Numerical Data Extraction from ECG Paper Recording Using Image Processing Technique

Tabassum, Tahmida; Ahmad, Mohiuddin

doi:10.1109/icece51571.2020.9393068

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…L = ld × 25.4/600 (5) where l is the length in millimeters and ld is the length in dots per inch (1 inch equals 25.4 mm) at 600 dots per inch (dpi), and each 1 dot corresponds to 0.0423 mm. It should be noted that, in the ECG/TMT-ECG graph, the grid unit along the amplitude axis corresponds to 0.1 mV and along the time series axis corresponds to 0.02 s [56]. The extracted data of the entire 12-lead plots are compared with the ECG graph of the original 12-lead images, as shown in Figure 7.…”

Section: Data Storagementioning

confidence: 99%

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Development of a Convolutional Neural Network Model to Predict Coronary Artery Disease Based on Single-Lead and Twelve-Lead ECG Signals

et al. 2022

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Coronary artery disease (CAD) is one of the most common causes of heart ailments; many patients with CAD do not exhibit initial symptoms. An electrocardiogram (ECG) is a diagnostic tool widely used to capture the abnormal activity of the heart and help with diagnoses. Assessing ECG signals may be challenging and time-consuming. Identifying abnormal ECG morphologies, especially in low amplitude curves, may be prone to error. Hence, a system that can automatically detect and assess the ECG and treadmill test ECG (TMT-ECG) signals will be helpful to the medical industry in detecting CAD. In the present work, we developed an intelligent system that can predict CAD, based on ECG and TMT signals more accurately than any other system developed thus far. The distinct convolutional neural network (CNN) architecture deals with single-lead and multi-lead (12-lead) ECG and TMT-ECG data effectively. While most artificial intelligence-based systems rely on the universal dataset, the current work used clinical lab data collected from a renowned hospital in the neighborhood. ECG and TMT-ECG graphs of normal and CAD patients were collected in the form of scanned reports. One-dimensional ECG data with all possible features were extracted from the scanned report with the help of a modified image processing method. This feature extraction procedure was integrated with the optimized architecture of the CNN model leading to a novel prediction system for CAD. The automated computer-assisted system helps in the detection and medication of CAD with a high prediction accuracy of 99%.

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Section: Data Storagementioning

confidence: 99%

“…The commonly used performance metrics were accuracy (Equation ( 7)), sensitivity (Equation ( 8)), f1 score (Equation ( 9)), specificity (Equation ( 10)), and precision (Equation ( 11)). [7,8,56].…”

Section: Performance Metrics Evaluationmentioning

confidence: 99%

Development of a Convolutional Neural Network Model to Predict Coronary Artery Disease Based on Single-Lead and Twelve-Lead ECG Signals

et al. 2022

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“…This digital data can be then analyzed with a machine learning algorithm to detect heart abnormalities quickly. Previous studies have developed algorithms to convert paper data into 1D signals [15][16][17][18]. These algorithms, however, were either too complicated or inadequately conceived for our experimental data.…”

Section: Introductionmentioning

confidence: 99%

Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks

Nguyen

Pham

et al. 2022

PLoS ONE

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The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, raising the need to develop novel tools to provide rapid and cost-effective screening and diagnosis. Clinical reports indicated that COVID-19 infection may cause cardiac injury, and electrocardiograms (ECG) may serve as a diagnostic biomarker for COVID-19. This study aims to utilize ECG signals to detect COVID-19 automatically. We propose a novel method to extract ECG signals from ECG paper records, which are then fed into one-dimensional convolution neural network (1D-CNN) to learn and diagnose the disease. To evaluate the quality of digitized signals, R peaks in the paper-based ECG images are labeled. Afterward, RR intervals calculated from each image are compared to RR intervals of the corresponding digitized signal. Experiments on the COVID-19 ECG images dataset demonstrate that the proposed digitization method is able to capture correctly the original signals, with a mean absolute error of 28.11 ms. The 1D-CNN model (SEResNet18), which is trained on the digitized ECG signals, allows to identify between individuals with COVID-19 and other subjects accurately, with classification accuracies of 98.42% and 98.50% for classifying COVID-19 vs. Normal and COVID-19 vs. other classes, respectively. Furthermore, the proposed method also achieves a high-level of performance for the multi-classification task. Our findings indicate that a deep learning system trained on digitized ECG signals can serve as a potential tool for diagnosing COVID-19.

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Noise ECG generation method based on generative adversarial network

Huang

Wang

2023

Biomedical Signal Processing and Control

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Numerical Data Extraction from ECG Paper Recording Using Image Processing Technique

Cited by 5 publications

References 9 publications

Development of a Convolutional Neural Network Model to Predict Coronary Artery Disease Based on Single-Lead and Twelve-Lead ECG Signals

Development of a Convolutional Neural Network Model to Predict Coronary Artery Disease Based on Single-Lead and Twelve-Lead ECG Signals

Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks

Noise ECG generation method based on generative adversarial network

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