【摘要】：呼吸生理參數(shù)是人們了解自身狀況的重要參考指標(biāo),隨著健康意識(shí)的深入人心,人們更期望長(zhǎng)期的呼吸檢測(cè),這對(duì)呼吸信號(hào)的采集裝置提出更高的要求。慣性傳感器作為一種新型技術(shù),可用于智能穿戴、人體傳感網(wǎng)絡(luò)和呼吸檢測(cè)等領(lǐng)域,近年來(lái)得到學(xué)術(shù)界廣泛的研究和探索。但目前,還未出現(xiàn)基于單通道的呼吸類(lèi)型分類(lèi)的研究。慣性傳感器包括加速度計(jì)和角速度計(jì)(陀螺儀),基于角速度計(jì)在獲取呼吸信號(hào)質(zhì)量上的優(yōu)勢(shì),本課題選用角速度計(jì)獲取的呼吸信號(hào),即呼吸角速度信號(hào)做研究。通過(guò)搭建平臺(tái),對(duì)從單通道呼吸角速度信號(hào)提取呼吸參數(shù)的可行性進(jìn)行了分析,并研究了單通道呼吸角速度信號(hào)在提取呼吸相位和呼吸信號(hào)分類(lèi)上的應(yīng)用。研究的主要工作可分為分析和應(yīng)用兩個(gè)部分。在分析方面,本文主要分了兩個(gè)部分:信號(hào)采集和呼吸參數(shù)的提取分析。其中,在信號(hào)的采集方面使用單個(gè)慣性傳感器放置在胸骨上切跡以獲取呼吸角速度信號(hào),單通道設(shè)備方便了呼吸信號(hào)的采集,胸骨上切跡位置保證了單通道呼吸數(shù)據(jù)的魯棒性。呼吸參數(shù)的提取分析部分則選用了呼吸頻率和呼吸相位作為參數(shù)進(jìn)行分析,選用呼吸二氧化碳濃度信號(hào)作為參考呼吸信號(hào)進(jìn)行參數(shù)對(duì)比,結(jié)果表明呼吸頻率位于置信區(qū)間內(nèi)且相移中位誤差低于0.5秒。在應(yīng)用方面,首先用呼吸角速度信號(hào)提取了呼吸相位,通過(guò)上位機(jī)界面軟件系統(tǒng)的設(shè)計(jì)將呼吸角速度轉(zhuǎn)換為易于識(shí)別的呼吸角度信號(hào)從而方便呼吸相位的提取,此外還介紹了肺功能康復(fù)治療儀的設(shè)計(jì),該儀器是呼吸相位的應(yīng)用點(diǎn)之一,也是本論文的工作量之一。其次對(duì)呼吸信號(hào)進(jìn)行了分類(lèi),選用了七類(lèi)常見(jiàn)的呼吸異常信號(hào)和正常呼吸信號(hào),基于前人的研究基礎(chǔ),使用支持向量機(jī)技術(shù)設(shè)計(jì)分類(lèi)器進(jìn)行模式識(shí)別。在分類(lèi)器的設(shè)計(jì)過(guò)程中結(jié)合了多種技術(shù)來(lái)獲取較優(yōu)的特征值,特征值包括:均值、方差、能量、過(guò)閾值呼吸數(shù)和符號(hào)聚集近似值,為了提高特征值的有效性使用了小波技術(shù)和窗口分割的技術(shù)。使用十折交叉驗(yàn)證的方式獲得的分類(lèi)準(zhǔn)確率最高達(dá)到91.25%,驗(yàn)證了單通道呼吸角速度信號(hào)在分類(lèi)呼吸類(lèi)型應(yīng)用上的可行性。綜上,本課題的研究結(jié)果表明:慣性傳感器采集的單通道呼吸角速度信號(hào)能夠代替?zhèn)鹘y(tǒng)的呼吸檢測(cè)儀器獲取呼吸頻率,并且能獲取較準(zhǔn)確的呼吸相位;該信號(hào)能夠用于提取呼吸相位并在常見(jiàn)呼吸類(lèi)型的分類(lèi)上有較好的分類(lèi)效果。本文的工作提出了呼吸檢測(cè)的新方式,為長(zhǎng)期呼吸檢測(cè)提供了可靠的思路,可應(yīng)用于呼吸相關(guān)疾病的早期預(yù)警。
[Abstract]:Respiratory physiological parameters are an important reference index for people to understand their own conditions. with the deepening of health awareness, people expect long-term respiratory detection, which puts forward higher requirements for respiratory signal acquisition devices. As a new technology, inertial sensor can be used in intelligent wear, human body sensor network and respiratory detection. In recent years, inertial sensor has been widely studied and explored in academic circles. However, at present, there is no research on the classification of respiratory types based on single channel. Inertial sensors include accelerometer and angular velocimeter (gyroscope). Based on the advantages of angular velocimeter in obtaining respiratory signal quality, the respiratory signal obtained by angular velocimeter, that is, respiratory angular velocity signal, is selected for research in this paper. By building a platform, the feasibility of extracting respiratory parameters from single channel respiratory angular velocity signals is analyzed, and the application of single channel respiratory angular velocity signals in extracting respiratory phase and classification of respiratory signals is studied. The main work of the study can be divided into two parts: analysis and application. In the aspect of analysis, this paper is divided into two parts: signal acquisition and respiratory parameter extraction and analysis. In the aspect of signal acquisition, a single inertial sensor is used to place a notch on the sternum to obtain the respiratory angular velocity signal. The single-channel equipment facilitates the acquisition of respiratory signal, and the location of the notch on the sternum ensures the robustness of the single-channel respiratory data. In the part of respiratory parameter extraction and analysis, respiratory frequency and respiratory phase were selected as parameters, and respiratory carbon dioxide concentration signal was used as reference respiratory signal for parameter comparison. The results showed that respiratory frequency was in confidence interval and phase shift median error was less than 0.5 seconds. In the aspect of application, the respiratory phase is extracted by respiratory angular velocity signal, and the respiratory angular velocity is converted into easily identifiable respiratory angle signal through the design of upper computer interface software system, so as to facilitate the extraction of respiratory phase. in addition, the design of pulmonary function rehabilitation therapy instrument is also introduced, which is one of the application points of respiratory phase and one of the workload of this paper. Secondly, the respiratory signals are classified, and seven kinds of respiratory abnormal signals and normal respiratory signals are selected. Based on the previous research basis, support vector machine (SVM) technology is used to design classifiers for pattern recognition. In the design process of the classifier, a variety of techniques are combined to obtain the better eigenvalues, including mean, variance, energy, over-threshold respiration and symbolic aggregation approximations. in order to improve the effectiveness of eigenvalues, wavelet technique and window segmentation technique are used. The highest classification accuracy is 91.5% by using ten fold cross verification, which verifies the feasibility of the application of single channel respiratory angular velocity signal in the classification of respiratory types. In summary, the results of this paper show that the single channel respiratory angular velocity signal collected by inertial sensor can replace the traditional respiratory detection instrument to obtain respiratory frequency, and can obtain more accurate respiratory phase, and the signal can be used to extract respiratory phase and has a good classification effect on the classification of common respiratory types. In this paper, a new method of respiratory detection is proposed, which provides a reliable idea for long-term respiratory detection and can be used for early warning of respiratory related diseases.
【學(xué)位授予單位】：深圳大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TN911.7;R443.6

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本文編號(hào)：2511207