The proposed shock advice algorithm using a convolutional neural network feature extractor and a Boosting classifier achieved an accuracy of 99.26%, sensitivity of 97.07%, and specificity of 99.44% for detecting shockable rhythms.
Does a novel algorithm using CNNE and a Boosting classifier accurately detect shockable rhythms on ECG signals?
A novel deep learning-based shock advice algorithm demonstrates high accuracy, sensitivity, and specificity for detecting shockable rhythms in AEDs.
Absolute Event Rate: 99.26% vs 98.36%
Ventricular fibrillation and ventricular tachycardia (VF/VT), known as shockable (SH) rhythms, are the mainly cause of sudden cardiac arrests (SCA), which is cured efficiently by the automated external defibrillator (AED). The performance of the shock advice algorithm (SAA) applied in the AED has been improved by using machine learning technique and variously conventional features, recently. In this paper, we propose a novel algorithm with relatively high performance for the SCA detection on electrocardiogram (ECG) signal. The algorithm consists of a convolutional neural network as a feature extractor (CNNE) and a Boosting (BS) classifier. A grid search with nested 5-folds cross validation (CV) is used to select the CNNE trained with preprocessed ECG, SH, and NSH signals using the modified variational mode decomposition technique. The deep feature vector learned by this CNNE is extracted at the first fully connected layer and then fed into BS classifier to validate its performance using 5-folds CV procedure. The secondary learning of the BS classifier and the use of three input channels for the CNNE improve certainly the detection performance of the proposed SAA with the validated accuracy of 99.26%, sensitivity of 97.07%, and specificity of 99.44%.
Nguyen et al. (Thu,) conducted a other in Sudden cardiac arrest (ventricular fibrillation and ventricular tachycardia) (n=57). Proposed Shock Advice Algorithm (SAA) using CNNE and Boosting classifier vs. Full CNN model (fCNN) and other machine learning classifiers was evaluated on Classification accuracy for shockable vs non-shockable rhythms. The proposed shock advice algorithm using a convolutional neural network feature extractor and a Boosting classifier achieved an accuracy of 99.26%, sensitivity of 97.07%, and specificity of 99.44% for detecting shockable rhythms.