本文为英国牛津大学（作者：P.J. Moor）的博士论文，共187页。

这项研究应用统计学模型来研究双相情感障碍患者的情绪。本文报告了三种对远程监测情绪数据的分析，每种分析都对应于作者的一篇期刊论文。

第一项分析显示，睡眠质量不稳定的患者比睡眠质量较差的患者更倾向于偶尔返回情绪评级。

第二项分析发现用每周的数据来预测抑郁症是不可行的。

第三项分析表明，抑郁时间序列不能与其线性拟合区分开来，非线性预测方法在预测情绪方面并不比线性方法更准确。

另一个贡献是开发了一种新的k近邻预测算法，该算法是根据情绪数据和其他时间序列进行评估的。对更频繁的采样数据和系统辨识提出了进一步的工作。最后，建议观察数据应与脑功能模型相结合，并在精神疾病的理论解释方面需要更多的工作。 

This study applies statistical models to mood in patients with bipolar disorder. Three analyses of telemonitored mood data are reported, each corresponding to a journal paper by the author. The first analysis reveals that patients whose sleep varies in quality tend to return mood ratings more sporadically than those with less variable sleep quality. The second analysis finds that forecasting depression with weekly data is not feasible using weekly mood ratings. A third analysis shows that depression time series cannot be distinguished from their linear surrogates, and that nonlinear forecasting methods are no more accurate than linear methods in forecasting mood. An additional contribution is the development of a new k-nearest neighbour forecasting algorithm which is evaluated on the mood data and other time series. Further work is proposed on more frequently sampled data and on system identification. Finally, it is suggested that observational data should be combined with models of brain function, and that more work is needed on theoretical explanations for mental illnesses.

1.       引言

2. 统计理论

3. 情绪相关因素

4. 情绪预测

5. 动态情绪

6. 最近邻预测

7. 一般结论

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