TCS Research and Innovation, Kolkata-700156, India.
Sensors (Basel). 2018 Apr 25;18(5):1322. doi: 10.3390/s18051322.
Smoking causes unalterable physiological abnormalities in the pulmonary system. This is emerging as a serious threat worldwide. Unlike spirometry, tidal breathing does not require subjects to undergo forceful breathing maneuvers and is progressing as a new direction towards pulmonary health assessment. The aim of the paper is to evaluate whether tidal breathing signatures can indicate deteriorating adult lung condition in an otherwise healthy person. If successful, such a system can be used as a pre-screening tool for all people before some of them need to undergo a thorough clinical checkup. This work presents a novel systematic approach to identify compromised pulmonary systems in smokers from acquired tidal breathing patterns. Tidal breathing patterns are acquired during restful breathing of adult participants. Thereafter, physiological attributes are extracted from the acquired tidal breathing signals. Finally, a unique classification approach of locally weighted learning with ridge regression (LWL-ridge) is implemented, which handles the subjective variations in tidal breathing data without performing feature normalization. The LWL-ridge classifier recognized compromised pulmonary systems in smokers with an average classification accuracy of 86.17% along with a sensitivity of 80% and a specificity of 92%. The implemented approach outperformed other variants of LWL as well as other standard classifiers and generated comparable results when applied on an external cohort. This end-to-end automated system is suitable for pre-screening people routinely for early detection of lung ailments as a preventive measure in an infrastructure-agnostic way.
吸烟会导致肺部系统不可改变的生理异常。这在全球范围内正成为一个严重的威胁。与肺活量测定不同，潮气呼吸不需要受试者进行强力呼吸动作，而是作为一种新的肺部健康评估方向正在发展。本文的目的是评估潮气呼吸特征是否可以指示健康人中成人肺部状况的恶化。如果成功，这样的系统可以作为所有人的预筛选工具，以便在其中一些人需要进行全面临床检查之前使用。本工作提出了一种从获得的潮气呼吸模式中识别吸烟者受损的肺部系统的新的系统方法。在成年参与者休息时获得潮气呼吸模式。然后，从获得的潮气呼吸信号中提取生理属性。最后，实现了一种独特的局部加权学习与岭回归（LWL-ridge）分类方法，该方法处理了潮气呼吸数据中的主观变化，而无需执行特征归一化。LWL-ridge 分类器以 86.17%的平均分类准确率、80%的灵敏度和 92%的特异性识别了吸烟者受损的肺部系统。所实现的方法优于 LWL 的其他变体以及其他标准分类器，并且在应用于外部队列时生成了可比的结果。这种端到端的自动化系统适用于常规对人们进行预筛选，以便以基础设施无关的方式作为预防措施来早期发现肺部疾病。
