Browsing by Author "Kuo, Chin-En"

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Development of an EOG-Based Automatic Sleep-Monitoring Eye Mask
(2015-11-01)
Liang, Sheng-Fu
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Kuo, Chin-En
;
Lee, Yi-Chieh
;
Lin, Wen-Chieh
;
Liu, Yen-Chen
Health care researchers have recently developed several home-based sleep monitoring systems and mobile applications that support healthy sleep by monitoring a user\'s sleep environment, activities, or sleep quality. This study describes the design and evaluation of an electrooculogram (EOG)-based system for an automatic sleep monitoring. Compared with polysomnogram or electroencephalogram recordings, EOG has the advantage of easy placement and can be operated by the users themselves. We also design an intelligent eye mask that is user friendly for measuring sleep stage and quality. Two user experiments were carried out to demonstrate that the proposed system produces valid measurements of sleep stage and sleep quality and has good usability and reliability while not disturbing sleep significantly. These findings suggest that our system can also be applied to long-term sleep monitoring or sleep environment control to improve the user\'s sleep quality and make sleep more comfortable.
Estimation and Prediction of Drug Therapy on the Termination of Atrial Fibrillation by Autoregressive Model With Exogenous Inputs
(2013-01-01)
Kuo, Chin-En
;
Liang, Sheng-Fu
;
Lu, Shao-Sheng
;
Kuan, Tang-Ching
;
Lin, Chih-Sheng
Atrial fibrillation (AF) is the most frequent cardiac arrhythmia seen in clinical practice. Several therapeutical approaches have been developed to terminate the AF and the effects are evaluated by the reduction of the wavelet number after the treatments. Most of the previous studies focus on modeling and analysis of the mechanism, and the characteristic of AF. But no one discusses about the prediction of the result after the drug treatment. This paper is the first study to predict whether the drug treatment for AF is active or not. In this paper, the linear autoregressive model with exogenous inputs (ARX) that models the system output-input relationship by solving linear regression equations with least-squares method was developed and applied to estimate the effects of pharmacological therapy on AF. Recordings (224-site bipolar recordings) of plaque electrode arrays placed on the right and left atria of pigs with sustained AF induced by rapid atrial pacing were used to train and test the ARX models. The cardiac mapping data from 12 pigs treated with intravenous administration of antiarrhythmia drug, propafenone (PPF), or dl-sotalol (STL) were evaluated. The recordings of cardiac activity before the drug treatment were input to the model and the model output reported the estimated wavelet number of atria after the drug treatment. The results show that the predicting accuracy rate corresponding to the PPF and STL treatments was 100% and 92%, respectively. It is expected that the developed ARX model can be further extended to assist the clinical staffs to choose the effective treatments for the AF patients in the future.