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 anal...

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Vydané v:Algorithms Ročník 18; číslo 11; s. 689
Hlavní autori: Jandera, Ales, Petryk, Yuliia, Muzelak, Martin, Skovranek, Tomas
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.11.2025
Predmet:
Algorithms
Automation
Cardiac arrhythmia
Cardiac function
ECG signal analysis
Electrocardiogram
Electrocardiography
Heart
Heart beat
Heart rate
Labels
MATLAB app designer
multi-class multi-label classification
Pan-Tompkins algorithm
Signal analysis
Signal processing
support vector machine algorithm
Support vector machines
Taxonomy
Wavelet transforms
ISSN:1999-4893, 1999-4893
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Shrnutí: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.
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ISSN:1999-4893
1999-4893
DOI:10.3390/a18110689