Assessing ECG Interpretation Expertise in Medical Practitioners Through Eye Movement Data and Neuromorphic Models

• Published in: IEEE access Vol. 13; pp. 9430 - 9449
• Main Authors: Bokhari, Syed Mohsin, Shafi, Muhammad, Noor, Fazal, Sohaib, Sarmad, Alqahtany, Saad, Donnelly, Mark
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Biomedical imaging; Cardiology; Data models; ECG; Electrocardiography; Evaluation; Eye movements; Gaze tracking; Long short term memory; Machine learning; Medical diagnostic imaging; Neural networks; Neuromorphic computing; random forest; recurrent spiking neural networks (RSNN); Spiking; spiking convolutional long short-term memory (SCLSTM); spiking convolutional neural networks (SCNN); Spiking neural networks; Spiking neural networks (SNN); Training; Visualization

This study introduces an innovative method for assessing ECG interpretation abilities in medical professionals via eye-tracking data. We examine eye movement patterns from five separate groups of cardiology practitioners utilizing a combination of neuromorphic computing models, including Spiking Neu...