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

Mjahad, Azeddine; Francés-Víllora, Jose V.; Bataller-Mompeán, Manuel; Muñoz, Andrés Pedreño

doi:10.3390/app12147248

Cited by 8 publications

(10 citation statements)

References 69 publications

(67 reference statements)

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“…In paper [21], two input data to the classifier are evaluated: TDA features and Persistence Diagram Image (PDI). Using the reduced TDA-obtained features, a high average accuracy near 99% was observed when discriminating four types of rhythms and specificity values higher than 97.16% in all cases.…”

Section: Discussionmentioning

confidence: 99%

Prediction and Detection of Ventricular Fibrillation Using Complex Features and AI-Based Classification

Fira,

Costin,

Goras

2024

Applied Sciences

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We analyzed the possibility of detecting and predicting ventricular fibrillation (VF), a medical emergency that may put people’s lives at risk, as the medical prognosis depends on the time in which medical personnel intervene. Therefore, besides immediate detection of VF, the possibility of predicting VF 40 or even 50 min in advance was analyzed. For testing the proposed algorithm, we used ECG signals taken from the MIT-BIH database, namely, Malignant Ventricular Ectopy Database, Sudden Cardiac Death Holter Database and Normal Sinus Rhythm Database. The presented method is based on features extracted from the ECG signals in the time domain, frequency domain and complexity measures. For VF detection, the possibility of identifying the VF episode in the first 3 s after its occurrence was tested. For this, the first 3 s immediately after the appearance of VF were cut out and the features were computed on these sections. For VF prediction, 3 min of the ECG signal clipped 40 or 50 min before VF onset was used. Then, on these pieces of ECG signal, the specific features were calculated for 1 s segments. For the normal signal situation, 3 min was randomly selected from the database with normal ECGs. For the classification or detection stage, both an MLP-type neural network and the classifiers from the Machine Learning toolbox of the MATLAB® environment were used. The results obtained for both detection and classification are over 94% in both cases. The novelty of our results compared to those previously obtained is the time interval with which the possibility of prediction was analyzed, namely, 50 min in advance of the VF installation date. This means that the patient will be informed that it is possible to suffer a VF and has time to take the necessary measures to overcome a possible medical emergency.

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Section: Discussionmentioning

confidence: 99%

Prediction and Detection of Ventricular Fibrillation Using Complex Features and AI-Based Classification

Fira,

Costin,

Goras

2024

Applied Sciences

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“…Xia et al [ 72 ] obtained high performance values (Sen = 98.15% and Spe = 96.01% for VF, and Sen = 96.01% and Spe = 98.15% for

) using Lempel–Ziv and Empirical Mode Decomposition (EMD) with selected clean episodes of

and

. Mjahad et al [ 73 ] achieved an accuracy, sensitivity, and specificity values of 98.68%, 92.72%, and 99.53%, respectively employing TDA. Kaur and Singh [ 74 ] used Empirical Mode Decomposition (EMD) and approximate entropy with selected

and

episodes from the MIT-BIH database, achieving moderate classification performance (Sen = 90.47%, Spe = 91.66%, and Acc = 91.17%).…”

Section: Discussionmentioning

confidence: 99%

“…This set of works primarily targets the implementation on external defibrillators (AEDs) and implantable cardioverter defibrillators (ICDs), distinguishing between shockable and non-shockable rhythms (considering both

and

as shockable). Mjahad et al [ 73 ] utilized TDA and obtained Sens = 99.03%, Spe = 99.67%, and Acc = 99.51% in discriminating

episodes. Acharya et al [ 76 ] proposed an eleven-layer convolutional neural network (CNN) for shockable and non-shockable arrhythmia classification, obtaining Sen = 95.32%, Spe = 91.04%, and Acc = 93.20%.…”

Section: Discussionmentioning

confidence: 99%

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

Mjahad,

Saban,

Azarmdel

et al. 2023

J. Imaging

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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.

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“…A similar approach was taken by Mjahad, et al [22], who applied TDA to generate novel features contributing to improve both detection and classification performance of cardiac arrhythmias such as Ventricular Fibrillation (VF) and Ventricular Tachycardia (VT). The electrocardiographic (ECG) signals used for this evaluation were obtained from the standard MIT-BIH and AHA databases.…”

Section: Ecg Data and Heart Rate Signalsmentioning

confidence: 99%

Topological Data Analysis in Cardiovascular Signals: An Overview

Hernández-Lemus,

Miramontes,

Martínez-García

2024

Entropy

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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.

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

Cited by 8 publications

References 69 publications

Prediction and Detection of Ventricular Fibrillation Using Complex Features and AI-Based Classification

Prediction and Detection of Ventricular Fibrillation Using Complex Features and AI-Based Classification

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

Topological Data Analysis in Cardiovascular Signals: An Overview

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