ADAPT: an AI-Driven approach for arrhythmia detection in diabetic patients

Arrhythmia detection in diabetic patients is a critical aspect of cardiac care, as these patients face an increased risk of developing cardiac rhythm irregularities. Early detection of arrhythmias is essential for timely intervention and improved management of cardiac health in diabetic individuals. In this research, we propose an innovative approach named “ADAPT” for early arrhythmia detection in diabetic patients. ADAPT leverages the power of modern deep learning techniques, primarily Convolutional Neural Networks (CNNs), for the automatic extraction of relevant features from raw electrocardiogram (ECG) signals, such as RR intervals, QT intervals, heart rate (HR wave duration), P-, QRS complex duration, and T-wave morphology. The CNN module effectively learns discriminative patterns and features from the ECG data, allowing for accurate classification between normal and abnormal heart rhythms. To optimize the model’s performance and address the challenge of limited annotated data, ADAPT incorporates data augmentation techniques, enabling the generation of diverse ECG samples. This increase strategy improves the model’s skill to generalize and detect arrhythmias in various scenarios, even with limited labelled data. Furthermore, ADAPT employs transfer learning by fine-tuning pre-trained CNN models on large-scale ECG datasets, incorporating general ECG knowledge with domain-specific data from diabetic patients. This transfer learning approach allows ADAPT to effectively capture the unique cardiac characteristics present in diabetic individuals, leading to improved detection accuracy. The performance evaluation of ADAPT are carried out on a real-world dataset of ECG recordings from diabetic patients, considering parameters like heart rate variability, frequency-domain features, clinical history, diabetes-related data, medication and drug usage, physical activity level, age, and gender. The results indicate that ADAPT achieves high accuracy and sensitivity, making it a valuable tool for identifying cardiac rhythm irregularities in diabetic patients. The proposed ADAPT approach contributes to enhancing the clinical management of diabetic patients’ cardiac health by providing an automated, accurate, and efficient system for early arrhythmia detection. With its AI-driven capabilities, ADAPT offers potential benefits for timely diagnosis, intervention, and improved overall cardiac health outcomes in this vulnerable patient population.