Early detection of myocardial ischemia in resting ECG: analysis by HHT

Wang, Chunlin; Wei, Chiu‐Chi; Tsai, Cheng‐Ting; Lee, Ying‐Hsiang; Liu, Lawrence Yu‐Min; Chen, Kang-Ying; Lin, Yu-Jen; Lin, Po-Lin

doi:10.1186/s12938-023-01089-9

Cited by 1 publication

(2 citation statements)

References 35 publications

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“…In contrast, the DL-based ECG model could contribute to automated ECG interpretation and risk quantification. Furthermore, while ECG may be a poor predictor from the perspective of the human eye and computer-assisted ECG features provided by GE machine, ECG characteristics have been demonstrated to be related to the prediction of ObCAD [ 31 , 32 , 34 – 36 ]. Previous literature reported that the resting ECG could not properly predict ObCAD based on the cardiologist’s interpretation of the ST segment, T and Q wave: 51.5% of sensitivity and 66.1% of specificity [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, the ECG interpretation provided by GE showed lower sensitivity in this study; it found myocardial ischemia in only 15% of 4,178 patients with QCA stenosis, whereas 11% of the patients without QCA stenosis had ischemia in the interpretation (15.0% of sensitivity and 88.9% of specificity). However, other studies have suggested that multiple ECG variables in the transformed and multiadjusted models could be important predictors of ObCAD detection and mortality, and another recent studies suggested that heart rate variability and the Hilbert–Huang transformation of ECG may reveal the hidden information on the myocardial ischemia [ 32 , 34 , 35 ]. Therefore, if the DL-based ECG models are enhanced in sophisticated and innovative ways, they may help clinicians make better discrimination and decision for further diagnostic methods.…”

Section: Discussionmentioning

confidence: 99%

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Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease

Choi¹,

Lee²,

Park³

et al. 2023

BMC Cardiovasc Disord

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Background Information on electrocardiogram (ECG) has not been quantified in obstructive coronary artery disease (ObCAD), despite the deep learning (DL) algorithm being proposed as an effective diagnostic tool for acute myocardial infarction (AMI). Therefore, this study adopted a DL algorithm to suggest the screening of ObCAD from ECG. Methods ECG voltage-time traces within a week from coronary angiography (CAG) were extracted for the patients who received CAG for suspected CAD in a single tertiary hospital from 2008 to 2020. After separating the AMI group, those were classified into ObCAD and non-ObCAD groups based on the CAG results. A DL-based model adopting ResNet was built to extract information from ECG data in the patients with ObCAD relative to those with non-ObCAD, and compared the performance with AMI. Moreover, subgroup analysis was conducted using ECG patterns of computer-assisted ECG interpretation. Results The DL model demonstrated modest performance in suggesting the probability of ObCAD but excellent performance in detecting AMI. The AUC of the ObCAD model adopting 1D ResNet was 0.693 and 0.923 in detecting AMI. The accuracy, sensitivity, specificity, and F1 score of the DL model for screening ObCAD were 0.638, 0.639, 0.636, and 0.634, respectively, while the figures were up to 0.885, 0.769, 0.921, and 0.758 for detecting AMI, respectively. Subgroup analysis showed that the difference between normal and abnormal/borderline ECG groups was not notable. Conclusions ECG-based DL model showed fair performance for assessing ObCAD and it may serve as an adjunct to the pre-test probability in patients with suspected ObCAD during the initial evaluation. With further refinement and evaluation, ECG coupled with the DL algorithm may provide potential front-line screening support in the resource-intensive diagnostic pathways.

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