Detection of irregular QRS complexes using Hermite Transform and Support Vector Machine

TL;DR

This paper presents a method combining Hermite transform and Support Vector Machine to automatically detect irregular QRS complexes in ECG signals, aiming to improve diagnostic speed and accuracy in cardiology.

Contribution

It introduces a novel feature extraction approach using Hermite transform for SVM-based ECG classification of irregular QRS complexes.

Findings

Hermite transform effectively represents QRS complexes.

SVM achieves high accuracy in detecting irregular QRS complexes.

Reduced false diagnoses in ECG analysis.

Abstract

Computer based recognition and detection of abnormalities in ECG signals is proposed. For this purpose, the Support Vector Machines (SVM) are combined with the advantages of Hermite transform representation. SVM represent a special type of classification techniques commonly used in medical applications. Automatic classification of ECG could make the work of cardiologic departments faster and more efficient. It would also reduce the number of false diagnosis and, as a result, save lives. The working principle of the SVM is based on translating the data into a high dimensional feature space and separating it using a linear classificator. In order to provide an optimal representation for SVM application, the Hermite transform domain is used. This domain is proved to be suitable because of the similarity of the QRS complex with Hermite basis functions. The maximal signal information is obtained using a small set of features that are used for detection of irregular QRS complexes. The aim of the paper is to show that these features can be employed for automatic ECG signal analysis.