本文關鍵詞： 光電容積脈搏波 GM(1 1)模型 脈搏波傳導時間 脈壓差測量模型　出處：《中國科學技術大學》2017年博士論文　論文類型：學位論文

【摘要】：心血管疾病(Cardiovascular diseases,CVDs)通常由心臟、血管和血液運輸系統(tǒng)發(fā)生異常導致,根據(jù)世界衛(wèi)生組織(World Health Organization,WHO)2015年公布的數(shù)據(jù)顯示,全球31%的人死于這類疾病,已經(jīng)對人體健康造成嚴重威脅。實現(xiàn)動態(tài)監(jiān)測心率、血壓、血氧濃度和血液黏度等生理參數(shù),對心血管類病癥的診治具有指導作用。目前傳統(tǒng)的生理參數(shù)測量方法由于便攜性差和測量舒適感低等限制,不能滿足連續(xù)測量的需求。人體脈搏波是判斷心血管系統(tǒng)健康與否的重要標準,生理參數(shù)測量模型的研究通常以脈搏波為研究對象。在眾多脈搏波測量方法當中,光電測量法通過特定光譜獲取人體容積脈搏波,測量過程重復性好,舒適度高,能夠?qū)崿F(xiàn)連續(xù)測量。本文重點對光電容積脈搏波(Photoplethysmography,PPG)和不同生理參數(shù)之間的關系進行深入研究,分別為心率、血壓、血液黏度和血氧飽和度四種生理參數(shù)建立對應的測量模型,將測量結果以腕表的形式展現(xiàn),具有很好的學術意義和市場應用價值。本文針對動態(tài)心率測量模型、舒張壓測量模型和血液黏度測量模型的建模難度大,血氧飽和度測量模型定標環(huán)境復雜等問題,進行一系列的探索和研究。第一,選取523nm綠光、660nm紅光和810nm紅外的組合光譜作為探測光源,并在結構設計中引入"目"型槽結構消除環(huán)境光干擾,提高測量信號信噪比。將低功耗通信、微傳感器技術,環(huán)境光學傳感器、低功耗控制處理器和藍牙模塊集成在腕表上,實現(xiàn)硬件層面的信號放大和濾波,完成測量系統(tǒng)的電路設計與實現(xiàn)。第二,在傳統(tǒng)廣義形態(tài)學去除基線漂移算法的基礎上進行改進,實現(xiàn)一種簡化的基線漂移去除算法,以犧牲部分精度為代價實現(xiàn)快速、低功耗的信號處理過程,運算量降低了 4倍。第三,引入灰度預測GM(1,1)模型,對受到運動偽跡干擾導致失真的光電脈搏波信號進行補償,結合加速度模型,建立動態(tài)心率測量模型,經(jīng)過實驗驗證,94%以上的測量結果誤差范圍在+/-6之間;根據(jù)朗伯-比爾定律分析推導脈搏波形態(tài)特征和波形面積等參數(shù)與脈壓差(收縮壓-舒張壓)之間的關系,成功推導得到脈壓差的測量模型,間接實現(xiàn)舒張壓的連續(xù)測量,通過實驗得到測量誤差在+/-10之間,符合美國醫(yī)療器械進步協(xié)會(The Association for the Advancement of Medical Instrumentation,AAMI)的規(guī)定;通過收縮壓和舒張壓計算得到關鍵參數(shù)K,帶入經(jīng)驗公式完成血液黏度的測量,以開放型醫(yī)學數(shù)據(jù)庫MIMIC(Multiparameter IntelligentMonitoringinIntensiveCare)Ⅱ中的記錄數(shù)據(jù)作為實驗分析的對象,得到95%的測量值誤差落在+/-0.5以內(nèi);根據(jù)修正的血氧飽和度測量公式,對未知系數(shù)進行定標,完成與定標環(huán)境相似條件下的血氧濃度的連續(xù)測量模型,經(jīng)過實驗驗證得到測量結果的平均誤差為0.076%。最后,通過手持設備與云服務平臺建立聯(lián)系,采集并存儲多維度生理參數(shù)信息。不但實現(xiàn)生理參數(shù)的遠程連續(xù)監(jiān)測,而且為深度挖掘生理參數(shù)信息并實現(xiàn)健康趨勢分析提供基礎。
[Abstract]:Cardiovascular disease (Cardiovascular diseases, CVDs) is usually composed of heart, blood vessels and blood transport system abnormal cause, according to WHO (World Health Organization, WHO) data released in 2015, 31% of the world's people died from the disease, has been a serious threat to human health. To realize the dynamic monitoring of heart rate, blood pressure, blood oxygen and blood concentration the physiological parameters such as viscosity, diagnosis and treatment of cardiovascular diseases has a guiding role. The physiological parameters of the traditional measuring method due to poor portability and comfort and low measurement limit, can not meet the demand of continuous measurement. Human pulse wave is an important standard to judge whether a healthy cardiovascular system and the study of physiological parameters measurement models usually study in the pulse wave. In many measurement methods of pulse wave, the photoelectric measuring method to obtain the body volume pulse wave through specific spectrum measurement Good repeatability, high comfort, can realize continuous measurement. This paper focuses on the photoplethysmography (Photoplethysmography, PPG) was studied and the relationship between different physiological parameters, respectively. Heart rate, blood pressure, establish the corresponding measurement model of blood viscosity and oxygen saturation of four physiological parameters, the measurement results will be to watch show the form, has great academic significance and market value. In this paper the dynamic heart rate measurement model, the measurement model and the difficulty of modeling diastolic pressure and blood viscosity measurement model, oxygen saturation measurement problem of complex environment model, a series of exploration and research. First, select the 523nm green light spectrum combination 660nm red and 810nm infrared as the detection light source, and the introduction of "eye" type groove structure in the structural design to eliminate the interference of ambient light, increase the signal-to-noise ratio. The low power consumption. The letter, micro sensor technology, environmental optical sensors, low-power control processor and Bluetooth module integrated in watches, signal amplifying and filtering the hardware level, complete the circuit design and Realization of measurement system. In second, improvement in the traditional generalized morphology based algorithm to remove baseline drift, to achieve a simplified baseline drift the removal algorithm, to realize fast at the expense of accuracy, signal processing and low power consumption, the computation is decreased by 4 times. Third, the introduction of gray prediction GM (1,1) model, the motion artifacts caused by compensating the photoelectric pulse wave signal distortion, combined with the acceleration model, establish dynamic heart rate measurement model after experimental verification, measurement results more than 94% of the error in the range of +/-6; according to the Lambert Bill law analysis of pulse wave shape and wave area and the parameters such as pulse pressure (e.g. The systolic pressure and diastolic blood pressure) of the relationship between the pulse pressure measurement model is derived successfully obtained, indirect continuous measurement of diastolic blood pressure, measurement error between +/-10 through experiments, consistent with the United States Association of medical progress (The Association for the Advancement of Medical Instrumentation, AAMI) regulations; the systolic and diastolic blood pressure calculation get the key parameters of K, into the empirical formula of measurement of blood viscosity, with an open type medical database MIMIC (Multiparameter IntelligentMonitoringinIntensiveCare) in the recorded data as the object of experimental analysis, 95% measurement error falls within +/-0.5; according to the measurement of oxygen saturation formula, calibration of the unknown coefficient, continuous the measurement model and the calibration environment similar to the oxygen concentration conditions, the average error after experimental verification obtained measurement results For 0.076%., finally, through handheld devices and cloud service platform to establish links, collect and store multi-dimensional physiological parameter information. Not only achieve physiological parameters of remote continuous monitoring, but also provide a basis for deep excavation of physiological parameter information and health trend analysis.

【學位授予單位】：中國科學技術大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：R318;TP274

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