Platform for detecting, managing, and manipulating characteristic points of the ECG waves through continuous wavelet...

Area of Science:

• Biomedical Engineering
• Signal Processing
• Medical Informatics

Background:

• Electrocardiogram (ECG) analysis is crucial for diagnosing cardiac conditions.
• Accurate detection and delineation of ECG waveform components (QRS, P, T waves) are essential for reliable interpretation.
• Existing methods can be susceptible to noise, artifacts, and limitations in managing diverse recording lengths.

Purpose of the Study:

• To develop and present open-source software for automated detection and delineation of characteristic points in ECG recordings.
• To enhance the management of long- and short-term ECG data by minimizing detection errors.
• To provide a user-friendly tool with manual correction and visualization capabilities for improved ECG analysis.

Main Methods:

• Implementation of detection and delineation algorithms for QRS complexes, P waves, and T waves.
• Utilization of continuous wavelet transform with splines for signal processing.
• Integration of an SQL Server database for efficient data management.
• Optimization techniques to reduce processing time.

Main Results:

• Validated QRS detection algorithm achieved high accuracy (Se = 99.91%, P+= 99.62%) on standard ECG databases (MIT-BIH, European ST-T, QT).
• Delineation algorithms for QRS, P, and T waves demonstrated low mean errors (under 8 ms) and standard deviations (under 24 ms) compared to expert annotations.
• The software enables simultaneous visualization and manual correction of detected ECG points.

Conclusions:

• The developed open-source software offers a robust and accurate solution for ECG waveform analysis.
• The tool effectively addresses challenges posed by noise and artifacts, enhancing the reliability of ECG interpretation.
• This software has the potential to significantly improve the efficiency and accuracy of managing and analyzing both long- and short-term ECG recordings.