Key result
An automated classification model using OTSU and CNN achieved an accuracy of 98.25% on the MIT-BIH dataset and 89.33% on clinical scanned ECG images for detecting premature ventricular contractions.
Why the study?
Patients cannot easily decipher symptoms to determine premature ventricular contraction severity, creating a need for automated methods to extract and classify heartbeats from scanned clinical ECG reports lacking digital data.
Does an automated classification model using OTSU and CNN accurately detect premature ventricular contractions from scanned ECG images?
Does an automated classification model using OTSU and CNN accurately detect premature ventricular contractions from scanned ECG images?
An automated model combining OTSU and CNN methods can accurately extract ECG curves from scanned clinical reports and detect premature ventricular contractions.
May support automated PVC screening in research; leaves open prospective clinical validation before any practice change.
Premature ventricular contraction (PVC) is one of the most common arrhythmias which can cause palpitation, cardiac arrest, and other symptoms affecting the work and rest activities of a patient. However, patients hardly decipher their own feelings to determine the severity of the disease thus, requiring a professional medical diagnosis. This study proposes a novel method based on image processing and convolutional neural network (CNN) to extract electrocardiography (ECG) curves from scanned ECG images derived from clinical ECG reports, and segment and classify heartbeats in the absence of a digital ECG data. The ECG curve is extracted using a comprehensive algorithm that combines the OTSU algorithm with erosion and dilation. This algorithm can efficiently and accurately separate the ECG curve from the ECG background grid. The performance of the classification model was evaluated and optimized using hundreds of clinical ECG data collected from Fujian Provincial Hospital. Additionally, thousands of clinical ECG reports were scanned to digital images as the test set to confirm the accuracy of the algorithm for practical application. Results showed that the average sensitivity, specificity, positive predictive value, and accuracy of the proposed model on the MIT-BIH dataset were 95.47%, 97.72%, 98.75%, and 98.25%, respectively. The classification average sensitivity, specificity, positive predictive value, and accuracy based on clinical scanned ECG images can reach to 97.24%, 81.6%, 83.8%, and 89.33%, respectively, and the clinical feasibility is high. Overall, the proposed method can extract ECG curves from scanned ECG images efficiently and accurately. Furthermore, it performs well on heartbeat classification of normal (N) and ventricular premature heartbeat.
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Wang et al. (2021) studied Premature ventricular contraction (PVC). Automated classification model with OTSU and CNN method was evaluated on Accuracy of heartbeat classification on clinical scanned ECG images. An automated classification model using OTSU and CNN achieved an accuracy of 98.25% on the MIT-BIH dataset and 89.33% on clinical scanned ECG images for detecting premature ventricular contractions.
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