Key result
An artificial intelligence algorithm using Support Vector Machine achieved 99.89% accuracy in predicting chronic thromboembolic pulmonary hypertension from 12-lead ECG signals.
Why the study?
Screening and accurate diagnosis of CTEPH are critical to manage progression and prevent mortality, but tools for this purpose are lacking.
Does an artificial intelligence algorithm using 12-lead electrocardiography accurately predict chronic thromboembolic pulmonary hypertension?
Cross-Sectional (n=77)
Yes
Does an artificial intelligence algorithm using 12-lead electrocardiography accurately predict chronic thromboembolic pulmonary hypertension?
An AI algorithm using 12-lead ECG signals demonstrated high accuracy (up to 99.89%) in predicting chronic thromboembolic pulmonary hypertension.
Background: Screening and accurate diagnosis of chronic thromboembolic pulmonary hypertension(CTEPH) are critical for managing the progression and preventing associated mortality; however, there are no tools for this purpose. We developed and validated an artificial intelligence (AI) algorithm for predicting CTEPH using electrocardiography (ECG). Methods: ECG signals were obtained from 54 regular and 23 CTEPH patients to test the technique. A dataset was created by converting the ECG results to digital. The 12-channel ECG signal received from 77 individuals is 924x1300 in size. The end-point was the diagnosis of CTEPH. By applying the suggested Nigerian motif pattern method to this data set, we obtained a feature matrix of 924x15010. FSCmRMR algorithm determined the most influential 947 characteristics among 15010 features and obtained a matrix of 924x947. We used a decision tree, SVM(Support Vector Machine), and KNN(K-Nearest Neighbour )algorithms to classify the selected most weighted features. Results: We achieved 98.05% success with the decision tree algorithm, 99.89% with the SVM algorithm, and 99.67% with the KNN algorithm. AI algorithm focused on each patient's S-wave, P-wave, and T-wave by QRS complex characteristics. Conclusion: The AI algorithm demonstrated high accuracy for CTEPH prediction using 12-lead and single-lead ECGs.
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Kocabay et al. (2022) conducted a cross-sectional in Chronic Thromboembolic Pulmonary Hypertension (n=77). Artificial intelligence algorithm using 12-lead ECG vs. Healthy controls was evaluated on Accuracy of CTEPH prediction (99.89% accuracy). An artificial intelligence algorithm using Support Vector Machine achieved 99.89% accuracy in predicting chronic thromboembolic pulmonary hypertension from 12-lead ECG signals.
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