Assessing ECG Interpretation Expertise in Medical Practitioners Through Eye Movement Data and Neuromorphic Models
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...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10836701/ |
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| author | Syed Mohsin Bokhari Muhammad Shafi Fazal Noor Sarmad Sohaib Saad Alqahtany Mark Donnelly |
| author_facet | Syed Mohsin Bokhari Muhammad Shafi Fazal Noor Sarmad Sohaib Saad Alqahtany Mark Donnelly |
| author_sort | Syed Mohsin Bokhari |
| collection | DOAJ |
| description | 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 Neural Networks (SNN), Spiking Convolutional Neural Networks (SCNN), Recurrent Spiking Neural Networks (RSNN), and Spiking Convolutional Long Short-Term Memory (SCLSTM). Utilizing eye movement data, we analyze the skill levels of practitioners in diverse medical positions, including consultants, nurses, and technicians, during ECG evaluations. Our proposed work combines spiking neuron activations with convolutional and recurrent architectures to analyze spatial and temporal gaze patterns that reflect clinical expertise. The suggested SNN, SCNN, RSNN, and SCLSTM models attained accuracies of 84.35%, 93.04%, 94.68%, 99.76% respectively, exceeding standard machine learning approaches in both precision and recall for identifying expertise levels based on visual attention patterns. This paradigm has the potential to construct skill evaluation tools in medical education, specifically for ECG interpretation training, thereby addressing prevalent difficulties related to inconsistent ECG diagnosis methods. |
| format | Article |
| id | doaj-art-73eef382b27c4767aa2f092140332586 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-73eef382b27c4767aa2f0921403325862025-01-21T00:01:58ZengIEEEIEEE Access2169-35362025-01-01139430944910.1109/ACCESS.2025.352825310836701Assessing ECG Interpretation Expertise in Medical Practitioners Through Eye Movement Data and Neuromorphic ModelsSyed Mohsin Bokhari0https://orcid.org/0009-0002-5888-4163Muhammad Shafi1https://orcid.org/0000-0002-8430-206XFazal Noor2https://orcid.org/0000-0002-0096-3435Sarmad Sohaib3Saad Alqahtany4Mark Donnelly5Department of Electrical and Computer Engineering, University of Engineering and Technology (UET) Taxila, Taxila, PakistanSchool of Computing, Ulster University, Belfast, U.K.Faculty of Computer and Information Systems (FCIS), Islamic University of Madinah, Madinah, Saudi ArabiaDepartment of Electrical and Electronic Engineering, University of Jeddah, Jeddah, Saudi ArabiaFaculty of Computer and Information Systems (FCIS), Islamic University of Madinah, Madinah, Saudi ArabiaSchool of Computing, Ulster University, Belfast, U.K.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 Neural Networks (SNN), Spiking Convolutional Neural Networks (SCNN), Recurrent Spiking Neural Networks (RSNN), and Spiking Convolutional Long Short-Term Memory (SCLSTM). Utilizing eye movement data, we analyze the skill levels of practitioners in diverse medical positions, including consultants, nurses, and technicians, during ECG evaluations. Our proposed work combines spiking neuron activations with convolutional and recurrent architectures to analyze spatial and temporal gaze patterns that reflect clinical expertise. The suggested SNN, SCNN, RSNN, and SCLSTM models attained accuracies of 84.35%, 93.04%, 94.68%, 99.76% respectively, exceeding standard machine learning approaches in both precision and recall for identifying expertise levels based on visual attention patterns. This paradigm has the potential to construct skill evaluation tools in medical education, specifically for ECG interpretation training, thereby addressing prevalent difficulties related to inconsistent ECG diagnosis methods.https://ieeexplore.ieee.org/document/10836701/Spiking neural networks (SNN)spiking convolutional neural networks (SCNN)recurrent spiking neural networks (RSNN)spiking convolutional long short-term memory (SCLSTM)random forestECG |
| spellingShingle | Syed Mohsin Bokhari Muhammad Shafi Fazal Noor Sarmad Sohaib Saad Alqahtany Mark Donnelly Assessing ECG Interpretation Expertise in Medical Practitioners Through Eye Movement Data and Neuromorphic Models IEEE Access Spiking neural networks (SNN) spiking convolutional neural networks (SCNN) recurrent spiking neural networks (RSNN) spiking convolutional long short-term memory (SCLSTM) random forest ECG |
| title | Assessing ECG Interpretation Expertise in Medical Practitioners Through Eye Movement Data and Neuromorphic Models |
| title_full | Assessing ECG Interpretation Expertise in Medical Practitioners Through Eye Movement Data and Neuromorphic Models |
| title_fullStr | Assessing ECG Interpretation Expertise in Medical Practitioners Through Eye Movement Data and Neuromorphic Models |
| title_full_unstemmed | Assessing ECG Interpretation Expertise in Medical Practitioners Through Eye Movement Data and Neuromorphic Models |
| title_short | Assessing ECG Interpretation Expertise in Medical Practitioners Through Eye Movement Data and Neuromorphic Models |
| title_sort | assessing ecg interpretation expertise in medical practitioners through eye movement data and neuromorphic models |
| topic | Spiking neural networks (SNN) spiking convolutional neural networks (SCNN) recurrent spiking neural networks (RSNN) spiking convolutional long short-term memory (SCLSTM) random forest ECG |
| url | https://ieeexplore.ieee.org/document/10836701/ |
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