Patents:
Journal Articles:
[1] H. F. Posada-Quintero and K. H. Chon, “Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review,” Sensors, vol. 20, no. 2, p. 479, Jan. 2020, doi: 10.3390/s20020479. Download
[2] M. S. Daley, D. Gever, H. F. Posada-Quintero, Y. Kong, K. Chon, and J. B. Bolkhovsky, “Machine Learning Models for the Classification of Sleep Deprivation Induced Performance Impairment During a Psychomotor Vigilance Task Using Indices of Eye and Face Tracking,” Front. Artif. Intell., vol. 3, p. 17, Apr. 2020, doi: 10.3389/frai.2020.00017. Download
[3] J. Lazaro, N. Reljin, M. B. Hossain, Y. Noh, P. Laguna, and K. Chon, “Wearable Armband Device for Daily Life Electrocardiogram Monitoring,” IEEE Trans Biomed Eng, Apr. 2020, doi: 10.1109/TBME.2020.2987759. Download
[4] S. Bashar, M. B. Hossain, E. Ding, A. Walkey, D. McManus, and K. Chon, “Atrial Fibrillation Detection during Sepsis: Study on MIMIC III ICU Data*,” IEEE Journal of Biomedical and Health Informatics, pp. 1–1, 2020, doi: 10.1109/JBHI.2020.2995139. Download Dataset
[5] S. Bashar et al., “Novel Density Poincare Plot Based Machine Learning Method to Detect Atrial Fibrillation from Premature Atrial/Ventricular Contractions,” IEEE Transactions on Biomedical Engineering, pp. 1–1, 2020, doi: 10.1109/TBME.2020.3004310. Download
[6] S. Sinha et al., “Integrated dry PEDOT:PSS electrodes on finished textiles for continuous and simultaneous monitoring of electrocardiogram, electromyogram and electrodermal activity,” Flex. Print. Electron., 2020, doi: 10.1088/2058-8585/abad89. Download
[7] R. M. Parra-Hernández, J. I. Posada-Quintero, O. Acevedo-Charry, and H. F. Posada-Quintero, “Uniform Manifold Approximation and Projection for Clustering Taxa through Vocalizations in a Neotropical Passerine (Rough-Legged Tyrannulet, Phyllomyias burmeisteri),” Animals, vol. 10, no. 8, Art. no. 8, Aug. 2020, doi: 10.3390/ani10081406. Download
[8] H. F. Posada-Quintero et al., “Using electrodermal activity to validate multilevel pain stimulation in healthy volunteers evoked by thermal grills,” American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, vol. 319, no. 3, pp. R366–R375, Jul. 2020, doi: 10.1152/ajpregu.00102.2020. Download
[9] N. Reljin et al., “Machine Learning Model Based on Transthoracic Bioimpedance and Heart Rate Variability for Lung Fluid Accumulation Detection: Prospective Clinical Study,” JMIR Medical Informatics, vol. 8, no. 8, p. e18715, 2020, doi: 10.2196/18715. Download
[10] N. Reljin, J. Lazaro, M. B. Hossain, Y. S. Noh, C. H. Cho, and K. H. Chon, “Using the Redundant Convolutional Encoder–Decoder to Denoise QRS Complexes in ECG Signals Recorded with an Armband Wearable Device,” Sensors, vol. 20, no. 16, Art. no. 16, Jan. 2020, doi: 10.3390/s20164611. Download
[11] D. Han et al., “Premature Atrial and Ventricular Contraction Detection using Photoplethysmographic Data from a Smartwatch,” Sensors, vol. 20, no. 19, Art. no. 19, Jan. 2020, doi: 10.3390/s20195683. Download Dataset
[12] M.-B. Hossain, J. Moon, and K. H. Chon, “Estimation of ARMA Model Order via Artificial Neural Network for Modeling Physiological Systems,” IEEE Access, vol. 8, pp. 186813–186820, 2020, doi: 10.1109/ACCESS.2020.3029756. Download
[13] N. A. Bosch et al., “Comparative effectiveness of heart rate control medications for the treatment of sepsis-associated atrial fibrillation,” CHEST, vol. 0, no. 0, Oct. 2020, doi: 10.1016/j.chest.2020.10.049. Download
[14] Y. Kong, H. F. Posada-Quintero, M. S. Daley, K. H. Chon, and J. Bolkhovsky, “Facial features and head movements obtained with a webcam correlate with performance deterioration during prolonged wakefulness,” Atten Percept Psychophys, Nov. 2020, doi: 10.3758/s13414-020-02199-5. Download
[15] M.-B. Hossain, S. K. Bashar, J. Lazaro, N. Reljin, Y. Noh, and K. H. Chon, “A robust ECG denoising technique using variable frequency complex demodulation,” Computer Methods and Programs in Biomedicine, p. 105856, Nov. 2020, doi: 10.1016/j.cmpb.2020.105856. Download