4th European Conference of the International Federation for Medical and Biological Engineering
4th European Conference of the International Federation for Medical and Biological Engineering
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国际医学与生物工程联合会第四届欧洲会议
DOI：
10.1007/978-3-540-89208-3_207
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Amor J
中科院分区：
文献类型：
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作者：
Amor J
双相情感障碍（BD）是一种以反复发作的躁狂和抑郁为特征的精神障碍。这种疾病可能是非常破坏性的，复发往往导致住院治疗。通过适当的培训，患者能够控制他们的症状，减少对日常生活的干扰。作为这种自我控制过程的辅助手段，正在开发个性化环境监测（PAM）项目。PAM项目旨在让BD患者监测他们的病情，并获得他们精神状态的指标。这将通过使用多个离散传感器来实现，这些传感器根据每个患者的需求进行个性化设置。传感器将检测躁狂和抑郁的相关性，这将被用来得出患者的心理健康状态的趋势。BD的主要症状集中在患者的活动水平和昼夜节律上。躁狂发作的典型特征是能量和活动增加，通常睡眠需求减少。然而，抑郁发作往往表现为活动减少。这是我们的目标，通过测量患者的活动水平和昼夜节律，我们可以提供信息，患者可以使用，以帮助控制他们的symptoms.here我们提出了一些初步的工作，旨在区分不同的活动和活动水平在正常对照组，基于一个小的，身体安装的三轴加速度计。一些参与者被要求在佩戴加速计的同时完成一些基本活动。对数据进行预处理，以提取一些显著特征，这些特征用于训练Neuroscale算法。Neuroscale产生一个生成映射，在低维空间中可视化高维数据，加上聚类算法，可用于对未知数据点进行分类。预计这种方法与来自其他传感器类型的数据相结合将形成应用于BD的PAM方法的主干。
Bipolar disorder (BD) is a mental disorder characterized by recurrent episodes of mania and depression. The disorder can be very disruptive and relapses often result in hospitalization. With adequate training, sufferers are able to control their symptoms and reduce the disruption to their daily lives. As an aid to this self-control process the Personalized Ambient Monitoring (PAM) project is being developed.The PAM project aims to allow patients with BD to monitor their condition and obtain indications of their mental state. This will be achieved through the use of multiple discreet sensors, personalized for each patient’s needs. The sensors will detect the correlates of mania and depression, which will be used to derive trends in the mental health state of the patient.The major symptoms of BD center on the patient’s activity level and circadian rhythm. Manic episodes are typified by increased energy and activity, often with a decreased need for sleep. Depressive episodes however often present with diminished activity. It is our aim that by measuring the patient’s activity levels and circadian rhythm we can provide information that the patient can use to help control their symptoms.Here we present some preliminary work aimed at distinguishing different activities and activity levels in normal controls, based on a small, body-mounted triaxial accelerometer. A number of participants were asked to complete some basic activities whilst wearing the accelerometer. The data was preprocessed to extract a number of salient features, which were used to train a Neuroscale algorithm. Neuroscale produces a generative mapping that visualizes high-dimensional data in a lower-dimensional space, which, with the addition of a clustering algorithm, can be used to classify unknown data points. It is expected that this approach, combined with data from other sensor types will form the backbone of the PAM approach applied to BD.