「世大智科/天才家居」-我們創業囉
Contact Professor: Yeh-Liang Hsu (徐業良)

一0二學年度元智大學機械工程研究所林翰俊博士論文

Doctoral Dissertation by Dr. Hanjun Lin
Mechanical Engineering Department, Yuan Ze University, 2013

102博士論文:應用於遠距健康照護之生醫訊號資料分類方法研究

本論文提出一應用於遠距健康照護之生醫訊號資料分類(classification)方法研究架構。本研究探討四種型態生醫訊號資料,每一種型態以一案例展示相對應的分類方法及在遠距健康照護之應用。第一種生醫訊號資料型態是單一數值的生理訊號量測資料,本研究展示了一個規則式的專家系統TES,在遠距居家照護應用中進行生理訊號異常、量測順從度以及硬體設備異常等異常事件之分類;第二種生醫訊號資料型態是生理訊號時間序列資料,本研究展示了以AIIA方法進行心跳訊號時間序列資料之分類之案例;第三種生醫訊號資料型態是人的動作與行為資料為例,本研究展示了以統計上的估計方法進行帕金森氏症病人正常及異常步態資料分類之案例;第四種生醫訊號資料型態是主觀的癥狀觀察資料,本研究展示了一個遠距居家健康照護決策支援系統,將出院病患癥狀資料搭配臨床醫療紀錄進行判讀後依緊急狀況分類,提供一回診指示。生醫訊號資料型態十分多樣化,在本研究中所提出的資料範例與分類方法或許無法涵括所有問題,但本研究針對遠距居家健康照護領域下生醫訊號資料分類處理方法提出一具體架構,未來亦可以針對特定應用情境持續發展更多不同的分類方法。 關鍵字:遠距居家健康照護、生醫訊號資料、分類、專家系統、統計上的估計方法、決策支援系統

Developing a Framework of Biomedical Data Classification Methodologies for Telehealthcare Applications

This dissertation presents a framework for biomedical data classification methodologies specifically for telehealthcare applications. Biomedical data is categorized into four categories in this study. An example case is demonstrated for each category with the corresponding classification methodology in telehealthcare application. For single-valued vital sign measurements, a rule-based expert system for telehealth application (TES) is demonstrated for the classification of abnormal events, including vital sign abnormality, compliance of measurement and malfunction of hardware devices. For vital sign time series, the AIIA method for classification of heart beats time series into healthy group and disease groups is demonstrated. For human motions and behaviors, a statistical estimation for classification of abnormal and abnormal gaits of Parkinson disease patients is demonstrated. For subjective observation of symptoms, a telehealthcare decision support system (TDSS) for patients recently discharged from hospital, which provides a degree of urgency of returning to the hospital, is demonstrated. This dissertation may not completely cover the classification of all biomedical data. It is intended to provide a structural research framework for biomedical data classification in the telehealth domain. Based on this framework, more example cases can be developed in the field of telehelath.

Keywords: telehealthcare, biomedical data, classification, expert system, statistical estimation, decision support system.

Journal papers
Huang, Y. C., Lin, H. J., Hsu, Y. L., & Lin, J. L., "Using n-gram analysis to cluster heartbeat signals," BMC Medical Informatics and Decision Making, 2012 (1037 visitors)
Lin, H., Hsu, Y. L., Hsu, M. S., & Cheng, C. M., "Development of a telehealthcare decision support system for patients discharged from hospital," Telemedicine and e-Health, 2014 (636 visitors)
Lin, H., Hsu, Y. L., Hsu, M. S., & Cheng, C. M., "Development and practice of a Telehealthcare Expert System - TES," Telemedicine and e-Health, 2013 (684 visitors)
Su, R. H., Hsu, Y. L., Chan, L., Lin, H., & Yang, C. C., "Assessing abnormal gaits of Parkinson’s disease patients using a wearable motion detector," Biomedical Engineering: Applications, Basis and Communications, 2014 (731 visitors)
Academic Thesis
Presentation Files
Last Updated:2014/9/9