PDF(Pubmed)
Abstract：
Breathing is one of the body\'s most basic functions and abnormal breathing can indicate underlying cardiopulmonary problems. Monitoring respiratory abnormalities can help with early detection and reduce the risk of cardiopulmonary diseases. In this study, a 77 GHz frequency-modulated continuous wave (FMCW) millimetre-wave (mmWave) radar was used to detect different types of respiratory signals from the human body in a non-contact manner for respiratory monitoring (RM). To solve the problem of noise interference in the daily environment on the recognition of different breathing patterns, the system utilised breathing signals captured by the millimetre-wave radar. Firstly, we filtered out most of the static noise using a signal superposition method and designed an elliptical filter to obtain a more accurate image of the breathing waveforms between 0.1 Hz and 0.5 Hz. Secondly, combined with the histogram of oriented gradient (HOG) feature extraction algorithm, K-nearest neighbours (KNN), convolutional neural network (CNN), and HOG support vector machine (G-SVM) were used to classify four breathing modes, namely, normal breathing, slow and deep breathing, quick breathing, and meningitic breathing. The overall accuracy reached up to 94.75%. Therefore, this study effectively supports daily medical monitoring.
摘要：
呼吸是人体最基本的功能之一，呼吸异常可能表明潜在的心肺问题。监测呼吸异常可以帮助早期发现并降低心肺疾病的风险。在这项研究中,使用77GHz调频连续波（FMCW）毫米波（mmWave）雷达以非接触方式检测来自人体的不同类型的呼吸信号，以进行呼吸监测（RM）。为解决日常环境中噪声干扰对不同呼吸模式的识别问题,该系统利用毫米波雷达捕获的呼吸信号。首先,我们使用信号叠加方法滤除了大部分静态噪声，并设计了一个椭圆滤波器，以获得0.1Hz至0.5Hz之间更准确的呼吸波形图像。其次,结合方向梯度直方图(HOG)特征提取算法,K-最近邻(KNN),卷积神经网络(CNN)和HOG支持向量机(G-SVM)对四种呼吸模式进行分类，即,正常呼吸,缓慢而深呼吸，快速呼吸,和脑膜炎呼吸。整体精度达到94.75%。因此,这项研究有效地支持日常医疗监测。
