Ventricular Fibrillation and Tachycardia Detection Using Features Derived from Topological Data Analysis

Topological Data Analysis in Cardiovascular Signals: An Overview

Topological data analysis (TDA) is a recent approach for analyzing and interpreting complex data sets based on ideas a branch of mathematics called algebraic topology. TDA has proven useful to disentangle non-trivial data structures in a broad range of data analytics problems including the study of cardiovascular signals. Here, we aim to provide an overview of the application of TDA to cardiovascular signals and its potential to enhance the understanding of cardiovascular diseases and their treatment in the form of a literature or narrative review. We first introduce the concept of TDA and its key techniques, including persistent homology, Mapper, and multidimensional scaling. We then discuss the use of TDA in analyzing various cardiovascular signals, including electrocardiography, photoplethysmography, and arterial stiffness. We also discuss the potential of TDA to improve the diagnosis and prognosis of cardiovascular diseases, as well as its limitations and challenges. Finally, we outline future directions for the use of TDA in cardiovascular signal analysis and its potential impact on clinical practice. Overall, TDA shows great promise as a powerful tool for the analysis of complex cardiovascular signals and may offer significant insights into the understanding and management of cardiovascular diseases.

Efficient Extraction of Deep Image Features Using a Convolutional Neural Network (CNN) for Detecting Ventricular Fibrillation and Tachycardia

To safely select the proper therapy for ventricular fibrillation (VF), it is essential to distinguish it correctly from ventricular tachycardia (VT) and other rhythms. Provided that the required therapy is not the same, an erroneous detection might lead to serious injuries to the patient or even cause ventricular fibrillation (VF). The primary innovation of this study lies in employing a CNN to create new features. These features exhibit the capacity and precision to detect and classify cardiac arrhythmias, including VF and VT. The electrocardiographic (ECG) signals utilized for this assessment were sourced from the established MIT-BIH and AHA databases. The input data to be classified are time-frequency (tf) representation images, specifically, Pseudo Wigner-Ville (PWV). Previous to Pseudo Wigner-Ville (PWV) calculation, preprocessing for denoising, signal alignment, and segmentation is necessary. In order to check the validity of the method independently of the classifier, four different CNNs are used: InceptionV3, MobilNet, VGGNet and AlexNet. The classification results reveal the following values: for VF detection, there is a sensitivity (Sens) of 98.16%, a specificity (Spe) of 99.07%, and an accuracy (Acc) of 98.91%; for ventricular tachycardia (VT), the sensitivity is 90.45%, the specificity is 99.73%, and the accuracy is 99.09%; for normal sinus rhythms, sensitivity stands at 99.34%, specificity is 98.35%, and accuracy is 98.89%; finally, for other rhythms, the sensitivity is 96.98%, the specificity is 99.68%, and the accuracy is 99.11%. Furthermore, distinguishing between shockable (VF/VT) and non-shockable rhythms yielded a sensitivity of 99.23%, a specificity of 99.74%, and an accuracy of 99.61%. The results show that using tf representations as a form of image, combined in this case with a CNN classifier, raises the classification performance above the results in previous works. Considering that these results were achieved without the preselection of ECG episodes, it can be concluded that these features may be successfully introduced in Automated External Defibrillation (AED) and Implantable Cardioverter Defibrillation (ICD) therapies, also opening the door to their use in other ECG rhythm detection applications.

Accurate Prediction of Sudden Cardiac Death Based on Heart Rate Variability Analysis Using Convolutional Neural Network

Sudden cardiac death (SCD) is a significant global health issue that affects individuals with and without a history of heart disease. Early identification of SCD risk factors is crucial in reducing mortality rates. This study aims to utilize electrocardiogram (ECG) tools, specifically focusing on heart rate variability (HRV), to detect early SCD risk factors. In this study, we expand the comparison group dataset to include five groups: Normal Sinus Rhythm (NSR), coronary artery disease (CAD), Congestive Heart Failure (CHF), Ventricular Tachycardia (VT), and SCD. ECG signals were recorded for 30 min and segmented into 5 min intervals, following the recommended HRV feature analysis guidelines. We introduce an innovative approach to HRV signal analysis by utilizing Convolutional Neural Networks (CNN). The CNN model was optimized by tuning hyperparameters such as the number of layers, learning rate, and batch size, significantly impacting the prediction accuracy. The findings demonstrate that the HRV approach, in conjunction with linear features and the DL method, achieved a higher accuracy rate, averaging 99.30%, reaching 97% sensitivity, 99.60% specificity, and 97.87% precision. Future research should focus on further exploring and refining DL methods in the context of HRV analysis to improve SCD prediction.