BMC Med Inform Decis Mak - Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning.

Tópicos

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Resumo

CKGROUND: Ventricular Fibrillation (VF) is a common presenting dysrhythmia in the setting of cardiac arrest whose main treatment is defibrillation through direct current countershock to achieve return of spontaneous circulation. However, often defibrillation is unsuccessful and may even lead to the transition of VF to more nefarious rhythms such as asystole or pulseless electrical activity. Multiple methods have been proposed for predicting defibrillation success based on examination of the VF waveform. To date, however, no analytical technique has been widely accepted. We developed a unique approach of computational VF waveform analysis, with and without addition of the signal of end-tidal carbon dioxide (PetCO2), using advanced machine learning algorithms. We compare these results with those obtained using the Amplitude Spectral Area (AMSA) technique.METHODS: A total of 90 pre-countershock ECG signals were analyzed form an accessible preshosptial cardiac arrest database. A unified predictive model, based on signal processing and machine learning, was developed with time-series and dual-tree complex wavelet transform features. Upon selection of correlated variables, a parametrically optimized support vector machine (SVM) model was trained for predicting outcomes on the test sets. Training and testing was performed with nested 10-fold cross validation and 6-10 features for each test fold.RESULTS: The integrative model performs real-time, short-term (7.8 second) analysis of the Electrocardiogram (ECG). For a total of 90 signals, 34 successful and 56 unsuccessful defibrillations were classified with an average Accuracy and Receiver Operator Characteristic (ROC) Area Under the Curve (AUC) of 82.2% and 85%, respectively. Incorporation of the end-tidal carbon dioxide signal boosted Accuracy and ROC AUC to 83.3% and 93.8%, respectively, for a smaller dataset containing 48 signals. VF analysis using AMSA resulted in accuracy and ROC AUC of 64.6% and 60.9%, respectively.CONCLUSION: We report the development and first-use of a nontraditional non-linear method of analyzing the VF ECG signal, yielding high predictive accuracies of defibrillation success. Furthermore, incorporation of features from the PetCO2 signal noticeably increased model robustness. These predictive capabilities should further improve with the availability of a larger database.

Resumo Limpo

ckground ventricular fibril vf common present dysrhythmia set cardiac arrest whose main treatment defibril direct current countershock achiev return spontan circul howev often defibril unsuccess may even lead transit vf nefari rhythm asystol pulseless electr activ multipl method propos predict defibril success base examin vf waveform date howev analyt techniqu wide accept develop uniqu approach comput vf waveform analysi without addit signal endtid carbon dioxid petco use advanc machin learn algorithm compar result obtain use amplitud spectral area amsa techniquemethod total precountershock ecg signal analyz form access preshospti cardiac arrest databas unifi predict model base signal process machin learn develop timeseri dualtre complex wavelet transform featur upon select correl variabl parametr optim support vector machin svm model train predict outcom test set train test perform nest fold cross valid featur test foldresult integr model perform realtim shortterm second analysi electrocardiogram ecg total signal success unsuccess defibril classifi averag accuraci receiv oper characterist roc area curv auc respect incorpor endtid carbon dioxid signal boost accuraci roc auc respect smaller dataset contain signal vf analysi use amsa result accuraci roc auc respectivelyconclus report develop firstus nontradit nonlinear method analyz vf ecg signal yield high predict accuraci defibril success furthermor incorpor featur petco signal notic increas model robust predict capabl improv avail larger databas

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