Key result
U-Net 3+ deep learning model using CWT scalograms achieves ~99% precision for R-peak detection.
Why the study?
ECG signals are critical for diagnosing cardiac diseases, but reliable segmentation-based detection of R-peak locations using deep learning methods needed investigation.
Does a U-Net-based deep learning architecture accurately detect R-peaks in ECG signals?
Does a U-Net-based deep learning architecture accurately detect R-peaks in ECG signals?
A U-Net 3+ deep learning model applied to CWT-based scalogram images of ECG signals achieves highly accurate R-peak detection.
Electrocardiography (ECG) is the recording of the electrical activity of the heart through electrodes. ECG signals are crucial in the early diagnosis of numerous cardiac diseases. Therefore, it is very important to read and analyze these signals using state-of-the-art technologies. The regular wave shapes in ECG data are frequently disturbed when certain heart diseases occur and these changes in signals help for detecting the disease. Signal processing and machine learning-based methods are widely used for this purpose. In recent years, deep learning-based methods have become widespread, and they offer promising results. This study aims to segmentation-based detection of R-peak locations in ECG signals. First, the ECG signal is transformed into a Continuous Wavelet Transform (CWT) based scalogram image, and then U-Net-based deep learning architectures are utilized for the segmentation. The comparisons are carried out on MIT -BIH Arrhythmia Database (MIT-DB). Whereas all methods provide promising results, U-Net 3+ model achieves 0.99 in Precision, 0.98 in Recall, 0.99 in F1 score, and 0.98 in Accuracy with the lowest parameter size.
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Çelik et al. (2022) studied Cardiac diseases (Arrhythmia). U-Net 3+ deep learning model vs. Other methods was evaluated on Detection of R-peak locations. A U-Net 3+ deep learning model using Continuous Wavelet Transform scalogram images achieved high performance for R-peak detection, with 0.99 Precision, 0.98 Recall, 0.99 F1 score, and 0.98 Accuracy.
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