Machine Learning-based Efficient Ventricular Tachycardia Detection Model of ECG Signal

In primary diagnosis and analysis of heart defects, an ECG signal plays a significant role. This paper presents a model for the prediction of ventricular tachycardia arrhythmia using noise filtering, a unique set of ECG features, and a machine learning-based classifier model. Before signal feature extraction, we detrend and denoise the signal to eliminate the noise for detecting features properly. After that necessary features have been extracted and necessary parameters related to these features are measured. Using these parameters, we prepared one efficient multiclass classifier model using a machine learning approach that can classify different types of ventricular tachycardia arrhythmias efficiently. Our results indicate that Logistic regression and Decision tree-based models are the most efficient machine learning models for detecting ventricular tachycardia arrhythmia. In order to diagnose heart diseases and find care for a patient, an early, reliable diagnosis of different types of arrhythmia is necessary. By implementing our proposed method, this work deals with the problem of reducing the misclassification of the critical signal related to ventricular tachycardia very efficiently. Experimental findings demonstrate satisfactory enhancements and demonstrate high resilience to the algorithm that we have proposed. With this assistance, doctors can assess this type of arrhythmia of a patient early and take the right decision at the proper time.

Supraventricular Tachycardia Detection and Classification Model of ECG signal Using Machine Learning

Investigation on the electrocardiogram (ECG) signals is an essential way to diagnose heart disease since the ECG process is noninvasive and easy to use. This work presents a supraventricular arrhythmia prediction model consisting of a few stages, including filtering of noise, a unique collection of ECG characteristics, and automated learning classifying model to classify distinct types, depending on their severity. We de-trend and de-noise a signal to reduce noise to better determine functionality before extractions are performed. After that, we present one R-peak detection method and Q-S detection method as a part of necessary feature extraction. Next parameters are computed that correspond to these features. Using these characteristics, we have developed a classification model based on machine learning that can successfully categorize different types of supraventricular tachycardia. Our findings suggest that decision-tree-based models are the most efficient machine learning models for supraventricular tachycardia arrhythmia. Among all the machine learning models, this model most efficiently lowers the crucial signal misclassification of supraventricular tachycardia. Experimental results indicate satisfactory improvements and demonstrate a superior efficiency of the proposed approach with 97% accuracy.