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首页> 外文会议>IEEE International Conference on Bioinformatics and Biomedicine >Prediction Indicators for Acute Exacerbations of Chronic Obstructive Pulmonary Disease By Combining Non-linear analyses and Machine
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Prediction Indicators for Acute Exacerbations of Chronic Obstructive Pulmonary Disease By Combining Non-linear analyses and Machine

机译：非线性分析与机器相结合的慢性阻塞性肺疾病急性加重的预测指标

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Acute exacerbations are important episodes in the course of chronic obstructive pulmonary disease (COPD) which is associated with a significant increase in mortality, hospitalization and impaired quality of life. An important treatment for COPD is home telehealth-monitoring intervention. Physiological signals monitored continuously with home ventilators would help us address disease condition in time. However, the absence of useful early predictors and poor accuracy and sensitivity of algorithms limit the effectiveness of home telemonitoring interventions. In order to find prediction indicators and improve the accuracy from physiological signals, we developed a prediction method to search for indicators connected with acute exacerbations. In this study, we analyzed one-month physiological data (airflow and oxygen saturation signals) of 22 patients with COPD before acute exacerbations happened. In the analysis we employed non-linear analyses and machine learning. We applied Multiscale entropy analysis (MSE) and Detrend fluctuation analysis (DFA) to extract features from airflow. Random forest (RF), linear discriminant analysis (LDA) and support vector machine (SVM) were used to classify the stable state and acute exacerbations of disease. The results showed that LDA had the best average precision of 62% and SVM had the best average recall of 56%. Additionally, according to the analysis of RF, the most predictive features are mean of airflow, results of DFA and MSE in scale 4. RF shows a highest accuracy of 75% in three methods, when LDA illustrates a highest specificity of 42.9%. This study will provide insights in developing COPD home-monitoring system which can prognose the onset of acute exacerbations, thus reducing the need of hospital admissions and improving the life quality of COPD patients.
机译：急性加重是慢性阻塞性肺疾病（COPD）过程中的重要事件，与死亡率，住院和生活质量显着增加有关。 COPD的重要治疗方法是家庭远程医疗监控干预。用家用呼吸机连续监测的生理信号将有助于我们及时处理疾病状况。但是，缺乏有用的早期预测器以及算法的准确性和敏感性较差，限制了家庭远程监控干预措施的有效性。为了找到预测指标并提高生理信号的准确性，我们开发了一种预测方法来搜索与急性加重有关的指标。在这项研究中，我们分析了22例COPD患者在急性加重发生之前的一个月生理数据（气流和血氧饱和度信号）。在分析中，我们采用了非线性分析和机器学习。我们应用了多尺度熵分析（MSE）和趋势趋势分析（DFA）从气流中提取特征。使用随机森林（RF），线性判别分析（LDA）和支持向量机（SVM）对疾病的稳定状态和急性发作进行分类。结果表明，LDA的平均召回率最高，为62％，SVM的平均召回率最高，为56％。此外，根据RF的分析，最具预测性的特征是气流的平均值，DFA和MSE的比例为4。RF在三种方法中显示出75％的最高准确度，而LDA显示出42.9％的最高特异性。这项研究将为开发COPD家庭监护系统提供见解，该系统可以预测急性加重发作的发生，从而减少入院的需要并改善COPD患者的生活质量。

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