Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification

no code implementations • 9 Feb 2024 • Muhammad Uzair Zahid, Aysen Degerli, Fahad Sohrab, Serkan Kiranyaz, Tahir Hamid, Rashid Mazhar, Moncef Gabbouj

Early detection of myocardial infarction (MI), a critical condition arising from coronary artery disease (CAD), is vital to prevent further myocardial damage.

Myocardial infarction detection One-Class Classification

Global ECG Classification by Self-Operational Neural Networks with Feature Injection

no code implementations • 7 Apr 2022 • Muhammad Uzair Zahid, Serkan Kiranyaz, Moncef Gabbouj

The classification layers can thus benefit from both temporal and learned features for the final arrhythmia classification.

Arrhythmia Detection Classification +1

Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks

1 code implementation • 30 Sep 2021 • Moncef Gabbouj, Serkan Kiranyaz, Junaid Malik, Muhammad Uzair Zahid, Turker Ince, Muhammad Chowdhury, Amith Khandakar, Anas Tahir

Although numerous R-peak detectors have been proposed in the literature, their robustness and performance levels may significantly deteriorate in low-quality and noisy signals acquired from mobile electrocardiogram (ECG) sensors, such as Holter monitors.

Computational Efficiency

Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network

no code implementations • 29 Dec 2020 • Muhammad Uzair Zahid, Serkan Kiranyaz, Turker Ince, Ozer Can Devecioglu, Muhammad E. H. Chowdhury, Amith Khandakar, Anas Tahir, Moncef Gabbouj

Results also demonstrate similar or better performance than most competing algorithms on MIT-DB with 99. 83% F1-score, 99. 85% recall, and 99. 82% precision.