Key result
An ECG analysis toolbox and new feature extraction method using wavelet transform increased the accuracy and reliability of ECG main wave detection and highly fastened the process duration compared to manual analysis.
Why the study?
ECG signals are the main diagnostic source for many cardiovascular diseases, making accurate ECG signal analysis essential for correct diagnosis.
Does the new ECG analysis toolbox improve the accuracy and speed of ECG main wave detection compared to manual analysis?
Does the new ECG analysis toolbox improve the accuracy and speed of ECG main wave detection compared to manual analysis?
A newly developed ECG analysis toolbox using wavelet transform and scalogram analysis improves the accuracy and speed of ECG wave detection, offering novel features for AI applications.
May expedite ECG analysis in research; leaves open clinical validation and AI integration.
Electrocardiography (ECG) signals and the information obtained through the analysis of these signals constitute the main source of diagnosis for many cardiovascular system diseases. Therefore, accurate analyses of ECG signals are very important for correct diagnosis. In this study, an ECG analysis toolbox together with a user-friendly graphical user interface, which contains the all ECG analysis steps between the recording unit and the statistical investigation, is developed. Furthermore, a new feature calculation methodology is proposed for ECG analysis, which carries distinct information than amplitudes and durations of ECG main waves and can be used in artificial intelligence studies. Developed toolbox is tested using both Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia ECG Database and an experimentally collected dataset for performance evaluation. The results show that ECG analysis toolbox presented in this study increases the accuracy and reliability of the ECG main wave detection analysis, highly fasten the process duration compared to manual ones and the new feature set can be used as a new parameter for decision support systems about ECG based on artificial intelligence.
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Yanık et al. (2020) studied Cardiovascular system diseases. ECG analysis toolbox and new feature calculation methodology vs. Manual analysis was evaluated on Accuracy, reliability, and process duration of ECG main wave detection analysis. An ECG analysis toolbox and new feature extraction method using wavelet transform increased the accuracy and reliability of ECG main wave detection and highly fastened the process duration compared to manual analysis.
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