ECG Signal Analysis and Abnormality Detection Application

The electrocardiogram (ECG) signal carries information crucial for health assessment, but its analysis can be challenging due to noise and signal variability; therefore, automated processing focused on noise removal and detection of key features is necessary. This paper introduces an ECG signal analysis and abnormality detection application developed to process single-lead ECG signals. In this study, the Lobachevsky University database (LUDB) was used as the source of ECG signals, as it includes annotated recordings using a multi-class, multi-label taxonomy that covers several diagnostic categories, each with specific diagnoses that reflect clinical ECG interpretation practices. The main aim of the paper is to provide a tool that efficiently filters noisy ECG data, accurately detects the QRS complex, PQ and QT intervals, calculates heart rate, and compares these values with normal ranges based on age and gender. Additionally, a multi-class, multi-label SVM-based model was developed and integrated into the application for heart abnormality diagnostics, i.e., assigning one or several diagnoses from various diagnostic categories. The MATLAB-based application is capable of processing raw ECG signals, allowing the use of ECG records not only from LUDB but also from other databases.