Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest

Chicote, Beatriz; Irusta, Unai; Aramendi, Elisabete; Alcaraz, Raúl; Rieta, J.J.; Isasi, Iraia; Alonso, Daniel; Baqueriza, María Del Mar; Ibarguren, Karlos

doi:10.3390/e20080591

Cited by 18 publications

(15 citation statements)

References 86 publications

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“…In the future several other clinical decision support algorithms during OHCA could benefit from using deep learning solutions. Some areas of current research include the prediction of shock success [79, 80], the analysis of the ECG during manual and mechanical chest compressions [81, 82], multiclass rhythm classification [30], or the detection of pulse [83]. In fact some deep learning solutions have been recently proposed for instance for the detection of pulse [84].…”

Section: Discussionmentioning

confidence: 99%

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

et al. 2019

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Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task. This study introduces a deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF. Two datasets were used, one from public repositories of Holter recordings captured at the onset of the arrhythmia, and a second from OHCA patients obtained minutes after the onset of the arrest. Data was partitioned patient-wise into training (80%) to design the classifiers, and test (20%) to report the results. The proposed architecture was compared to 1D-CNN only deep learners, and to a classical approach based on VF-detection features and a support vector machine (SVM) classifier. The algorithms were evaluated in terms of balanced accuracy (BAC), the unweighted mean of the sensitivity (Se) and specificity (Sp). The BAC, Se, and Sp of the architecture for 4-s ECG segments was 99.3%, 99.7%, and 98.9% for the public data, and 98.0%, 99.2%, and 96.7% for OHCA data. The proposed architecture outperformed all other classifiers by at least 0.3-points in BAC in the public data, and by 2.2-points in the OHCA data. The architecture met the 95% Sp and 90% Se requirements of the American Heart Association in both datasets for segment lengths as short as 3-s. This is, to the best of our knowledge, the most accurate VF detection algorithm to date, especially on OHCA data, and it would enable an accurate shock no shock diagnosis in a very short time.

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Section: Discussionmentioning

confidence: 99%

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

et al. 2019

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“…FuzzyEn quantifies the regularity of VF by analyzing repetitive patterns along the waveform. VF-amplitude was considered in the calculation of FuzzyEn as proposed in [17,18], because VF-amplitude has been shown to correlate to the state of the myocardium [35].…”

Section: Predictors Of Defibrillation Successmentioning

confidence: 99%

“…The best known VF-waveform feature is Amplitude Spectrum Area (AMSA), a measure of amplitude and frequency distribution of VF [15,16]. Recently, VF waveform regularity and predictability measures based on entropy estimates have been shown to accurately predict shock success [17,18], and Fuzzy Entropy (FuzzyEn) was identified as the most accurate predictor based on entropy estimates outperforming other classical predictors [17].…”

Section: Introductionmentioning

confidence: 99%

Value of capnography to predict defibrillation success in out-of-hospital cardiac arrest

et al. 2019

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Background and aim: Unsuccessful defibrillation shocks adversely affect survival from out-ofhospital cardiac arrest (OHCA). Ventricular fibrillation (VF) waveform analysis is the tool-ofchoice for the non-invasive prediction of shock success, but surrogate markers of perfusion like end-tidal CO 2 (EtCO 2) could improve the prediction. The aim of this study was to evaluate EtCO 2 as predictor of shock success, both individually and in combination with VF-waveform analysis. Materials and methods: In total 514 shocks from 214 OHCA patients (75 first shocks) were analysed. For each shock three predictors of defibrillation success were automatically calculated from the device files: two VF-waveform features, amplitude spectrum area (AMSA) and fuzzy entropy (FuzzyEn), and the median EtCO 2 (MEtCO 2) in the minute before the shock. Sensitivity, specificity, receiver operating characteristic (ROC) curves and area under the curve (AUC) were calculated, for each predictor individually and for the combination of MEtCO 2 and VF-waveform predictors. Separate analyses were done for first shocks and all shocks. Results: MEtCO 2 in first shocks was significantly higher for successful than for unsuccessful shocks (31 mmHg/25 mmHg, p<0.05), but differences were not significant for all shocks (32 mmHg/29 mmHg, p>0.05). MEtCO 2 predicted shock success with an AUC of 0.66 for first shocks, but was not a predictor for all shocks (AUC 0.54). AMSA and FuzzyEn presented AUCs of 0.76 and 0.77 for first shocks, and 0.75 and 0.75 for all shocks. For first shocks, adding MEtCO 2 improved the AUC of AMSA and FuzzyEn to 0.79 and 0.83, respectively. Conclusions: MEtCO 2 predicted defibrillation success only for first shocks. Adding MEtCO 2 to VF-waveform analysis in first shocks improved prediction of shock success. VF-waveform

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“…Features from 10 to 15 in the left column and from 10 to 12 in the right column were introduced by Rad et al [8]. Fuzzy Entropy (FuzzEn), the Signal Integral parameter (SignInt), the Peak Power Frequency (PPF), the Smoothed Nonlinear Energy Operator (SNEO) and the Hjorth Mobility parameter are described in [9,12], [13], [14], [15] and [16], respectively. The remaining features were designed for this work: the number of QRS-like peaks (Npeak) and the Euclidean distance between the Hjorth Mobility and the Hjorth Mobility of the second degree (Mx2).…”

Section: Resultsmentioning

confidence: 99%

ECG Rhythm Analysis During Manual Chest Compressions Using an Artefact Removal Filter and Random Forest Classifiers

Isasi¹,

Rad²,

Irusta³

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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Interruptions in cardiopulmonary resuscitation (CPR) decrease the chances of survival. However, CPR must be interrupted for a reliable rhythm analysis because chest compressions (CCs) induce artifacts in the ECG. This paper introduces a double-stage shock advice algorithm (SAA) for a reliable rhythm analysis during manual CCs. The method used two configurations of the recursive least-squares (RLS) filter to remove CC artifacts from the ECG. For each filtered ECG segment over 200 shock/no-shock decision features were computed and fed into a random forest (RF) classifier to select the most discriminative 25 features. The proposed SAA is an ensemble of two RF classifiers which were trained using the 25 features derived from different filter configurations. Then, the average value of class posterior probabilities was used to make a final shock/no-shock decision. The dataset was comprised of 506 shockable and 1697 non-shockable rhythms which were labelled by expert rhythm resuscitation reviewers in artifact-free intervals. Shock/no-shock diagnoses obtained through the proposed double-stage SAA were compared with the rhythm annotations to obtain the Sensitivity (Se), Specificity (Sp) and balanced accuracy (BAC) of the method. The results were 93.5%, 96.5% and 95.0%, respectively.

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Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest

Cited by 18 publications

References 86 publications

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

Value of capnography to predict defibrillation success in out-of-hospital cardiac arrest

ECG Rhythm Analysis During Manual Chest Compressions Using an Artefact Removal Filter and Random Forest Classifiers

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