| 摘要: | 隨著工商業發展愈來愈快，工作壓力愈來愈大；加上飲食不均衡，罹患心血管疾病的人口愈來愈多。根據2008～2012行政院衛生署統計的國人十大死因，多項死因與心血管疾病有關。近五年臺灣十大死因中，有將近一半的項目為心血管相關疾病；例如心臟病、腦血管疾病、糖尿病、高血壓性疾病以及腎臟方面疾病；另外，心血管相關疾病的死亡人數佔2012臺灣總死亡人數的30.3%，且心臟疾病以及腦血管疾病為心血管相關疾病致死的兩大主因。根據WHO的統計，單是心血管疾病（CVD）的死亡人數佔2008全球總死亡人數的31%。很多患者都在病況嚴重時才就醫檢查；更何況在很多地區如印度與中國大陸由於醫療資源嚴重不足。如何在醫院外的居家自我檢查、早期發現心血管有關疾病是一項重要課題。
近幾年來網路與智慧手機與這一二年興起的智慧手錶、手環等的迅速普再加上醫療器材有微形化、可攜化趨勢；若能掌握這可攜式健康管理於心血管相關的疾病的徵兆判斷商機無可限量。本論文使用Qt程式語言設計心率分析程式，經由穿戴式裝置（如：心電圖手錶、ECG holter等）所量測之心電訊號(Electrocardiography, ECG)，可經由無線藍芽(Bluetooth)傳至不同的分析顯示裝置（如：android 平板電腦、windows筆記型電腦、桌上型電腦），讓使用者可即時掌握自身的健康狀況。本系統使用時域分析與頻域分析，對心跳的徵兆作不同統計性質的描述，包含：交感/副交感神經分析、心電圖推導呼吸率分析（ECG-derived respiration, EDR）、心率變異度分析(Heart rate variability)等，進一步可廣泛應用於心血管相關疾病評估、壓力指數評估、交感神經過度興奮、心率異常、及呼吸終止症關聯性的評估，作為日後心血管疾病（Cardiovascular Diseases, CVD）發展與預防上的評估依據。
;With the fast developments of industrial society, the living pressure of daily life is increasing. Besides, the inappropriate dietary habit also results in the increment of population with cardiovascular problems. In the investigation of top ten leading causes of death during the past five years, more than half of deaths were related to cardiovascular diseases, such as heat failure, brain vascular disease, diabetes, high blood pressure, and kidney-related diseases. It is worthy to notice that more than 30.3% in the death toll in Taiwan was caused by cardiovascular diseases (CVD), and it also caused 31% in global death. Therefore, how to discover vital signs or biomarkers for early detection of CVD in daily life is important issue.
Owing to the novel technologies in internet, smart phones, smart watch, and smart bands in recent years, the miniaturization of medical devices enable the coupling between wearable devices and medical instruments. It becomes a tremendous business market that causes interests of scientist and engineers to pursue. In this thesis, we develop a cross-platform heat-rate analysis program based on QT language. The developed program enables ECG signals measured by smart watch and ECG holter can be wirelessly transmitted to different display device (e.g., android pads, notebook, or desktops) which enables users can monitor their health condition in real time. The present system utilized temporal-frequency analysis to extract different features of heart rates, such as autonomous system regulation, ECG-derived respiration rate (EDR), heart-rate variability (HRV). Further development of the system can be used to estimate other pre-cursors for CVD not only for homecare applications but also for clinics. |
