Garrard Peter, Nemes Vanda, Nikolic Dragana, Barney Anna
Molecular and Clinical Sciences Research Institute, St George's, University of London, London SW17 0RE. United Kingdom.
Institute of Physiology, University of Pécs Medical School, Pécs 7624. Hungary.
Curr Alzheimer Res. 2017;14(9):951-959. doi: 10.2174/1567205014666170309121025.
Perseveration - repetition of words, phrases or questions in speech - is commonly described in Alzheimer's disease (AD). Measuring perseveration is difficult, but may index cognitive performance, aiding diagnosis and disease monitoring. Continuous recording of speech would produce a large quantity of data requiring painstaking manual analysis, and risk violating patients' and others' privacy. A secure record and an automated approach to analysis are required.
To record bone-conducted acoustic energy fluctuations from a subject's vocal apparatus using an accelerometer, to describe the recording and analysis stages in detail, and demonstrate that the approach is feasible in AD.
Speech-related vibration was captured by an accelerometer, affixed above the temporomandibular joint. Healthy subjects read a script with embedded repetitions. Features were extracted from recorded signals and combined using Principal Component Analysis to obtain a one-dimensional representation of the feature vector. Motif discovery techniques were used to detect repeated segments. The equipment was tested in AD patients to determine device acceptability and recording quality.
Comparison with the known location of embedded motifs suggests that, with appropriate parameter tuning, the motif discovery method can detect repetitions. The device was acceptable to patients and produced adequate signal quality in their home environments.
We established that continuously recording bone-conducted speech and detecting perseverative patterns were both possible. In future studies we plan to associate the frequency of verbal repetitions with stage, progression and type of dementia. It is possible that the method could contribute to the assessment of disease-modifying treatments.
持续性言语——在言语中重复词语、短语或问题——在阿尔茨海默病（AD）中很常见。测量持续性言语很困难，但它可能是认知表现的指标，有助于诊断和疾病监测。连续记录言语会产生大量数据，需要进行费力的人工分析，并且有侵犯患者及他人隐私的风险。因此需要一种安全的记录方式和自动化分析方法。
使用加速度计记录受试者发声器官的骨传导声能波动，详细描述记录和分析阶段，并证明该方法在AD中是可行的。
通过贴在颞下颌关节上方的加速度计捕捉与言语相关的振动。健康受试者阅读一段包含重复内容的脚本。从记录的信号中提取特征，并使用主成分分析进行组合，以获得特征向量的一维表示。使用基序发现技术检测重复片段。该设备在AD患者中进行测试，以确定设备的可接受性和记录质量。
与已知的嵌入基序位置进行比较表明，通过适当调整参数，基序发现方法可以检测到重复内容。该设备为患者所接受，并且在他们的家庭环境中产生了足够的信号质量。
我们证实了连续记录骨传导言语和检测持续性模式都是可行的。在未来的研究中，我们计划将言语重复的频率与痴呆症的阶段、进展和类型联系起来。该方法有可能有助于评估疾病修饰治疗。
