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Muammar Sadrawi, B.S., M.S., Ph.D.

Research Interest

  • Non-invasive signal generator
  • Human-computer interaction
  • Brain-heart interplay

Biography

Dr. Muammar Sadrawi, S.T., M.Sc. acquired his bachelor’s degree in the Department of Mechanical Engineering, Universitas Syiah Kuala, Indonesia, in 2009. He received his master’s and Ph.D. degrees in Mechanical Engineering at Yuan Ze University, Taiwan, in 2013 and 2018, respectively. Dr. Sadrawi has expertise in artificial intelligence (AI) and data science in sensing and condition monitoring systems.

Muammar Sadrawi, B.S., M.S., Ph.D.

Research Activity and Highlighted Project

Year

Title

Partners

2020-2021

Non-Invasive Cardiovascular and Cerebral Hemodynamics

New Era AI Robotic Inc., Taipei, Taiwan

2020

Continuous Blood Pressure Estimation Project

New Era AI Robotic Inc., Taipei, Taiwan

2016-2017

Arrhythmia Detection Development for Wearable System Project

Kinpo Electronics, Inc., Taipei, Taiwan

2015-2018

Emergency Medical Service Big Data Evaluation

National Taiwan University Hospital (NTUH), Taipei, Taiwan

2015

Anesthesia signal processing

National Taiwan University Hospital (NTUH), Taipei, Taiwan

Selected Publication

Sadrawi, M., Lin, Y. T., Lin, C. H., Mathunjwa, B., Hsin, H. T., Fan, S. Z., … & Shieh, J. S. (2021). Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder. Sensors21(18), 6264.

Sadrawi, M., Lin, Y. T., Lin, C. H., Mathunjwa, B., Fan, S. Z., Abbod, M. F., & Shieh, J. S. (2020). Genetic deep convolutional autoencoder applied for generative continuous arterial blood pressure via photoplethysmography. Sensors20(14), 3829.

Sadrawi, M., Sun, W. Z., Ma, M. H. M., Yeh, Y. T., Abbod, M. F., & Shieh, J. S. (2018). Ensemble genetic fuzzy neuro model applied for the emergency medical service via unbalanced data evaluation. Symmetry10(3), 71.

Sadrawi, M., Lin, C. H., Lin, Y. T., Hsieh, Y., Kuo, C. C., Chien, J. C., … & Shieh, J. S. (2017). Arrhythmia evaluation in wearable ECG devices. Sensors17(11), 2445.

Sadrawi, M., Fan, S. Z., Abbod, M. F., Jen, K. K., & Shieh, J. S. (2015). Computational depth of anesthesia via multiple vital signs based on artificial neural networks. BioMed research international2015.

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