本文選題：脈搏信號采集系統(tǒng) + 脈搏信號預(yù)處理�。� 參考：《哈爾濱工業(yè)大學》2014年博士論文

【摘要】：脈搏信號是一種重要的人體生理信號,包含了豐富的循環(huán)系統(tǒng)信息。由于脈搏的搏動在體表就能觸摸到,因而脈搏信號的獲取具有無痛苦、采集簡單、成本低廉等優(yōu)勢。脈搏信號的分析自古就備受醫(yī)學界青睞,在古代中醫(yī)、古代印度醫(yī)學、古埃及醫(yī)學中脈診都占有十分重要的地位。在利用脈診診病時醫(yī)師將手指放置于患者橈動脈的寸、關(guān)、尺三個穴位,通過指腹的觸感來感受不同取脈壓力下脈搏的搏動從而分析患者的身體狀況。然而,脈診的技巧需要長期的學習和實踐才能掌握,而且這種診法的主觀性較強,存在不同醫(yī)師診斷結(jié)果不一致的問題。為了克服這些問題,近年來越來越多的學者利用傳感器技術(shù)獲取脈搏信號并利用計算機技術(shù)來分析病人的健康狀況,使脈診技術(shù)向客觀化的方向發(fā)展。在脈搏信號的獲取與分析中主要包括脈搏信號的獲取、預(yù)處理、特征提取與模式分類等內(nèi)容,本文從這四方面出發(fā)對脈搏信號的獲取與分析展開了系統(tǒng)的研究。脈搏信號的獲取是脈搏信號分析研究的基礎(chǔ)。近年來不同的研究機構(gòu)研發(fā)了各種各樣的脈搏信號獲取系統(tǒng),然而現(xiàn)有的采集設(shè)備仍存在一些不足以待改進,首先現(xiàn)有的脈搏信號采集系統(tǒng)大多沒有定位輔助系統(tǒng),將傳感器放置于患者手腕的過程仍然很大程度依賴采集人員的經(jīng)驗,使得位置的選擇較為主觀和費時。其次現(xiàn)有的系統(tǒng)不能自動控制采樣壓力,使得壓力的設(shè)置較為主觀和費時。另外現(xiàn)有設(shè)備在測量脈寬和寸、關(guān)、尺三個部位同時采集等方面也存在一些欠缺。針對這些問題,本文設(shè)計了新的脈搏信號獲取系統(tǒng)。通過該系統(tǒng)可以快速的找到合理的采樣位置并根據(jù)預(yù)設(shè)的壓力自動調(diào)節(jié)探頭高度,減少了采集過程中的主觀因素并加快了采樣速度,提高了采樣的客觀性。本文的系統(tǒng)還具有測量脈寬和寸、關(guān)、尺三路同時采集的功能。脈搏信號的預(yù)處理旨在去除耦合在脈搏信號中的干擾,提升脈搏信號的質(zhì)量從而提高后續(xù)的特征提取以及模式分類的準確性�，F(xiàn)有的預(yù)處理過程通常關(guān)注去除脈搏信號中耦合的噪聲和基線漂移等干擾,然而有一些干擾由于發(fā)生了信息損失或者與脈搏信號的頻帶重疊使得這類干擾的處理去除難度較大,如飽和、偽跡這兩種干擾很難通過現(xiàn)有的預(yù)處理方法得到處理。這使得預(yù)處理之后脈搏數(shù)據(jù)集中包含異常樣本,從而影響了特征提取和分類的準確性。針對這個問題本文擴展了原預(yù)處理框架,增加了飽和檢測模塊和偽跡檢測模塊,并提出了基于差分的飽和檢測算法和基于復(fù)雜網(wǎng)絡(luò)連通性的偽跡檢測算法,可以有效的檢測飽和與偽跡這兩種常見干擾。脈搏信號的特征提取是脈搏信號分析的另一個重要課題。目前常用的脈搏信號特征大體上可以分為兩類:一類是以分析脈搏周期的特點為出發(fā)點的基于單個脈搏周期的特征提取方法;一類是以分析脈搏信號整體性質(zhì)為出發(fā)點的基于整個脈搏信號的脈搏特征提取方法。脈搏信號是一個準周期信號,各個周期之間存在一定的差異,我們認為這種差異具有一定的診斷意義尤其是對一些與節(jié)律相關(guān)的疾病的診斷。然而這兩種方法都沒有注重對脈搏信號周期間差異的刻畫,本文從刻畫脈搏信號周期間差異出發(fā)提出了三種對周期間差異敏感的特征。實驗結(jié)果表明周期間的差異對于疾病診斷具有重要意義,尤其是在一些對周期間差異較敏感的疾病的診斷。由于本文同時考慮了脈搏信號周期間和周期內(nèi)的差異,在分類實驗中本文所提取的特征獲得了相對于其他特征更好的識別效果。在脈搏信號的分類研究中本文比較了壓力脈搏信號與另外兩種較為常見的脈搏信號(光電脈搏信號和超聲脈搏信號)的分類性能,并提出基于組合核模型的脈搏信號融合分類算法。通過分析三種不同的脈搏信號的的采集原理、物理意義及其相互聯(lián)系,本文討論了不同類型脈搏信號各自的敏感特征,我們認為不同類型的脈搏信號對于不同疾病的診斷效果有一定的差別,如果某種疾病所引起的病變與某種類型脈搏信號的敏感特征相關(guān),則在這種疾病的診斷中該類型的脈搏信號具有一定的診斷精度優(yōu)勢。實驗結(jié)果表明,超聲脈搏信號在糖尿病的診斷中取得了相對其他脈搏信號更好的診斷效果,而壓力信號在動脈硬化的診斷上取得了更好的診斷結(jié)果。利用信號之間的互補性,融合使用多種脈搏信號我們可以進一步獲得更多有效地診斷信息。為此我們提出了基于組合核模型的多種脈搏信號的融合分類算法,并在實驗中取得了比使用單一脈搏信號更高的診斷精度。
[Abstract]:Pulse signal is an important physiological signal of human body. It contains abundant information of circulatory system. Because pulse pulsation can be touched on the body surface, pulse signal has the advantages of painless, simple collection, low cost and so on. Pulse signal analysis has attracted much attention from medical circles since ancient times, in ancient Chinese medicine, ancient India medicine, Pulse diagnosis in ancient Egyptian medicine occupies a very important position. In the use of pulse diagnosis, doctors place fingers in the three points of the patient's radial artery in the patient's radial artery, through the touch of the finger to the pulse pulsation under different pulse pressure to analyze the patient's physical condition. However, the technique of pulse diagnosis needs long-term learning and practice. In order to overcome these problems, in order to overcome these problems, in order to overcome these problems, in order to overcome these problems, more and more scholars have used the sensor technology to obtain pulse signal and use the computer technology to analyze the patient's health condition, and make the pulse diagnosis technology develop to the objective direction. The acquisition and analysis of stroke signal mainly include the acquisition of pulse signal, preprocessing, feature extraction and pattern classification. In this paper, the acquisition and analysis of pulse signal is systematically studied from these four aspects. The acquisition of pulse signal is the basis of pulse signal analysis. In recent years, different research institutions have developed various kinds of research institutions. All kinds of pulse signal acquisition system, however, the existing acquisition equipment still has some shortcomings to improve. First of all, the existing pulse signal acquisition system mostly does not have a positioning auxiliary system. The process of placing the sensor on the wrist is still largely dependent on the experience of the acquisition personnel, making the selection of the position more subjective and time-consuming. The existing system can not automatically control the sampling pressure, making the pressure setting more subjective and time-consuming. In addition, the existing equipment also has some deficiencies in the measurement of the pulse width and inch, the three parts of the scale, the ruler and the ruler. In this paper, a new pulse signal acquisition system is designed. The sampling position and the height of the probe automatically adjust to the preset pressure to reduce the subjective factors in the acquisition process and accelerate the sampling speed and improve the objectivity of the sampling. The system also has the functions of measuring pulse width and inch, Guan, and ruler three. The preprocessing of pulse signal signal is designed to remove coupling in pulse signal. Interference to improve the quality of the pulse signal and improve the accuracy of subsequent feature extraction and pattern classification. The existing preprocessing process is usually focused on removing interference from the coupled noise and baseline drift in the pulse signal, however, some interference is caused by the loss of information or the overlap of the frequency band of the pulse signal. The removal of disturbance is difficult to remove, such as saturation, and the two kinds of artifacts, such as pseudo trace, are difficult to be processed by the existing preprocessing methods. This makes the pulse data set after preprocessing to include abnormal samples, thus affecting the accuracy of feature extraction and classification. This paper extends the original preprocessing framework and adds saturation detection mode to this problem. Block and artifact detection module, and propose a difference based saturation detection algorithm and the pseudo trace detection algorithm based on the connectivity of complex network. It can effectively detect two common disturbances such as saturation and artifact. The feature extraction of pulse signal is another important lesson in pulse signal analysis. It is divided into two categories: one is a feature extraction method based on a single pulse cycle based on the analysis of the characteristics of the pulse cycle. One is a pulse feature extraction method based on the whole pulse signal based on the analysis of the whole pulse signal. The pulse signal is a quasi periodic signal, and there is a certain difference between each cycle. Different, we think this difference has a certain diagnostic significance, especially for some diseases related to rhythm. However, these two methods do not pay attention to the depiction of the difference between the pulse signal cycles. In this paper, we put forward three kinds of characteristics that are sensitive to the cycle difference from the difference between the period of the pulse signal. The difference between cycles is important for the diagnosis of disease, especially in the diagnosis of diseases that are more sensitive to periodic differences. In this paper, the characteristics obtained in this paper are better than other characteristics in the classification experiments. In the classification study, the classification performance of the pressure pulse signal and the other two more common pulse signals (the photoelectric pulse signal and the ultrasonic pulse signal) is compared, and the pulse signal fusion classification algorithm based on the combined kernel model is proposed. By analyzing the acquisition principle of the three different pulse signals, the physical meaning and the interconnected phase are analyzed. In this paper, we discuss the sensitive characteristics of different types of pulse signals. We believe that different types of pulse signals have a certain difference in the diagnosis of different diseases. If the lesion caused by a disease is related to the sensitive characteristics of a certain type of pulse signal, the pulse signal of this type of disease is in the diagnosis of this type of disease. The experimental results show that the ultrasonic pulse signal is better than the other pulse signals in the diagnosis of diabetes, and the pressure signal has achieved better diagnostic results in the diagnosis of arteriosclerosis. More effective diagnosis information is obtained step by step. Therefore, we propose a fusion classification algorithm based on combined kernel model for multiple pulse signals. In the experiment, the accuracy of diagnosis is higher than that of single pulse signal.

【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：R241;TN911.6

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本文編號：1863227