Journal Articles:

[1]          J. Moon, M. B. Hossain, and K. H. Chon, “AR and ARMA Model Order Selection for Time-Series Modeling with ImageNet Classification,” Signal Process., p. 108026, Feb. 2021, doi: 10.1016/j.sigpro.2021.108026. Download

[2]          A. J. Walkey et al., “Development and Validation of an Automated Algorithm to Detect Atrial Fibrillation Within Stored Intensive Care Unit Continuous Electrocardiographic Data: Observational Study,” JMIR Cardio, vol. 5, no. 1, p. e18840, Feb. 2021, doi: 10.2196/18840. Download

[3]          Hajeb‐M Shirin, Cascella Alicia, Valentine Matt, and Chon K. H., “Deep Neural Network Approach for Continuous ECG‐Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation,” J. Am. Heart Assoc., vol. 0, no. 0, p. e019065, doi: 10.1161/JAHA.120.019065. Download

[4]          Y. Kong, H. Posada-Quintero, and K. Chon, “Sensitive Physiological Indices of Pain based on Differential Characteristics of Electrodermal Activity,” IEEE Trans. Biomed. Eng., pp. 1–1, 2021, doi: 10.1109/TBME.2021.3065218. Download

[5]            Y. Kong, H. F. Posada-Quintero, and K. H. Chon, “Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor,” Sensors, vol. 21, no. 12, Art. no. 12, Jan. 2021, doi: 10.3390/s21123956. Download

[6]          H. F. Posada–Quintero, Y. Kong, and K. H. Chon, “Objective pain stimulation intensity and pain sensation assessment using machine learning classification and regression based on electrodermal activity,” Am. J. Physiol.-Regul. Integr. Comp. Physiol., vol. 321, no. 2, pp. R186–R196, Aug. 2021, doi: 10.1152/ajpregu.00094.2021. Download

[7]          S. K. Bashar, E. Y. Ding, A. J. Walkey, D. D. McManus, and K. H. Chon, “Atrial Fibrillation Prediction from Critically Ill Sepsis Patients,” Biosensors, vol. 11, no. 8, Art. no. 8, Aug. 2021, doi: 10.3390/bios11080269. Download

[8]          S. K. Bashar et al., “Feasibility of atrial fibrillation detection from a novel wearable armband device,” Cardiovasc. Digit. Health J., vol. 2, no. 3, pp. 179–191, Jun. 2021, doi: 10.1016/j.cvdhj.2021.05.004. Download

[9]          A. Fahlman et al., “The New Era of Physio-Logging and Their Grand Challenges,” Front. Physiol., vol. 0, 2021, doi: 10.3389/fphys.2021.669158. Download

[10]        Y. Kong, H. F. Posada-Quintero, M. S. Daley, J. Bolkhovsky, and K. H. Chon, “Machine-Learning-Based Closed-Set Text-Independent Speaker Identification Using Speech Recorded During 25 Hours of Prolonged Wakefulness,” IEEE Access, vol. 9, pp. 96890–96897, 2021, doi: 10.1109/ACCESS.2021.3094175. Download

[11]        E. L. Dickson et al., “Smartwatch Monitoring for Atrial Fibrillation After Stroke – The Pulsewatch Study: Protocol for a Multi-Phase Randomized Controlled Trial,” Cardiovasc. Digit. Health J., vol. 0, no. 0, Jul. 2021, doi: 10.1016/j.cvdhj.2021.07.002. Download

[12]        S. Hajeb-Mohammadalipour, A. Cascella, M. Valentine, and K. H. Chon, “Automated Condition-Based Suppression of the CPR Artifact in ECG Data to Make a Reliable Shock Decision for AEDs during CPR,” Sensors, vol. 21, no. 24, Art. no. 24, Jan. 2021, doi: 10.3390/s21248210. Download

Conference Papers: