Diagnosis of Arrhythmia Using Neural Networks

This dissertation presents an intelligent framework for classification of heart arrhythmias. It is a framework of cascaded discrete wavelet transform and the Fourier transform as preprocessing stages for the neural network. This work exploits the information about heart activity contained in the ECG signal; the power of the wavelet and Fourier transforms in characterizing the signal and the power learningpower of neural networks. Firstly, the ECG signals are four-level discrete wavelet decomposed using a filter-bank and mother wavelet 'db2'. Then all the detailed coefficients were discarded, while retaining only the approximation coefficients at the fourth level. The retained approximation coefficients are Fourier transformed using a 16-point FFT. The FFT is symmetrical, therefore only the first 8-points are sufficient to characterize the spectrum. The last 8-points resulting from theFFTare discarded during feature selection. The 8-point feature vector is then used to train a feedforward neural network with one hidden layer of 20-units and three outputs. The neural network is trained by using the Scaled Conjugate Gradient Backpropagation algorithm (SCG). This was implemented in a MATLAB environment using the MATLAB GUINeural networktoolbox.. This approach yields an accuracy of 94.66% over three arrhythmia classes, namely the Ventricular Flutter (VFL), the Ventricular Tachycardia (VT) and the Supraventricular Tachyarrhythmia (SVTA). We conclude that for the amount of information retained and the number features used the performance is fairly competitive

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.N/

Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation

This work presents the application of machine learning techniques to analyze the influence of physical exercise in the heart's physiological properties, during ventricular fibrillation. With that purpose, different kinds of classifiers (linear and neural models) were used to classify between trained and sedentary rabbit hearts. These classifiers were used to perform knowledge extraction through a wrapper feature selection algorithm. The obtained results showed the higher performance of the neural models compared to the linear classifier (higher performance measures and higher dimensionality reduction). The most relevant features to describe the benefits of physical exercise are those related to myocardial heterogeneity, mean activation rate and activation complexity

Design of an artificial neural network and feature extraction to identify arrhythmias from ECG

This paper presents a design of an artificial neural network (ANN) and feature extraction methods to identify two types of arrhythmias in datasets obtained through electrocardiography (ECG) signals, namely arrhythmia dataset (AD) and supraventricular arrhythmia dataset (SAD). No special ANN toolkit was used; instead, each neuron and necessary calculus were modeled and individually programmed. Thus, four temporal-based features are used: heart rate (HR), R-peaks root mean square (R-RMS), RR-peaks variance (RR-VAR), and QSR-complex standard deviation (QSR-SD). The network architecture presents four neurons in the input layer, eight in hidden layer and an output layer with two neurons. The proposed classification method uses the MIT-BIH Dataset (Massachusetts Institute of Technology-Beth Israel Hospital) for training, validation and execution or test phases. Preliminary results show the high efficiency of the proposed ANN design and its classification method, reaching accuracies between 98.76% and 98.91%, when in the identification of NSRD and arrhythmic ECG; and accuracies of 86.37% (AD) and 76.35% (SAD), when analyzing only classifications between both arrhythmias.info:eu-repo/semantics/acceptedVersio

Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review.

Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned

Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains