•This paper presents a methodology based on the support vector machine (SVM) and adaboost (adaptive boosting algorithm) with the help of an optimal combination of features for detection of ventricular fibrillation (VF) rhythm by using the electrocardiogram (ECG) signal.•The proposed methodology implements a differential evolution algorithm with SVM and adaboost algorithm for selecting the best combination of features from the extracted 17 features.•The proposed methodology shows better accuracy, sensitivity, and specificity compared to other methodologies for detection of VF rhythm by using ECG signal for the window length of 5 s and 2 s. In this paper, the ventricular fibrillation (VF) rhythm is detected by using a new approach involving the support vector machine (SVM), adaptive boosting (AdaBoost) and differential evolution (DE) algorithms with the help of an optimal variable combination. The proposed methodology has been validated on training sets and testing sets that were obtained from three databases, namely MIT-BIH malignant ventricular arrhythmia database, arrhythmia database, and CUDB database. In the evaluation phase, the proposed methodology shows superior performance in detection of the VF rhythm than competing methods: an accuracy of 98.20%, a sensitivity of 98.25%, and specificity of 98.18% using 5 s of the ECG segments. Another advantage of our method is that it needs less memory and can be implemented in real-time. Display omitted