Statistical Approaches to Analyzing Breath Sample Data in order to Determine Disease Status

分析呼吸样本数据以确定疾病状态的统计方法

• 批准号: 521171-2018
• 负责人: Flemming, Joanna
• 金额: $ 0.72万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Engage Plus Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=650845
• 关键词: Statistical; Approaches; Analyzing; Breath; Sample

Picomole Inc. has developed a non-invasive breath analysis device that can detect pulmonary diseases by**identifying chemical compounds in the breath. We will demonstrate that this device has a sensitivity and**specificity that can so far only be obtained with more invasive surgeries. It is based on absorption**measurements of compounds in the mid-infrared (mid-IR) region obtained using Continuous Wave Cavity**Ring-Down Spectroscopy (CW-CRDS).**We will develop Functional Data Analysis (FDA) techniques for breath sample data that will distinguish**healthy subjects from those with pulmonary disease (e.g. lung cancer). FDA is ideally suited to data that can be**represented as functions or curves (e.g. spectroscopy data). Functional Linear Discriminant Analysis (FLDA) is**an extension of a classical statistical**classification technique (Linear Discriminant Analysis) and can deal with problems unique to functional**datasets. When applied to fabricated breath sample data made available by Picomole, FLDA perfectly**distinguished between subjects with lung cancer and those without. Another classification technique that we**will explore is DDG plot (Depth vs. Depth plots for**G groups) classifiers. DDG plot classifiers apply any statistical classification method to measures of data depth.**Data depth measures quantify how "deep" an observation is in relation to its underlying distribution. DDG plot**classifiers have been applied to other spectrometry datasets with good results and show promise here. Our**methods will be readily extendible to the classification of other disease statuses of interest.**Dalhousie researchers will make monthly visits to Picomole's facilities in Moncton to critically appraise**ongoing data collection and processing methods as well as to communicate modelling results. Picomole will

Picomole公司开发了一种无创呼吸分析设备，可以通过识别呼吸中的化合物来检测肺部疾病。我们将证明该装置具有灵敏度和特异性，迄今为止只能通过更具侵入性的手术获得。它是基于使用连续波腔衰荡光谱（CW-CRDS）获得的中红外（mid-IR）区域化合物的吸收测量。**我们将开发呼气样本数据的功能数据分析（FDA）技术，以区分健康受试者和肺部疾病患者（如肺癌）。FDA非常适合可以**表示为函数或曲线的数据（例如光谱数据）。功能线性判别分析（FLDA）是经典统计分类技术（线性判别分析）的扩展，可以处理功能数据集特有的问题。当应用于Picomole提供的合成呼吸样本数据时，FLDA完美地区分了肺癌患者和非肺癌患者。我们将探索的另一种分类技术是DDG图（**G组的深度vs深度图）分类器。DDG图分类器应用任何统计分类方法来测量数据深度。**数据深度测量量化观测值相对于其底层分布的“深度”。DDG图**分类器已应用于其他光谱数据集，效果良好，在这里显示出希望。我们的方法将很容易扩展到其他感兴趣的疾病状态的分类。达尔豪斯的研究人员将每月访问Picomole在蒙克顿的设施，以严格评估正在进行的数据收集和处理方法，并传达建模结果。皮摩尔将