR artifact removal without compromising the integrity of the shockable rhythm by considering three different spectral factors. The dataset from the PhysioNet archive was used to develop this condition-based approach. To quantify the performance of the approach on a separate dataset, three performance metrics were computed: the correlation coefficient, signal-to-noise ratio (SNR), and accuracy of Defibtech’s shock decision algorithm. This dataset, containing 14 s ECG segments of different types of rhythms from 458 subjects, belongs to Defibtech commercial AED’s validation set. The CPR artifact data from 52 different resuscitators were added to artifact-free ECG data to create 23,816 CPR-contaminated data segments. From this, 82% of the filtered shockable and 70% of the filtered non-shockable ECG data were highly correlated (>0.7) with the artifact-free ECG; this value was only 13 and 12% for CPR-contaminated shockable and non-shockable, respectively, without our filtering approach. The SNR improvement was 4.5 ± 2.5 dB, averaging over the entire dataset. Defibtech’s rhythm analysis algorithm was applied to the filtered data. We found a sensitivity improvement from 67.7 to 91.3% and 62.7 to 78% for VF and rapid VT, respectively, and specificity improved from 96.2 to 96.5% and 91.5 to 92.7% for normal sinus rhythm (NSR) and other non-shockables, respectively.
Highlights
Our development set contains different types found in the original study [23]. This algorithm is considered as the category of apof non-shockable andanalyzes shockable rhythms from and 45 differentECGs subjects, proaches that directly and classifies
Analyzing cardiopulmonary resuscitation (CPR)-contaminated ECG signals may lead to erroneous shock decisions by an automated external defibrillator (AED)
This work introduced a computationally simple and yet efficient ECG filtering approach which can be used to determine shock versus no-shock decisions while CPR is performed without stoppage
Summary
Out of hospital cardiac arrest (OHCA) affects more than 325,000 people in the United. This occurs either due to shockable rhythms, such as rapid ventricular tachycardia (RVT) and ventricular fibrillation (VF), or non-shockable rhythms such as asystole and pulseless electrical activity (PEA) [1]. Two-thirds of OHCAs start as a non-shockable rhythm [2]. The most effective treatment for non-shockable rhythms is cardiopulmonary resuscitation (CPR). For shockable RVT and VF, applying electrical shock with an automated external defibrillator (AED) in conjunction with CPR is critical to reset heart activity, according to the American Heart Association’s (AHA) 2020 guidelines [3]
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