 
        
    
    - Office: HWH 2W127
- Email: Nathan Riek
Nathan Riek
(Pronouns: He/Him/His)
    - Post Doctoral Associate
Education
- PhD in Electrical and Computer Engineering, 2025. University of Pittsburgh. Pittsburgh, PA
- BS in Electrical Engineering, 2020. University of Pittsburgh. Pittsburgh, PA
                                    Jos Willems Early Career Investigator Competition Winner
                                
                            
                                    ISCE, 2024
                                
                            
                                        Hybrid model for forecasting atrial fibrillation from normal ECG signal
                                    
                                
                                        ISCE Conference, ISCE, 2025
                                    
                                
                                        Bardolino, Italy
                                    
                                
                                        Saliency maps to enhance explainability of occlusion myocardial infarction classification among pre-hospital chest pain patients
                                    
                                
                                        ISCE Conference, ISCE, 2024
                                    
                                
                                        Braselton, Georgia
                                    
                                
                                        Predicting recovery from coma following cardiac arrest with a reduced set of EEG channels
                                    
                                
                                        Computing in Cardiology, CinC, 2023
                                    
                                
                                        Atlanta, Georgia
                                    
                                
                                        Robust estimation of ST segment amplitude: Revisiting the logic of automated ECG interpretation systems for STEMI classification
                                    
                                
                                        ISCE Conference, ISCE, 2023
                                    
                                
                                        Indian Wells, California
                                    
                                
                                    Riek, N.T., Gokhale, T.A., Akcakaya, M. and Al-Zaiti, S.S (2025). Using large language models for ECG rhythm interpretation: Pitfalls, limitations, and future opportunities. Heart & Lung.  DOI: 10.1016/j.hrtlng.2025.10.003
                                
                            
                                    Helman, S.M., Riek, N.T., Sereika, S.M., Tafti, A.P., Olsen, R., Gaynor, J.W., Lisanti, A.J. & Al-Zaiti, S.S. (2025). Exploring Novel Data-Driven Clustering Methods for Uncovering Patterns in Longitudinal Neonatal Postoperative Temperature Measurements. Mayo Clinic Proceedings: Digital Health.  DOI: 10.1016/j.mcpdig.2025.100270
                                
                            
                                    Gokhale, T.A., Riek, N.T., Medoff, B., Ji, R.Q., Rivera-Lebron, B., Sejdic, E., Akcakaya, M., Saba, S.F., Al-Zaiti, S. & Toma, C. (2025). Artificial intelligence-driven electrocardiogram analysis for risk stratification in pulmonary embolism. European Heart Journal - Digital Health.  DOI: 10.1093/ehjdh/ztaf083
                                
                            
                                    Ji, R.Q., Riek, N.T., Bouzid, Z., Kraevsky-Phillips, K., Gokhale, T., Zègre-Hemsey, J.K., Clermont, G., Saba, S., Martin-Gill, C., Callaway, C.W. &  Akcakaya, M. (2025). Adversarial Debiasing for Equitable and Fair Detection of Acute Coronary Syndrome using 12-Lead ECG. IEEE Transactions on Biomedical Engineering.  PMID: 40788801  DOI: 10.1109/TBME.2025.3597527
                                
                            
                                    Riek, N.T., Akcakaya, M., Bouzid, Z., Gokhale, T., Helman, S., Kraevsky-Philips, K., Ji, R.Q., Sejdic, E., Zègre-Hemsey, J.K., Martin-Gill, C. & Callaway, C.W. (2025). ECG-SMART-NET: A Deep Learning Architecture for Precise ECG Diagnosis of Occlusion Myocardial Infarction. IEEE Transactions on Biomedical Engineering.  PMID: 40418608  DOI: 10.1109/TBME.2025.3573581
                                
                            
                                    Bouzid, Z., Sejdic, E., Martin-Gill, C., Faramand, Z., Frisch, S., Alrawashdeh, M., Helman, S., Gokhale, T.A., Riek, N.T., Kraevsky-Phillips, K. & Gregg, R.E.  (2025). Electrocardiogram-based machine learning for risk stratification of patients with suspected acute coronary syndrome. European Heart Journal.  PMID: 39804231  PMCID: PMC11887543  DOI: 10.1093/eurheartj/ehae880
                                
                            
                                    Riek, N.T., Gokhale, T.A., Martin-Gill, C., Kraevsky-Philips, K., Zègre-Hemsey, J.K., Saba, S., Callaway, C.W., Akcakaya, M. & Al-Zaiti, S.S. (2024). Clinical usability of deep learning-based saliency maps for occlusion myocardial infarction identification from the prehospital 12-Lead electrocardiogram. Journal of Electrocardiology.  PMID: 39255653  PMCID: PMC11899406  DOI: 10.1016/j.jelectrocard.2024.153792
                                
                            
                                    Riek, N.T., Elmer, J., Al-Zaiti, S. & Akcakaya, M. (2023). Predicting Recovery from Coma Following Cardiac Arrest with a Reduced Set of EEG Channels. Computing in Cardiology Conference (CinC).  DOI: 10.22489/cinc.2023.044
                                
                            
                                    Riek, N.T., Susam, B.T., Hudac, C.M., Conner, C.M., Akcakaya, M., Yun, J., White, S.W., Mazefsky, C.A. & Gable, P.A. (2023). Feedback Related Negativity Amplitude is Greatest Following Deceptive Feedback in Autistic Adolescents. Journal of Autism and Developmental Disorders.  PMID: 37393370  DOI: 10.1007/s10803-023-06038-y
                                
                            
                                    Al-Zaiti, S.S., Martin-Gill, C., Zègre-Hemsey, J.K., Bouzid, Z., Faramand, Z., Alrawashdeh, M.O., Gregg, R.E., Helman, S., Riek, N.T., Kraevsky-Phillips, K. & Clermont, G. (2023). Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction. Nature Medicine.  PMID: 37386246  PMCID: PMC10353937  DOI: 10.1038/s41591-023-02396-3
                                
                            
                                    Riek, N.T., So, S., Akcakaya, M. & Yun, M. (2022). Selection of Classifiers to Enhance Efficacy of Metal/Organic Hybrid Sensor Array for VOC and Toxic Gas Identification. IEEE Sensors Journal.  DOI: 10.1109/JSEN.2022.3198014
                                
                            
                                    Riek, N.T., Susam, B.T., Beck, K., Eldeeb, Hudac, C.M. & Gable, P.A. (2022). Quantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exercise. IEEE Transactions on Neural Systems and Rehabilitation Engineering.  PMID: 35976834  PMCID: PMC9979338  DOI: 10.1109/TNSRE.2022.3199151
                                
                            
                                    Susam, B.T., Riek, N.T., Akcakaya, M., Xu, X., De Sa, V.R., Nezamfar, H., Diaz, D., Craig, K.D., Goodwin, M.S. & Huang, J.S. (2022). Automated Pain Assessment in Children Using Electrodermal Activity and Video Data Fusion via Machine Learning. IEEE Transactions on Biomedical Engineering, 69 (1), 422-431.  PMID: 34242161  DOI: 10.1109/TBME.2021.3096137
                                
                            