Deep Neural Network Algorithm Using the Electrocardiogram for Detection of Obstructive Sleep Apnea

BACKGROUND: Although highly prevalent, obstructive sleep apnea (OSA) remains largely underdiagnosed, thus justifying the need for high-performing screening tools. OBJECTIVES: The authors sought to develop a machine learning-powered algorithm to identify OSA from the 12-lead electrocardiogram (ECG), a routine clinical test. METHODS: A retrospective population of 11,299 patients who completed sleep evaluation and underwent 12-lead ECG at Mayo Clinic were included. OSA was defined as an apnea-hypopnea index ≥5. A deep convolutional neural network model was constructed to detect OSA from the ECG (artificial intelligence [AI]-ECG). Predictive performance of the algorithm in the total sample and separately in males and females was evaluated using the receiver-operating characteristic curve with area under the curve (AUC). RESULTS: The population consisted of 7,170 patients with OSA and 4,129 controls (53.7% males, median [Q1-Q3] of age 58 [47-68] years). The AUC of the AI-ECG model for identification of OSA in the test sample was 0.80 (95% CI: 0.77-0.83), with accuracy, sensitivity, and specificity of 73.7%, 77.0%, and 68.6%, respectively. The model showed better discriminatory performance in females (AUC: 0.82; 95% CI: 0.79-0.86) than in males (AUC: 0.73; 95% CI: 0.68-0.78; P < 0.001). Sensitivity analyses showed that the predictive abilities of the model were robust across different time intervals and even when including ECG recordings manifesting cardiac abnormalities. CONCLUSIONS: Our AI-ECG model demonstrated good diagnostic performance as an ECG-based screening tool for OSA in a clinical population, particularly among females. Incorporating this algorithm in medical practice may enable widespread low-cost screening for OSA, optimizing early diagnosis and therapy.