AI Article Synopsis
- The paper presents a new method for detecting and measuring QRS complexes in ECG images using convolutional neural networks (CNNs) rather than converting images to digital formats.
- The approach employs Faster-RCNN for detecting QRS complexes and a CNN for locating and measuring R-peaks directly from the images.
- Results showed impressive performance with a recall rate of 98.32%, precision of 99.01%, and a mean absolute error of 0.012 mv, outperforming traditional detection methods.
Article Abstract
In this paper, we propose an end-to-end approach to addressing QRS complex detection and measurement of Electrocardiograph (ECG) paper using convolutional neural networks (CNNs). Unlike conventional detection solutions that convert images to digital data, our method can directly detect QRS complex in images using Faster-RCNN, then the R-peak can be located and measured through a CNN. Validated by clinical ECG data in the St.-Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database and real ECG paper from Peking University People's Hospital, the proposed method can achieve the recall of 98.32%, the precision of 99.01% in detecting and 0.012 mv of mean absolute error in measuring. Experimental results demonstrate the superior performance of our method over conventional solutions, which would pave the way to detect and measure ECG paper using CNNs.
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| http://dx.doi.org/10.1109/EMBC.2018.8513132 | DOI Listing |
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