Machine learning based hybrid anomaly detection technique for automatic diagnosis of cardiovascular diseases using cardiac sympathetic nerve activity and electrocardiogram

• Published in: Biomedizinische Technik Vol. 69; no. 1; pp. 79 - 109
• Main Authors: Terzi, Merve Begum, Arikan, Orhan
• Format: Journal Article
• Language: English
• Published: Germany De Gruyter 26.02.2024; Walter de Gruyter GmbH
• Subjects: Anomalies; Artificial intelligence; Artificial neural networks; Automation; Broadband; Cardiovascular diseases; classification; Clustering; Coronary artery disease; Data processing; Decision support systems; Deep learning; Diagnosis; Diagnostic systems; EKG; Electrocardiography; Feature extraction; Heart diseases; Learning algorithms; Machine learning; Nerves; Neural networks; Neyman-Pearson hypothesis testing; Outliers (statistics); Probabilistic models; Signal processing; Statistical analysis; Sympathetic nerves; synthetic minority oversampling technique (SMOTE); Time domain analysis; Unsupervised learning; clustering; classification; Neyman-Pearson hypothesis testing; feature extraction; signal processing; synthetic minority oversampling technique (SMOTE)

Coronary artery diseases (CADs) are the leading cause of death worldwide and early diagnosis is crucial for timely treatment. To address this, our study presents a novel automated Artificial Intelligence (AI)-based Hybrid Anomaly Detection (AIHAD) technique that combines various signal processing, f...