Tracy John M, Özkanca Yasin, Atkins David C, Hosseini Ghomi Reza
Member of DigiPsych Lab, University of Washington, Seattle, WA, USA.
Electrical & Electronics Engineering, Ozyegin University, Istanbul, Turkey.
J Biomed Inform. 2020 Apr;104:103362. doi: 10.1016/j.jbi.2019.103362. Epub 2019 Dec 19.
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.
近年来，语音技术取得了巨大发展，将语音作为生物标志物也越来越有证据支持。通过展示用于临床分析的语音信号处理方法，我们证明了语音作为全球第二常见神经退行性疾病帕金森病（PD）深度表型的潜力。PD症状的检测通常需要运动障碍专家进行检查，而且很难获得检查机会，检查结果也不一致。一种语音数字生物标志物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手动检查。具体而言，PD的语音生物标志物是评估日常生活中症状和严重程度的一种潜在有效方法，这也是当前研究的重点。我们分析了一个包含PD患者和非PD受试者语音记录的数据库，这些记录用于提取副语言特征，作为机器学习模型预测PD严重程度的输入。这里展示了结果，并根据记录的性质讨论了局限性。我们指出，我们的方法仅推进了生物标志物研究，尚未获批用于临床。具体而言，我们证明了应用于语音信号的传统机器学习模型可用于区分几乎没有症状的PD患者和健康对照。这项工作突出了语音用于PD早期检测的潜力，并表明语音可能作为PD的深度表型，通过提高PD管理的速度、准确性、可及性和成本来实现精准医学。
