Nonlinear Energy Operators for Defibrillation Shock Outcome Prediction

Chicote, Beatriz; Irusta, Unai; Aramendi, Elisabete; Isasi, Iraia; Alonso, Daniel; Vicente, Fernando

doi:10.22489/cinc.2016.020-378

Cited by 3 publications

(3 citation statements)

References 8 publications

(12 reference statements)

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“…In paper [24], it is shown that various simple VF features, such as median slope, already reach the maximum prediction power extractable from VF ECG and are not sufficient to make a VF prediction with good results. In paper [25], the Smoothed Nonlinear Energy Operator (SNEO) is applied to analyze the energy content of the pre-shock ventricular fibrillation (VF) waveform acquired by automated external defibrillators (AEDs), and using SNEO as a shock outcome predictor, the minimum pre-shock segment duration (5-s segments) was determined. In paper [26], the predictive power of a model developed by 'genetic' programming (GP) to predict defibrillation success was studied.…”

Section: Discussionmentioning

confidence: 99%

“…The time domain features contain the amplitude range (AR), peak-to-peak amplitude (PPA), mean amplitude (MA), median stepping increment (MSI), signal integral (SignInt), two definitions of the VF waveform root mean square (RMS) value, RMS1 and RMS2 [23], mean and median slope (MS and MdS) [24], and a smoothed nonlinear energy operator (SNEO) [25,33].…”

Section: Featuresmentioning

confidence: 99%

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Prediction and Detection of Ventricular Fibrillation Using Complex Features and AI-Based Classification

Fira,

Costin,

Goras

2024

Applied Sciences

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We analyzed the possibility of detecting and predicting ventricular fibrillation (VF), a medical emergency that may put people’s lives at risk, as the medical prognosis depends on the time in which medical personnel intervene. Therefore, besides immediate detection of VF, the possibility of predicting VF 40 or even 50 min in advance was analyzed. For testing the proposed algorithm, we used ECG signals taken from the MIT-BIH database, namely, Malignant Ventricular Ectopy Database, Sudden Cardiac Death Holter Database and Normal Sinus Rhythm Database. The presented method is based on features extracted from the ECG signals in the time domain, frequency domain and complexity measures. For VF detection, the possibility of identifying the VF episode in the first 3 s after its occurrence was tested. For this, the first 3 s immediately after the appearance of VF were cut out and the features were computed on these sections. For VF prediction, 3 min of the ECG signal clipped 40 or 50 min before VF onset was used. Then, on these pieces of ECG signal, the specific features were calculated for 1 s segments. For the normal signal situation, 3 min was randomly selected from the database with normal ECGs. For the classification or detection stage, both an MLP-type neural network and the classifiers from the Machine Learning toolbox of the MATLAB® environment were used. The results obtained for both detection and classification are over 94% in both cases. The novelty of our results compared to those previously obtained is the time interval with which the possibility of prediction was analyzed, namely, 50 min in advance of the VF installation date. This means that the patient will be informed that it is possible to suffer a VF and has time to take the necessary measures to overcome a possible medical emergency.

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Section: Discussionmentioning

confidence: 99%

Section: Featuresmentioning

confidence: 99%

Prediction and Detection of Ventricular Fibrillation Using Complex Features and AI-Based Classification

Fira,

Costin,

Goras

2024

Applied Sciences

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“…• The smoothed nonlinear energy operator (SNEO) as described in [40], which shows higher values for signals with higher amplitudes.…”

mentioning

confidence: 99%

Multimodal Algorithms for the Classification of Circulation States During Out-of-Hospital Cardiac Arrest

Elola¹,

Aramendi

Irusta³

et al. 2021

IEEE Trans. Biomed. Eng.

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Identifying the circulation state during 1 out-of-hospital cardiac arrest (OHCA) is essential to determine 2 what life-saving therapies to apply. Currently algorithms 3 discriminate circulation (pulsed rhythms, PR) from no circulation 4 (pulseless electrical activity, PEA), but PEA can be classified 5 into true (TPEA) and pseudo (PPEA) depending on cardiac 6 contractility. This study introduces multi-class algorithms to 7 automatically determine circulation states during OHCA using 8 the signals available in defibrillators. Methods: A cohort of 9 60 OHCA cases were used to extract a dataset of 2506 5-s 10 segments, labeled as PR (1463), PPEA (364) and TPEA (679) 11 using the invasive blood pressure, experimentally recorded 12 through a radial/femoral cannulation. A multimodal algorithm 13 using features obtained from the electrocardiogram, the thoracic 14 impedance and the capnogram was designed. A random forest 15 model was trained to discriminate three (TPEA/PPEA/PR) and 16 two (PEA/PR) circulation states. The models were evaluated 17 using repeated patient-wise 5-fold cross-validation, with the 18 unweighted mean of sensitivities (UMS) and F1-score as 19 performance metrics. Results: The best model for 3-class had 20 a median (interquartile range, IQR) UMS and F1 of 69.0% 21 (68.0-70.1) and 61.7% (61.0-62.5), respectively. The best two class 22 classifier had median (IQR) UMS and F1 of 83.9% (82.9-84.5) 23 and 76.2% (75.0-76.9), outperforming all previous proposals 24 in over 3-points in UMS. Conclusions: The first multiclass 25 OHCA circulation state classifier was demonstrated. The method 26 improved previous algorithms for binary pulse/no-pulse decisions.

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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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Nonlinear Energy Operators for Defibrillation Shock Outcome Prediction

Cited by 3 publications

References 8 publications

Prediction and Detection of Ventricular Fibrillation Using Complex Features and AI-Based Classification

Prediction and Detection of Ventricular Fibrillation Using Complex Features and AI-Based Classification

Multimodal Algorithms for the Classification of Circulation States During 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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