EAGER: Feasibility of Using Speech as Biomarker for Concussions

EAGER：使用言语作为脑震荡生物标志物的可行性

• 批准号: 1450349
• 负责人: Christian Poellabauer
• 金额: $ 30万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1450349&HistoricalAwards=false
• 关键词: EAGER; Feasibility; Using; Speech; Biomarker

This project studies the feasibility of using speech as a novel biomarker for concussions, based on insights obtained from prior research that have shown links between impaired brain functioning and speech. Mild traumatic brain injuries (mTBI) such as concussions are arguably one of the most pressing concerns in sports today, with an estimated 2-4 million cases every year in youth sports alone. The potential short- and long-term impacts on the health and well-being of individuals with brain injuries are extensive. Untreated concussions can lead to diseases such as chronic traumatic encephalopathy, an elevated incidence of Alzheimer's disease, and dementia developing at a higher rate and a younger age. It is therefore imperative to accurately detect and appropriately treat mTBI, especially among adolescents, whose brains are still developing and more prone to long-term damage. This research advances the understanding of the relationship between brain injuries and acoustic features in speech and lays the foundation for novel and accurate concussion screening tools. Initial results based on voice collections from more than 300 boxers (25 of which were concussed) have been promising and indicate that speech may have the potential to serve as a new, easy-to-capture biomarker for mTBI. Using mobile technologies such as smartphones and tablets, brief speech recordings from youth athletes are being captured. With the help of speech recognition techniques, each word is isolated and acoustic features are extracted for each phoneme in each word. Features that are being analyzed include temporal characteristics of speech (e.g., speech duration per word or phoneme or articulation rate) and frequency spectrum characteristics (e.g., pitch, formant frequencies, and mel-frequency cepstrum coefficients). Using state-of-the-art statistical analysis and machine learning techniques, these features are then analyzed for their potential as marker for mild forms of brain injuries.

该项目基于从先前的研究中获得的见解，研究了将语音用作脑震荡的新型生物标志物的可行性，这些研究表明大脑功能受损与言语之间的联系。轻度创伤性脑损伤（MTBI），例如脑震荡，可以说是当今运动中最紧迫的担忧之一，仅在青年运动中，每年估计每年有2-4万例。潜在的短期和长期影响对脑损伤患者的健康和福祉的潜在影响是广泛的。未经治疗的脑震荡会导致疾病，例如慢性创伤性脑病，阿尔茨海默氏病的发病率升高，并且痴呆症以较高的速度和年龄较小。因此，必须准确检测并适当地治疗MTBI，尤其是在青少年中，他们的大脑仍在发育，并且更容易受到长期损害。这项研究促进了对语音中脑损伤与声学特征之间关系的理解，并为新颖而准确的脑震荡筛查工具奠定了基础。基于300多个拳击手的语音收集（其中25个脑震荡）的初步结果很有希望，并表明语音可能有可能用作MTBI的新型，易于捕捉的生物标志物。使用智能手机和平板电脑等移动技术，正在捕获青年运动员的简短录音。借助语音识别技术，每个单词都是隔离的，每个单词中的每个音素都会提取声学特征。正在分析的特征包括语音的时间特征（例如，每个单词或音素或发音率的语音持续时间）和频谱特征（例如，音高，强义频率和MEL频率Cepstrum系数）。然后，使用最先进的统计分析和机器学习技术，对这些特征进行分析，以作为轻度脑损伤形式的标志物的潜力。