Billeci Lucia, Tonacci Alessandro, Tartarisco Gennaro, Narzisi Antonio, Di Palma Simone, Corda Daniele, Baldus Giovanni, Cruciani Federico, Anzalone Salvatore M, Calderoni Sara, Pioggia Giovanni, Muratori Filippo
Institute of Clinical Physiology, National Research Council of ItalyPisa, Italy; Department of Clinical and Experimental Medicine, University of PisaPisa, Italy.
Institute of Clinical Physiology, National Research Council of Italy Pisa, Italy.
Front Neurosci. 2016 Jun 21;10:276. doi: 10.3389/fnins.2016.00276. eCollection 2016.
Autism Spectrum Disorders (ASD) are associated with physiological abnormalities, which are likely to contribute to the core symptoms of the condition. Wearable technologies can provide data in a semi-naturalistic setting, overcoming the limitations given by the constrained situations in which physiological signals are usually acquired. In this study an integrated system based on wearable technologies for the acquisition and analysis of neurophysiological and autonomic parameters during treatment is proposed and an application on five children with ASD is presented. Signals were acquired during a therapeutic session based on an imitation protocol in ASD children. Data were analyzed with the aim of extracting quantitative EEG (QEEG) features from EEG signals as well as heart rate and heart rate variability (HRV) from ECG. The system allowed evidencing changes in neurophysiological and autonomic response from the state of disengagement to the state of engagement of the children, evidencing a cognitive involvement in the children in the tasks proposed. The high grade of acceptability of the monitoring platform is promising for further development and implementation of the tool. In particular if the results of this feasibility study would be confirmed in a larger sample of subjects, the system proposed could be adopted in more naturalistic paradigms that allow real world stimuli to be incorporated into EEG/psychophysiological studies for the monitoring of the effect of the treatment and for the implementation of more individualized therapeutic programs.
自闭症谱系障碍（ASD）与生理异常有关，这些生理异常可能导致该病症的核心症状。可穿戴技术能够在半自然环境中提供数据，克服了通常获取生理信号时受限环境所带来的局限性。在本研究中，提出了一种基于可穿戴技术的集成系统，用于在治疗过程中采集和分析神经生理和自主神经参数，并展示了该系统在五名自闭症谱系障碍儿童中的应用。信号是在基于自闭症谱系障碍儿童模仿协议的治疗过程中采集的。分析数据的目的是从脑电图（EEG）信号中提取定量脑电图（QEEG）特征，以及从心电图（ECG）中提取心率和心率变异性（HRV）。该系统能够证明儿童从脱离状态到参与状态时神经生理和自主神经反应的变化，表明儿童在提出的任务中有认知参与。监测平台的高接受度为该工具的进一步开发和实施带来了希望。特别是如果这项可行性研究的结果能在更大样本的受试者中得到证实，那么所提出的系统可以应用于更自然的范式中，使现实世界的刺激能够纳入脑电图/心理生理学研究，以监测治疗效果并实施更个性化治疗方案。
