当前位置：
X-MOL 学术
›
J. Biomed. Inform.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
Investigating voice as a biomarker: Deep phenotyping methods for early detection of Parkinson's disease.
Journal of Biomedical informatics ( IF 4.0 ) Pub Date : 2019-12-19 , DOI: 10.1016/j.jbi.2019.103362 John M Tracy 1 , Yasin Özkanca 2 , David C Atkins 3 , Reza Hosseini Ghomi 4
Journal of Biomedical informatics ( IF 4.0 ) Pub Date : 2019-12-19 , DOI: 10.1016/j.jbi.2019.103362 John M Tracy 1 , Yasin Özkanca 2 , David C Atkins 3 , Reza Hosseini Ghomi 4
Affiliation
|
Voice technology has grown tremendously in recent years and using voice as a biomarker has also been gaining evidence. We demonstrate the potential of voice in serving as a deep phenotype for Parkinson's Disease (PD), the second most common neurodegenerative disorder worldwide, by presenting methodology for voice signal processing for clinical analysis. Detection of PD symptoms typically requires an exam by a movement disorder specialist and can be hard to access and inconsistent in findings. A vocal digital biomarker could supplement the cumbersome existing manual exam by detecting and quantifying symptoms to guide treatment. Specifically, vocal biomarkers of PD are a potentially effective method of assessing symptoms and severity in daily life, which is the focus of the current research. We analyzed a database of PD patient and non-PD subjects containing voice recordings that were used to extract paralinguistic features, which served as inputs to machine learning models to predict PD severity. The results are presented here and the limitations are discussed given the nature of the recordings. We note that our methodology only advances biomarker research and is not cleared for clinical use. Specifically, we demonstrate that conventional machine learning models applied to voice signals can be used to differentiate participants with PD who exhibit little to no symptoms from healthy controls. This work highlights the potential of voice to be used for early detection of PD and indicates that voice may serve as a deep phenotype for PD, enabling precision medicine by improving the speed, accuracy, accessibility, and cost of PD management.
中文翻译：
研究语音作为生物标志物：帕金森氏病早期检测的深表型方法。
语音技术近年来发展迅猛，使用语音作为生物标记物也获得了证据。通过展示用于临床分析的语音信号处理方法，我们证明了语音在帕金森氏病（PD）（全球第二大最常见的神经退行性疾病）的深表型中的潜力。PD症状的检测通常需要运动障碍专家进行检查，并且可能难以获取且结果不一致。语音数字生物标记可以通过检测和量化症状来指导治疗，从而补充现有的繁琐的手动检查。具体而言，PD的声带生物标志物是评估日常生活中症状和严重程度的潜在有效方法，这是当前研究的重点。我们分析了PD患者和非PD受试者的数据库，这些数据库包含用于提取旁语功能的语音记录，这些数据用作机器学习模型的输入以预测PD的严重程度。此处介绍了结果，并根据录音的性质讨论了限制。我们注意到，我们的方法仅推进了生物标志物的研究，并未明确用于临床。具体来说，我们证明了应用于语音信号的常规机器学习模型可用于区分PD参与者与健康对照之间表现出很少甚至没有症状。这项工作强调了语音可以用于PD的早期检测的潜力，并表明语音可以作为PD的一种深表型，可以通过提高速度，准确性，可访问性，
更新日期：2019-12-19
中文翻译：
研究语音作为生物标志物：帕金森氏病早期检测的深表型方法。
语音技术近年来发展迅猛，使用语音作为生物标记物也获得了证据。通过展示用于临床分析的语音信号处理方法，我们证明了语音在帕金森氏病（PD）（全球第二大最常见的神经退行性疾病）的深表型中的潜力。PD症状的检测通常需要运动障碍专家进行检查，并且可能难以获取且结果不一致。语音数字生物标记可以通过检测和量化症状来指导治疗，从而补充现有的繁琐的手动检查。具体而言，PD的声带生物标志物是评估日常生活中症状和严重程度的潜在有效方法，这是当前研究的重点。我们分析了PD患者和非PD受试者的数据库，这些数据库包含用于提取旁语功能的语音记录，这些数据用作机器学习模型的输入以预测PD的严重程度。此处介绍了结果，并根据录音的性质讨论了限制。我们注意到，我们的方法仅推进了生物标志物的研究，并未明确用于临床。具体来说，我们证明了应用于语音信号的常规机器学习模型可用于区分PD参与者与健康对照之间表现出很少甚至没有症状。这项工作强调了语音可以用于PD的早期检测的潜力，并表明语音可以作为PD的一种深表型，可以通过提高速度，准确性，可访问性，
京公网安备 11010802027423号