A Deep Learning Ensemble Model Based on ECG for the Diagnosis of Variant Angina

Variant Angina (VA) is a condition characterized by chest pain caused by atherosclerotic spasms and contractions. It can affect younger individuals without typical risk factors and is often accompanied by symptoms like sweating, tachycardia, and low blood pressure. In severe cases, VA can result in life-threatening complications such as myocardial infarction or arrhythmia, highlighting the importance of accurate diagnosis and early prevention. Although various diagnostic methods exist in clinical practice, diagnosing VA remains difficult due to the reliance on invasive procedures that carry risks such as arterial damage and bleeding. With advancements in big data and deep learning technologies, there is growing potential for automated and accurate diagnoses through electrocardiography (ECG). This study introduces a deep learning algorithm designed specifically for diagnosing VA using ECG data. As a non-invasive approach, it offers a cost-effective and accessible solution for patients with suspected VA. To the best of our knowledge, this is the first study to apply a deep learning model solely to ECG data for VA diagnosis.