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Heart Rate Variability (HRV) Analysis in Health Care Platform
Electrocardiogram(ECG), Tele-homecare, Heart rate variability (HRV), ECG-derived respiration (EDR), Data mining, Feature generation.
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以遠端居家照護為前提，本論文以創新兩片式設計可感測即時動態的心跳頻率與心電圖量測之健康照護平台，將所偵測到之心電訊號進行心律變異性分析。心律變異性分析可由訊號處理及資料庫分析程序建置所組成，訊號處理包含即時R波演算法偵測及生理訊號信號處理。而資料庫建置可由特徵值建立、資料探勘、特徵萃取及系統分析歸納模型組成。特徵值建立擬利用感測器所量測到之生理訊號進行心律變異性分析萃取，資料探勘則是在未知的特徵值中發掘具有參考性的資訊。利用特徵選取方法搜尋出最佳的特徵組合，目的在於減少資料運算及傳輸時間。系統分析歸納模型，建立在心率變異分析及資料分析歸納等分析結果下，將受測者與身體資訊關聯性做初步判別，可提醒使用者與專業人員作為自我管理及診斷輔助。利用不同型態的生理資訊，以驗證資料庫的可靠性，以評估個人身體狀態。藉由健康照護平台和心率變異性分析(Heart Rate Variability, HRV)進行資料整合，而本研究期望提供使用者一即時心律變異性分析整合之健康管理平台。|
The population structure towards an aging society has led to the rapid development of new medical treatment and technologies, putting the importance of health care under spot lights. The development of tele-medicine and tele-homecare (THC) plays an important role in computer science and healthcare application. The healthcare system provides abundant of contextual information and alerting mechanisms for both users and professionals. Also, the wearable health system is an emerging technology for continuous monitoring by using biomedical signal such as ECG, blood pressure, or oxygen. In this thesis, we develop a healthcare application which called the data analysis system (DSA) based on health care platform (HCP). HCP is a wearable health system which is able to solve the baseline drift problem, detect the heart-rate status, and record the ECG signals that can be used as a wide range remote home-care system. Also, it provides a convenient handheld device used for collecting ECG signals in moving state from only two conductive electrodes which consists of three parts: a bio-signal sensor, a wireless transmitter, and a health monitoring database. The device transmits the data to computers through wireless transmission and conducting the healthcare application. For healthcare applications, we construct a data system analysis (DSA) to extract the meaningful physiological information. In this thesis, the DSA is divided into two parts, the digital signal processing and the database analysis. The signal processing algorithm involves the R-peak detection in ECG wave, generating the EDR signal, and preprocessing the Physiological signal. Also, database construction consists of the feature generation, data mining, feature selection, and establish the DSA predictive model. Finally, the experimental results show that the life-behavior patterns of tested subjects may affect HRV and EDR parameters so that would be useful for disease prevention and health management for the user.
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