Machine Learning-based Efficient Ventricular Tachycardia Detection Model of ECG Signal
This work addresses the need for early and reliable diagnosis of ventricular tachycardia arrhythmia to assist doctors in timely patient care, but it appears incremental as it applies existing machine learning methods to this specific medical domain.
The paper tackled the problem of detecting ventricular tachycardia arrhythmia from ECG signals by developing a machine learning model that uses noise filtering and feature extraction, achieving satisfactory enhancements and high resilience in classification.
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.