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Your search keyword '"I-Chun Kuo"' showing total 7 results
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7 results on '"I-Chun Kuo"'

1. Three-Heartbeat Multilead ECG Recognition Method for Arrhythmia Classification

Author

Liang-Hung Wang, Yan-Ting Yu, Wei Liu, Lu Xu, Chao-Xin Xie, Tao Yang, I-Chun Kuo, Xin-Kang Wang, Jie Gao, Pao-Cheng Huang, Shih-Lun Chen, Wei-Yuan Chiang, and Patricia Angela R. Abu

Subjects
Arrhythmia classification, electrocardiogram, one-dimensional convolutional neural network (1D-CNN), priority model integrated voting method, three-heartbeat multi-lead (THML), Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Electrocardiogram (ECG) is the primary basis for the diagnosis of cardiovascular diseases. However, the amount of ECG data of patients makes manual interpretation time-consuming and onerous. Therefore, the intelligent ECG recognition technology is an important means to decrease the shortage of medical resources. This study proposes a novel classification method for arrhythmia that uses for the very first time a three-heartbeat multi-lead (THML) ECG data in which each fragment contains three complete heartbeat processes of multiple ECG leads. The THML ECG data pre-processing method is formulated which makes use of the MIT-BIH arrhythmia database as training samples. Four arrhythmia classification models are constructed based on one-dimensional convolutional neural network (1D-CNN) combined with a priority model integrated voting method to optimize the integrated classification effect. The experiments followed the recommended inter-patient scheme of the Association for the Advancement of Medical Instrumentation (AAMI) recommendations, and the practicability and effectiveness of THML ECG data are proved with ablation experiments. Results show that the average accuracy of the N, V, S, F, and Q classes is 94.82%, 98.10%, 97.28%, 98.70%, and 99.97%, respectively, with the positive predictive value of the N, V, S, and F classes being 97.0%, 90.5%, 71.9%, and 80.4%, respectively. Compared with current studies, the THML ECG data can effectively improve the morphological integrity and time continuity of ECG information and the 1D-CNN model of ECG sequence has a higher accuracy for arrhythmia classification. The proposed method alleviates the problem of insufficient samples, meets the needs of medical ECG interpretation and contributes to the intelligent dynamic research of cardiac disease.

Published
2022
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2. Automated Classification Model With OTSU and CNN Method for Premature Ventricular Contraction Detection

Author

Liang-Hung Wang, Lin-Juan Ding, Chao-Xin Xie, Su-Ya Jiang, I-Chun Kuo, Xin-Kang Wang, Jie Gao, Pao-Cheng Huang, and Patricia Angela R. Abu

Subjects
Electrocardiogram (ECG), convolutional neural network, premature ventricular contraction, OTSU, ECG classification, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Premature ventricular contraction (PVC) is one of the most common arrhythmias which can cause palpitation, cardiac arrest, and other symptoms affecting the work and rest activities of a patient. However, patients hardly decipher their own feelings to determine the severity of the disease thus, requiring a professional medical diagnosis. This study proposes a novel method based on image processing and convolutional neural network (CNN) to extract electrocardiography (ECG) curves from scanned ECG images derived from clinical ECG reports, and segment and classify heartbeats in the absence of a digital ECG data. The ECG curve is extracted using a comprehensive algorithm that combines the OTSU algorithm with erosion and dilation. This algorithm can efficiently and accurately separate the ECG curve from the ECG background grid. The performance of the classification model was evaluated and optimized using hundreds of clinical ECG data collected from Fujian Provincial Hospital. Additionally, thousands of clinical ECG reports were scanned to digital images as the test set to confirm the accuracy of the algorithm for practical application. Results showed that the average sensitivity, specificity, positive predictive value, and accuracy of the proposed model on the MIT-BIH dataset were 95.47%, 97.72%, 98.75%, and 98.25%, respectively. The classification average sensitivity, specificity, positive predictive value, and accuracy based on clinical scanned ECG images can reach to 97.24%, 81.6%, 83.8%, and 89.33%, respectively, and the clinical feasibility is high. Overall, the proposed method can extract ECG curves from scanned ECG images efficiently and accurately. Furthermore, it performs well on heartbeat classification of normal (N) and ventricular premature heartbeat.

Published
2021
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4. Method of ECG Lead Recovery from 3-Lead to 12-Lead

Author

I-Chun Kuo, Lin-Pei Xu, and Liang-Hung Wang

Subjects
Correlation coefficient, Computer science, Real-time computing, Wearable computer, Ecg lead, Collection system, Lead (electronics)
Abstract

Wearable ECG collection system for home detection has attracted increasing attention. Such devices are designed to be portable with fewer leads. Reconstruction from three-lead ECG to 12-lead ECG can provide useful auxiliary information for telemedicine dynamic ECG detection. In this study, two reconstruction algorithms are discussed, and the correlation coefficients of the reconstruction results are analyzed. The average correlation coefficient in the randomized experiment reaches 0.901.

Published
2021
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5. The benefit of Simulation Test Application on the Development of Autonomous Driving System

Author

I-Chun Kuo, Yi-Zhao Chen, and Chien-Hung Yu

Subjects
Development (topology), Simulation test, Computer science, media_common.quotation_subject, Driving simulator, Sample (statistics), Scenario testing, Set (psychology), Function (engineering), Reliability engineering, Test (assessment), media_common
Abstract

Advanced Driving Assistance System (ADAS) & Autonomous Driving System (ADS) are promptly developed as the demands on intelligent & secure transportation rise. For the mentioned system to get complex, the way to verify the function and evaluate the performance of it requires rigorous and comprehensive arrangement on test scenario settings. Meanwhile, the cost to build up the mentioned tests and the risks that the test personnel take may reach a new level if applying real car tests.To deal with these mentioned issues, the X-in-the-Loop(XiL) simulation test is adopted. In this paper, the benefit to apply simulation tests for ADS development will be introduced through the discussion about driving simulator appliance and test scenario construction. An ADS equipped with path following system and Automated Emergency Braking (AEB) system is set as a sample to be tested in the settled XiL to determine its efficacy of driving safety. According to the results, the requirements of ADS Development are met quite well - The system functionality is determined through each proposed tests. The cost of test equipment and hazards during the tests can thus be reduced.

Published
2020
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6. Adaptive tracking control for nonlinear systems by an adaptive model-based FNNs sliding mode control scheme

Author

Yi-Chao Chen, Tsung-Chih Lin, I-Chun Kuo, and Yu-Chen Lin

Subjects
Lyapunov stability, 0209 industrial biotechnology, Variable structure control, Adaptive control, 020208 electrical & electronic engineering, 02 engineering and technology, Fuzzy control system, Sliding mode control, Nonlinear system, 020901 industrial engineering & automation, Control theory, Adaptive system, 0202 electrical engineering, electronic engineering, information engineering, Reference model, Mathematics
Abstract

This paper concerned with the adaptive tracking control problem of an adaptive model-based fuzzy-neural networks (FNNs) sliding mode control (AFSMC) scheme for a class of nonlinear systems, which are represented by Takagi-Sugeno (T-S) fuzzy model to express a nonlinear systems model. Then, an adaptive parameter estimator is proposed to estimate the unknown nonlinear system parameters. Considering the online estimating error from the estimation model and nonlinear system model, a state estimation based feedback controller is derived by the proposed adaptive FNNs sliding mode control scheme and free parameters can be updated online by adaptive laws based on Lyapunov stability theorem. The proposed control scheme can guarantee that the unknown nonlinear system output can track to the states of reference model for any desired input signals when the stability condition is satisfied. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed approaches.

Published
2017
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7. An eco-cruising control systems using nonlinear predictive control approach

Author

Hsiang-Chieh Hsu, Yu-Chen Lin, and I-Chun Kuo

Subjects
050210 logistics & transportation, Engineering, business.industry, 05 social sciences, 020302 automobile design & engineering, Advanced driver assistance systems, Control engineering, 02 engineering and technology, Model predictive control, 0203 mechanical engineering, Control theory, Control system, 0502 economics and business, Fuel efficiency, Global Positioning System, Road map, business, Cruise control
Abstract

Recently, a number of modern premium cars have been equipped with advanced driver assistance systems (ADAS), especially an adaptive cruise control (ACC) system, which was found to be one of the most widely and successful ADAS systems to date. However, the road topography on highways affects fuel consumption of vehicles. Rapid acceleration, rapid deceleration, and unreasonable gear use can all result in increasing fuel consumption. Therefore, an eco-cruising control (ECC) systems on roads with up-down slopes is a very critical significance for improving vehicle fuel economy based on the global positioning system (GPS) and geographic information system (GIS). This proposed approach combine the road elevation profile by digital road map and a nonlinear predictive control (NPC) strategy to deal with the vehicle velocity control; it is aimed at providing the most economical speed on road grade. The developed algorithms not only to provide the ecological driving guidance for driver, but also to achieve the driving safety. Extensive simulations illustrate that the proposed controller can significantly reducing fuel consumption of a vehicle and ensuring driving comfort and safety.

Published
2016
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