Artificial intelligence to diagnose paroxysmal supraventricular tachycardia using electrocardiography during normal sinus rhythm. Issue 2 (9th February 2021)

• Record Type: Journal Article
• Authors: Jo, Yong-Yeon; Kwon, Joon-Myoung; Jeon, Ki-Hyun; Cho, Yong-Hyeon; Shin, Jae-Hyun; Lee, Yoon-Ji; Jung, Min-Seung; Ban, Jang-Hyeon; Kim, Kyung-Hee; Lee, Soo Youn; Park, Jinsik; Oh, Byung-Hee
• Is Part Of: European heart journal. Volume 2:Issue 2(2021)
• Page Start: 290
• Page End: 298
• Publication Date: 2021-02-09
• Subjects: Tachycardia -- Supraventricular -- Deep learning -- Electrocardiography -- Artificial intelligence; Medical informatics -- Periodicals; Medical technology -- Periodicals; Cardiovascular system -- Periodicals; 616.100284
• DOI: 10.1093/ehjdh/ztab025
• Languages: English
• ISSNs: 2634-3916

Abstract

Abstract: Aims: Paroxysmal supraventricular tachycardia (PSVT) is not detected owing to its paroxysmal nature, but it is associated with the risk of cardiovascular disease and worsens the patient quality of life. A deep learning model (DLM) was developed and validated to identify patients with PSVT during normal sinus rhythm in this multicentre retrospective study. Methods and results: This study included 12 955 patients with normal sinus rhythm, confirmed by a cardiologist. A DLM was developed using 31 147 electrocardiograms (ECGs) of 9069 patients from one hospital. We conducted an accuracy test with 13 753 ECGs of 3886 patients from another hospital. The DLM was developed based on residual neural network. Digitally stored ECG were used as predictor variables and the outcome of the study was ability of the DLM to identify patients with PSVT using an ECG during sinus rhythm. We employed a sensitivity map method to identify an ECG region that had a significant effect on developing PSVT. During accuracy test, the area under the receiver operating characteristic curve of a DLM using a 12-lead ECG for identifying PSVT patients during sinus rhythm was 0.966 (0.948–0.984). The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of DLM were 0.970, 0.868, 0.972, 0.255, and 0.998, respectively. The DLM showed delta wave and QT interval were important to identify the PSVT. Conclusion: The proposed DLM demonstrated a high performance in identifying PSVT during normal sinus rhythm. Thus, it can be used as a rapid, inexpensive, point-of-care means of identifying PSVT in patients. Graphical Abstract: