Harnessing Speech-Derived Digital Biomarkers to Detect and Quantify Cognitive Decline Severity in Older Adults.
Harnessing Speech-Derived Digital Biomarkers to Detect and Quantify Cognitive Decline Severity in Older Adults.
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利用语音衍生的数字生物标记来检测和量化老年人的认知衰退严重程度。
DOI：
10.1159/000536250
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发表时间：
2024
期刊：
影响因子：
3.5
通讯作者：
Najafi,Bijan
中科院分区：
文献类型：
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作者：
Cay,Gozde;Pfeifer,ValeriaA;Lee,Myeounggon;Rouzi,MohammadDehghan;Nunes,AdonayS;El-Refaei,Nesreen;Momin,AnmolSalim;Atique,MdMoinUddin;Mehl,MatthiasR;Vaziri,Ashkan;Najafi,Bijan
目前的认知评估存在“地板/天花板效应”和“练习效应”，轻症患者心理测量效果差，评估效果重复。本研究探讨了使用数字语音分析作为确定认知障碍的替代工具。该研究特别侧重于识别与认知障碍及其严重程度相关的数字语音生物标志物。方法我们招募了认知健康状况不同的老年人。他们在朗读一段标准段落时，通过可穿戴式麦克风记录语音数据，并对数据进行处理，得出数字生物标记，如时间、音调和响度。科恩的效应大小强调了群体差异，并将其与蒙特利尔认知评估（MoCA）联系起来。采用随机森林模型逐步区分语音数据的认知状态，并基于高度相关特征预测MoCA分数。结果该研究包括59名参与者，其中36人表现出认知障碍，23人作为认知完整的对照组。在所有评估参数中，由动态时间扭曲（Dynamic Time Warping， DTW）决定的相似度与MoCA得分表现出最显著的正相关（rho= 0.529, p< 0.001），而时间参数，特别是额外单词的比例，与MoCA得分表现出最强的负相关（rho= - 0.441, p< 0.001）。通过四个语音参数的组合：总停顿时间、语音停顿比、DTW的相似性和DTW的可理解性，获得了最佳的判别性能。精密度和平衡准确度得分分别为88.1±1.2%和76.3±1.3%。我们的研究表明，阅读衍生的语音数据有助于区分认知障碍个体和认知完整、年龄匹配的老年人。具体来说，语音数据中基于时间和相似性的参数提供了认知障碍严重程度的有效衡量标准。这些结果表明，语音分析作为一种可行的早期检测和监测认知障碍的数字生物标志物，为痴呆症的护理提供了新的方法。
IntroductionCurrent cognitive assessments suffer from floor/ceiling and practice effects, poor psychometric performance in mild cases, and repeated assessment effects. This study explores the use of digital speech analysis as an alternative tool for determining cognitive impairment. The study specifically focuses on identifying the digital speech biomarkers associated with cognitive impairment and its severity.MethodsWe recruited older adults with varying cognitive health. Their speech data, recorded via a wearable microphone during the reading aloud of a standard passage, were processed to derive digital biomarkers such as timing, pitch, and loudness. Cohen’s d effect size highlighted group differences, and correlations were drawn to the Montreal Cognitive Assessment (MoCA). A stepwise approach using a Random Forest model was implemented to distinguish cognitive states using speech data and predict MoCA scores based on highly correlated features.ResultsThe study comprised 59 participants, with 36 demonstrating cognitive impairment and 23 serving as cognitively intact controls. Among all assessed parameters, similarity, as determined by Dynamic Time Warping (DTW), exhibited the most substantial positive correlation (rho= 0.529, p< 0.001), while timing parameters, specifically the ratio of extra words, revealed the strongest negative correlation (rho=− 0.441, p< 0.001) with MoCA scores. Optimal discriminative performance was achieved with a combination of four speech parameters: total pause time, speech-to-pause ratio, similarity via DTW, and intelligibility via DTW. Precision and balanced accuracy scores were found to be 88.1±1.2% and 76.3±1.3%, respectively.DiscussionOur research proposes that reading-derived speech data facilitates the differentiation between cognitively impaired individuals and cognitively intact, age-matched older adults. Specifically, parameters based on timing and similarity within speech data provide an effective gauge of cognitive impairment severity. These results suggest speech analysis as a viable digital biomarker for early detection and monitoring of cognitive impairment, offering novel approaches in dementia care.