ECG derived feature combination versus single feature in predicting defibrillation success in out-of-hospital cardiac arrested patients
Аутори
Ivanović, Marija D.Ring, Matthias
Baronio, Fabio
Calza, Stefano
Vukčević, Vladan
Hadžievski, Ljupčo
Maluckov, Aleksandra
Eskofier, Bjoern Michael
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Objective: Algorithms to predict shock outcome based on ventricular fibrillation (VF) waveform features are potentially useful tool to optimize defibrillation strategy (immediate defibrillation versus cardiopulmonary resuscitation). Researchers have investigated numerous predictive features and classification methods using single VF feature and/or their combinations, however reported predictabilities are not consistent. The purpose of this study was to validate whether combining VF features can enhance the prediction accuracy in comparison to single feature. Approach: The analysis was performed in 3 stages: feature extraction, preprocessing and feature selection and classification. Twenty eight predictive features were calculated on 4s episode of the pre-shock VF signal. The preprocessing included instances normalization and oversampling. Seven machine learning algorithms were employed for selecting the best performin single feature and combination of features using wrapper method: Log...istic Regression (LR), Naïve-Bayes (NB), Decision tree (C4.5), AdaBoost.M1 (AB), Support Vector Machine (SVM), Nearest Neighbour (NN) and Random Forest (RF). Evaluation of the algorithms was performed by nested 10 fold cross-validation procedure. Main results: A total of 251 unbalanced first shocks (195 unsuccessful and 56 successful) were oversampled to 195 instances in each class. Performance metric based on average accuracy of feature combination has shown that LR and NB exhibit no improvement, C4.5 and AB an improvement not greater than 1% and SVM, NN and RF an improvement greater than 5% in predicting defibrillation outcome in comparison to the best single feature. Significance: By performing wrapper method to select best performing feature combination the non-linear machine learning strategies (SVM, NN, RF) can improve defibrillation prediction performance. © 2018 IOP Publishing Ltd.
Кључне речи:
classification / ventricular fibrillation (VF) / defibrillation / shock outcome / feature selection / machine learning algorithmsИзвор:
Biomedical Physics and Engineering Express, 2018, 5, 1, 015012-Финансирање / пројекти:
- Capturing and quantitative analysis of multi-scale multi-channel diagnostic data. (EU-691051)
- Фотоника микро и нано структурних материјала (RS-45010)
- German Research Foundation within the framework of the Heisenberg professorship programme [ES 434/8-1]
- Federal Ministry of Education and Research of Germany [01IS17070]
DOI: 10.1088/2057-1976/aaebec
ISSN: 2057-1976
WoS: 000457627700012
Scopus: 2-s2.0-85062830869
URI
http://stacks.iop.org/2057-1976/5/i=1/a=015012?key=crossref.179380a6d1fbac3633b726787a95feb5https://vinar.vin.bg.ac.rs/handle/123456789/8095
Колекције
Институција/група
VinčaTY - JOUR AU - Ivanović, Marija D. AU - Ring, Matthias AU - Baronio, Fabio AU - Calza, Stefano AU - Vukčević, Vladan AU - Hadžievski, Ljupčo AU - Maluckov, Aleksandra AU - Eskofier, Bjoern Michael PY - 2018 UR - http://stacks.iop.org/2057-1976/5/i=1/a=015012?key=crossref.179380a6d1fbac3633b726787a95feb5 UR - https://vinar.vin.bg.ac.rs/handle/123456789/8095 AB - Objective: Algorithms to predict shock outcome based on ventricular fibrillation (VF) waveform features are potentially useful tool to optimize defibrillation strategy (immediate defibrillation versus cardiopulmonary resuscitation). Researchers have investigated numerous predictive features and classification methods using single VF feature and/or their combinations, however reported predictabilities are not consistent. The purpose of this study was to validate whether combining VF features can enhance the prediction accuracy in comparison to single feature. Approach: The analysis was performed in 3 stages: feature extraction, preprocessing and feature selection and classification. Twenty eight predictive features were calculated on 4s episode of the pre-shock VF signal. The preprocessing included instances normalization and oversampling. Seven machine learning algorithms were employed for selecting the best performin single feature and combination of features using wrapper method: Logistic Regression (LR), Naïve-Bayes (NB), Decision tree (C4.5), AdaBoost.M1 (AB), Support Vector Machine (SVM), Nearest Neighbour (NN) and Random Forest (RF). Evaluation of the algorithms was performed by nested 10 fold cross-validation procedure. Main results: A total of 251 unbalanced first shocks (195 unsuccessful and 56 successful) were oversampled to 195 instances in each class. Performance metric based on average accuracy of feature combination has shown that LR and NB exhibit no improvement, C4.5 and AB an improvement not greater than 1% and SVM, NN and RF an improvement greater than 5% in predicting defibrillation outcome in comparison to the best single feature. Significance: By performing wrapper method to select best performing feature combination the non-linear machine learning strategies (SVM, NN, RF) can improve defibrillation prediction performance. © 2018 IOP Publishing Ltd. T2 - Biomedical Physics and Engineering Express T1 - ECG derived feature combination versus single feature in predicting defibrillation success in out-of-hospital cardiac arrested patients VL - 5 IS - 1 SP - 015012 DO - 10.1088/2057-1976/aaebec ER -
@article{ author = "Ivanović, Marija D. and Ring, Matthias and Baronio, Fabio and Calza, Stefano and Vukčević, Vladan and Hadžievski, Ljupčo and Maluckov, Aleksandra and Eskofier, Bjoern Michael", year = "2018", abstract = "Objective: Algorithms to predict shock outcome based on ventricular fibrillation (VF) waveform features are potentially useful tool to optimize defibrillation strategy (immediate defibrillation versus cardiopulmonary resuscitation). Researchers have investigated numerous predictive features and classification methods using single VF feature and/or their combinations, however reported predictabilities are not consistent. The purpose of this study was to validate whether combining VF features can enhance the prediction accuracy in comparison to single feature. Approach: The analysis was performed in 3 stages: feature extraction, preprocessing and feature selection and classification. Twenty eight predictive features were calculated on 4s episode of the pre-shock VF signal. The preprocessing included instances normalization and oversampling. Seven machine learning algorithms were employed for selecting the best performin single feature and combination of features using wrapper method: Logistic Regression (LR), Naïve-Bayes (NB), Decision tree (C4.5), AdaBoost.M1 (AB), Support Vector Machine (SVM), Nearest Neighbour (NN) and Random Forest (RF). Evaluation of the algorithms was performed by nested 10 fold cross-validation procedure. Main results: A total of 251 unbalanced first shocks (195 unsuccessful and 56 successful) were oversampled to 195 instances in each class. Performance metric based on average accuracy of feature combination has shown that LR and NB exhibit no improvement, C4.5 and AB an improvement not greater than 1% and SVM, NN and RF an improvement greater than 5% in predicting defibrillation outcome in comparison to the best single feature. Significance: By performing wrapper method to select best performing feature combination the non-linear machine learning strategies (SVM, NN, RF) can improve defibrillation prediction performance. © 2018 IOP Publishing Ltd.", journal = "Biomedical Physics and Engineering Express", title = "ECG derived feature combination versus single feature in predicting defibrillation success in out-of-hospital cardiac arrested patients", volume = "5", number = "1", pages = "015012", doi = "10.1088/2057-1976/aaebec" }
Ivanović, M. D., Ring, M., Baronio, F., Calza, S., Vukčević, V., Hadžievski, L., Maluckov, A.,& Eskofier, B. M.. (2018). ECG derived feature combination versus single feature in predicting defibrillation success in out-of-hospital cardiac arrested patients. in Biomedical Physics and Engineering Express, 5(1), 015012. https://doi.org/10.1088/2057-1976/aaebec
Ivanović MD, Ring M, Baronio F, Calza S, Vukčević V, Hadžievski L, Maluckov A, Eskofier BM. ECG derived feature combination versus single feature in predicting defibrillation success in out-of-hospital cardiac arrested patients. in Biomedical Physics and Engineering Express. 2018;5(1):015012. doi:10.1088/2057-1976/aaebec .
Ivanović, Marija D., Ring, Matthias, Baronio, Fabio, Calza, Stefano, Vukčević, Vladan, Hadžievski, Ljupčo, Maluckov, Aleksandra, Eskofier, Bjoern Michael, "ECG derived feature combination versus single feature in predicting defibrillation success in out-of-hospital cardiac arrested patients" in Biomedical Physics and Engineering Express, 5, no. 1 (2018):015012, https://doi.org/10.1088/2057-1976/aaebec . .