Key result
Proposed algorithm yields high sensitivity and specificity for detecting single ventricular extrasystoles on ECG.
Why the study?
Existing mobile arrhythmia diagnostic systems do not meet modern requirements, creating a need for simple, real-time algorithms to recognize and classify ventricular arrhythmias on mobile devices.
A newly developed algorithm implemented in Labview provides a simple, real-time method for detecting ventricular extrasystoles and heart rate variability with high sensitivity and specificity.
May support automated PVC detection in select ECGs; leaves open validation in diverse clinical settings.
Determining the boundaries of QRS complexes in ECG signals is of fundamental importance in the diagnosis of arrhythmias and in the recognition of signals in clinical manifestations in general. Existing systems of mobile diagnostics of arrhythmias do not meet modern requirements: some algorithms are too complicated for software implementation, others are not sufficiently resistant to interference. Ventricular extrasystoles, as one of the most important studies in arrhythmology, are currently considered one of the main causes of sudden cardiac death, and their timely detection and objective diagnosis, as well as proper prevention, remain one of the most pressing issues. One of the urgent issues is the development of simple (acceptable for mobile devices – which can be implemented online) algorithms for the recognition and classification of ventricular arrhythmias. The aim of the work is to develop a system for determining the informative parameters of the ECG signal in real time for the diagnosis of ventricular extrasystoles and parameters of heart rate variability. An algorithm for recognition of arrhythmias (ventricular extrasystoles) is proposed, which is characterized by ease of implementation and minimal requirements for computing resources, while maintaining high values of sensitivity and specificity for ECG signals with sinus rhythm and single ventricular extrasystoles. The algorithm is implemented in the Labview software environment. Using ECG files taken from international databases, the algorithm and system were tested to determine the parameters of heart rate variability.
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Shafag Samadova (2023) studied Ventricular arrhythmias. Algorithm for recognition of ventricular extrasystoles was evaluated on Sensitivity and specificity for recognizing ventricular extrasystoles. A proposed algorithm for recognizing ventricular extrasystoles demonstrated high sensitivity and specificity for ECG signals with sinus rhythm and single ventricular extrasystoles.
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