云端融合的神经系统疾病多通道辅助诊断研究

Multimodal aided neurological disease diagnosis with synergy of cloud and client

对于神经系统疾病,其主要的生理特性可以反映在患者日常的人机交互行为中.对交互过程中产生的相关生理信息进行获取和分析,有助于神经系统疾病辅助诊断与早期预警.传统的神经系统疾病自动检测系统常常只分析单一交互通道中包含的信息,会造成其他通道中包含的重要信息的缺失.为此,本文提出了一个云端融合的神经系统疾病多通道辅助诊断系统.首先,提出一种基于多通道信息的疾病自动诊断方法,获取并分析了用户在多个交互通道中释放的生理信息,并将结果进行融合,提升自动诊断的精确性与鲁棒性;其次,提出了一个云端融合的框架,将从不同地区、不同时刻提取的用户生理信息在云端存储,降低了数据采集的地域限制与时间限制;再次,基于云计算强大的运算能力,系统亦可以实时地对用户在自然交互过程中产生的多通道生理信息进行分析,做出实时、精确的自动诊断;最后,通过语音与笔的交互混合诊断系统实例,验证基于云端融合的神经系统疾病多通道辅助诊断方法的有效性.

The dominating physiological characteristics of neurological diseases are reflected in patients＇ daily behaviors. The aided diagnosis and early warning of neurological diseases will benefit from obtaining and analyzing related physiological information generated during the interaction process. Traditional systems for detecting neurological conditions only analyze a single interaction modal, which may lose important features contained in other modalities. Based on the above, we propose a multi-modal aided neurological disease diagnosis system with synergy of cloud and client. First, we propose an automatic disease diagnosis method based on multi-modal information; users＇ physiological information collected from multiple modals is then analyzed and the results are integrated to improve accuracy and robustness. Second, a framework of cloud-client synergy is proposed that stores the user＇s physiological information from different regions and different times in the cloud, thus reducing the geographical restrictions and time constraints of data collection. Third, based on the powerful computing capacity, the system can make real-time and precise automatic diagnosis by analyzing multi-modal physiological information produced by users during natural interaction. Finally, the effectiveness of this multi-modal method for diagnosing aided neurological diseases based on cloud-client synergy is verified by using a hybrid diagnostic system of voice and pen.