應用於無線床邊監護系統之高集成度多核生醫信號處理晶片設計與系統實作

Abstract

全球高齡人口的數量(1990年至2025年)已被預測將從3.75億人成長至7.61億人，此劇烈成長的趨勢造成在不久的未來社會將面臨高齡化社會所帶來的健康照護問題。在現今醫務人員短缺的情況下，將使愈來愈多的醫院無法負擔此現象所帶來的健康照護成本。因此，利用創新的科技開發出高整合度的健康照護系統已成為研究人員在最近幾年的重要課題。 生理監視儀是醫院門診間或病房常見的醫療儀器之一，主要用途為量測各項生理參數，目的是為提供醫師與護理人員診斷、照護之參考用。而目前的生理監視儀必須仰賴國外進口，且價格昂貴。導致許多醫院無法更新成功能較齊全的機種，也增加了護理人員進行重症照護時的負擔。量測越多生理訊號時病患身上的連接線也會越多，增加病患量測時的不適感。無線床邊監護系統主要是希望能降低醫療器材的成本消耗與減少病患身上的連接線等多種問題。 本論文提出一應用於無線床邊監護系統之多核生醫信號處理晶片設計與系統實作，此晶片內部包含：線上遞迴獨立成分分析處理引擎來去除腦電波之眼動雜訊，心率變異性分析處理器來計算心電波之心率與心率變異性，基礎生理訊號處理器則收集其餘生理訊號如血壓、血氧、體溫與呼吸速率並傳送至封包處理單元做封裝與傳遞。 無線床邊監護系統則分為無線近身網路、生醫訊號處理與資料顯示儲存三大部分。無線近身網路包含前端擷取電路、數位控制電路與藍芽模組；生醫訊號處理則由數位晶片與藍芽模組組成；最後資料則透過藍芽接收即時顯示在螢幕並儲存在非揮發之儲存媒介。此晶片設計已實現於台灣積體電路90奈米CMOS技術製程下線並完成測試。

The global population of people over the age of 65 is predicted to more than double, from 375 million in 1990 to 761 million in 2025. Healthcare problems relating to the aging population will become a serious social issue. Furthermore, because of the shortage of medical staff, more and more hospitals will be unable to afford the necessary medical interventions. Therefore, innovative and high integrated health-care systems have become an important topic of research in recent years. Physiological monitors are common in medicine to observe of a disease. This instrument can monitor one or several physiological parameters over time for diagnosis. Up till now, monitoring systems were only imported from the foreign countries, and their price were very expensive so that many hospitals cannot update to the instruments with completely function. Measuring more physiological signals would increase the number of wires between monitoring systems and patients. Thus, reducing the number of wires to improve comfort has also become an important issue. This study presents a highly integrated multiprocessor system-on-chip (SoC) design which can process multi-biomedical signals real-time in a wireless bedside monitoring system. This system includes a real-time online recursive independent component analysis (ORICA) processor to automatically remove brain electroencephalogram (EEG) artifacts signals, a heart rate variability (HRV) analysis processor for monitoring electrocardiogram (ECG) signals, and a basic biomedical signal processor for monitoring common physiological data such as oxygen saturation, blood pressure, or body temperature. The implemented wireless bedside monitoring system includes a wireless body sensor network, bio-signal processor, data storage device, and display platform. The wireless body sensor network comprised an analog front-end (AFE) circuit, a digital control module and a commercial Bluetooth module. The digital control module controls the AFE circuit to acquire and digitize the biomedical data. The bio-signal processor processes the biomedical data and sends these data by wireless technology. Finally, biomedical signal packets received by the PC or tablet over the Bluetooth channel are decoded, displayed in real-time, and stored in non-volatile media for further processing and analysis. The multiprocessor chip is fabricated using TSMC 90 nm CMOS technology and completed the chip testing.