心血管疾病一直以來都是全球的主要死因，近十幾年來，不論是高度開發且富裕的國家，或是開發程度較低、較為貧窮的國家，心血管疾病始終穩居致死原因的前三名，且隨著開發程度的提高，其排名就越是往上攀升，這可怕的盛行程度使得心臟相關研究至今依然十分地重要。為了能夠控制或減低這疾病帶來的危害，心臟狀態的監測與生理情形的紀錄就成了必須執行的動作，而這些檢測皆須要收集人的生理信號，才能藉此評估其狀態。而心電圖的量測正是記錄心臟活動基本而重要的方法，也能進一步推估更多的生理參數，獲得更全面的資訊。又，在現今這個健康意識高漲的年代，開發一穿戴式的系統用於監測心臟狀況已是一種趨勢，而且在諸多場景與應用上皆有其需求，從個人用戶、照護機構到醫院體系，從健康、運動需求到亞健康族群，甚至患病需長期監控者，如果有一輕巧便攜易穿戴的心電檢測器，配合智慧型裝置去發展應用，相信一定能讓心血管疾病的影響受到更多的抑制。
本篇論文將以心電訊號擷取裝置為核心，並延伸至不同面向的醫療應用。首先，第一部分介紹一心律不整檢測系統的開發並進行臨床測試之應用。本系統包含三個主要系統功能區塊：生醫訊號檢測電路、數位訊號處理單元、軟體辨識與顯示介面，其中，系統中的晶片部分是以TSMC 0.18μm standard CMOS 製程所製造。軟體端除了可以記錄使用者的心電圖、計算RR區間等生理資訊外，亦可進行心率變異分析與初步病症分析。此檢測系統之心率演算法在MIT-BIH資料庫驗證下，其心跳辨識率大於99.4%。此心律不整檢測系統亦正在進行臨床試驗，自2016年3月開始進行臨床收案，本論文提出數個案例進行結果討論，此檢測系統能有效地檢測心電訊號，並進行後續心律不整之辨識。
另一方面，在心血管疾病的檢測與判別中，心臟聽診一直是一相當快速且基本的診斷方式。本論文之第二大項應用即是提出一智慧聽診器系統，結合心律檢測與心音收集之概念，系統實現上包含晶片設計、韌體撰寫與軟體開發。在聽診時，醫生可參考在行動裝置上呈現的生理訊號並同時聽取心音訊號，以達到更有依據且有效之診斷。此系統將生理訊號視覺化且保留原本的診斷方式，希望能提升醫生聽診之效率，後續也得以藉由本系統建立心電結合心音的資料庫，使未來心臟疾病相關之學習環境與診斷效率能夠有更好的提升。
本論文中的兩大項應用皆是以心電訊號的量測作為出發點，期許未來將這些系統應用更進一步優化之後，能對於健康照護與醫療環境的改善盡一份心力。

Cardiovascular disease is the main cause of death worldwide. Its prevalence makes heart-related studies still important. It is necessary to acquire physiological signals to access the state of a person for the consideration of health condition monitoring. Since electrocardiography (ECG) is a fundamental and common method to record the activity of the heart, much physiological information can then be accessed. To better the healthcare environment, developing a wearable system to acquire the heart status becomes a trend.
The dissertation is based on ECG monitoring system and its application in two different aspects. The first is the development of an arrhythmia monitoring system and human study. The chip implementation, design of arrhythmia monitoring system, QRS complex detection and classification are all discussed in this part. And, heart rate variability analysis is also adopted to estimate the physiological condition of a person. The second is an ECG and phonocardiogram (PCG) monitoring system for cardiac auscultation. To assist doctors in making diagnoses more rapid and accurate, the proposed system is capable of measuring the PCG and ECG simultaneously. By taking advantage of signal visualization, the uncertainty of heart sounds can be eliminated, and the training period to acquire auscultation skills can be reduced.
Two applications in this dissertation are all based on ECG signal acquisition. After the further optimization in the future, these applications can expectantly improve the health care and medical environment. The burden of long-term care can then be relieved.

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