Artificial intelligence-enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes

Abstract Background Cardiac autonomic neuropathy (CAN) is an important yet often overlooked complication of diabetes, which significantly increases the risk of cardiovascular (CV) events and mortality. Traditional diagnostic methods like CV autonomic function tests (CARTs) are laborious and rarely evaluated in clinical practice. This study aimed to develop and employ machine learning (ML) algorithms to analyze electrocardiogram (ECG) for the diagnosis of CAN. Methods We utilized motif and discord extraction techniques alongside Long Short-Term Memory (LSTM) networks to analyze 12-lead, 10 seconds ECG tracings to detect CAN in patients with diabetes. The performance of these methods with the Support Vector Machine (SVM) classification model was evaluated using Ten-Cross Validation (TCV) with the following metrics accuracy, precision, recall, F1 score, and area under the ROC Curve (AUC). Results Among 205 patients (mean age 54 ± 17; 54% female), 100 were diagnosed with CAN, including 38 with definite or severe CAN (dsCAN) and 62 with early CAN (eCAN). The best model performance for dsCAN classification was achieved using both motifs and discords, with an accuracy of 0.92, an F1 score of 0.92, a recall at 0.94, a precision of 0.91, and an excellent AUC of 0.93 (95%CI 0.91-0.94). For the detection of any stage of CAN, the approach combining motifs and discords yielded best results with an accuracy of 0.65, F1 score of 0.68, a recall of 0.75, a precision of 0.68, and an AUC of 0.68 (95%CI 0.54-0.81). Conclusion Our study highlights the potential of using ML techniques, particularly motifs and discords, to effectively detect dsCAN in patients with diabetes. This approach could be applied in large-scale screening of CAN, particularly to identify definite/severe CAN where CV risk factor modification may be initiated.