在慢性阻塞性肺疾病（COPD）患者急性加重期及缓解期，运用语音分析评估其临床及功能状态
Assessing the Clinical and Functional Status of COPD Patients Using Speech Analysis During and After Exacerbation.
作者信息
Mayr Wolfgang, Triantafyllopoulos Andreas, Batliner Anton, Schuller Björn W, Berghaus Thomas M
机构信息
Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Augsburg, Germany.
Chair of Health Informatics (CHI), Department of Clinical Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
出版信息
Int J Chron Obstruct Pulmon Dis. 2025 Jan 20;20:137-147. doi: 10.2147/COPD.S480842. eCollection 2025.
BACKGROUND
Chronic obstructive pulmonary disease (COPD) affects breathing, speech production, and coughing. We evaluated a machine learning analysis of speech for classifying the disease severity of COPD.
METHODS
In this single centre study, non-consecutive COPD patients were prospectively recruited for comparing their speech characteristics during and after an acute COPD exacerbation. We extracted a set of spectral, prosodic, and temporal variability features, which were used as input to a support vector machine (SVM). Our baseline for predicting patient state was an SVM model using self-reported BORG and COPD Assessment Test (CAT) scores.
RESULTS
In 50 COPD patients (52% males, 22% GOLD II, 44% GOLD III, 32% GOLD IV, all patients group E), speech analysis was superior in distinguishing during and after exacerbation status compared to BORG and CAT scores alone by achieving 84% accuracy in prediction. CAT scores correlated with reading rhythm, and BORG scales with stability in articulation. Pulmonary function testing (PFT) correlated with speech pause rate and speech rhythm variability.
CONCLUSION
Speech analysis may be a viable technology for classifying COPD status, opening up new opportunities for remote disease monitoring.
背景
慢性阻塞性肺疾病（COPD）会影响呼吸、言语产生和咳嗽。我们评估了用于对COPD疾病严重程度进行分类的语音机器学习分析。
方法
在这项单中心研究中，前瞻性招募了非连续性COPD患者，以比较他们在COPD急性加重期间及之后的言语特征。我们提取了一组频谱、韵律和时间变异性特征，将其用作支持向量机（SVM）的输入。我们预测患者状态的基线是使用自我报告的博格量表（BORG）和慢性阻塞性肺疾病评估测试（CAT）分数的支持向量机模型。
结果
在50例COPD患者中（52%为男性，22%为全球慢性阻塞性肺疾病倡议组织（GOLD）II级，44%为GOLD III级，32%为GOLD IV级，所有患者均为E组），与单独使用博格量表和CAT分数相比，言语分析在区分加重期和加重期后状态方面更具优势，预测准确率达到84%。CAT分数与阅读节奏相关，博格量表与发音稳定性相关。肺功能测试（PFT）与言语停顿率和言语节奏变异性相关。
结论
言语分析可能是一种用于对COPD状态进行分类的可行技术，为远程疾病监测开辟了新机会。
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