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2017 International Conference on Orange Technologies (ICOT)最新文献

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An accurate sleep staging system with novel feature generation and auto-mapping 具有新颖特征生成和自动映射的精确睡眠分期系统
Pub Date : 2017-12-01 DOI: 10.1109/ICOT.2017.8336079
Zhuo Zhang, Cuntai Guan
Traditional sleep monitoring conducted in professional sleep labs and scored by sleep specialist is costly and labor intensive. Recent development of light-weight headband EEG provides possible solution for home-based sleep monitoring. This study proposed a machine learning approach for automatic sleep stage detection. A set of effective and efficient features are extracted from EEG data. The utilization of a collection of well annotated sleep data ensures the quality of learning model. A feature mapping algorithm is proposed to map the feature spaces generated from EEG data acquired through different electrodes. We collected headband EEG data for 1 hour naps in experiments conducted in our sleep lab. Preliminary result shows that sleep stages detected by proposed method are highly agreeable with the sleepiness score we obtained.
传统的睡眠监测是在专业的睡眠实验室进行的,由睡眠专家评分,成本高昂,而且需要大量的劳动。最近发展的轻型头带脑电图为家庭睡眠监测提供了可能的解决方案。本研究提出了一种自动检测睡眠阶段的机器学习方法。从脑电数据中提取出一组有效、高效的特征。使用一组注释良好的睡眠数据保证了学习模型的质量。提出了一种特征映射算法,对不同电极采集的脑电数据生成的特征空间进行映射。在我们的睡眠实验室中,我们收集了小睡1小时的头带脑电图数据。初步结果表明,该方法检测到的睡眠阶段与我们得到的困倦评分高度吻合。
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引用次数: 4
Empirical evaluation of consumer EEG and actigraphy devices for home-based sleep assessment 家用脑电图和活动描记仪用于家庭睡眠评估的实证评价
Pub Date : 2017-12-01 DOI: 10.1109/ICOT.2017.8336086
Suzanne-Kae Rocknathan, Wa Thone, Yeoh Tze Xuan, Andrew Yapp Wei Rong, Anisha Roy, Deepak Alagusubramanian, Aung Aung Phyo Wai
Sleep is the most essential part of everyone's daily life. With prevalence of wearable and mobile technologies, several consumer solutions have emerged, targeting home-based sleep assessment. This study aims to investigate the performance of EEG and actigraphy devices in assessing sleep quality. Various ambient factors affecting sleep quality and usability of such wearable devices were also evaluated. The study protocol consists of an online opinion survey, subjective sleep assessment and multi-night data recording with or without white noise. From survey results, 94.2% of respondents had no prior experience in using sleep-tracking devices but 37.3% of total respondents encounter sleep problems. We collected multi-night sleep data with 18 high school student subjects. Significant correlation was found between sleep parameters and factors like naps, caffeine and stress. Playing white noise during sleep showed improvement in the occurrence and duration of deep sleep, possibly a positive effect of sound-based sleep assistance. Our analysis showed the sleep quality parameters derived from EEG are more complete and accurate than actigraphy. However, actigraphy surpassed EEG headband in usability aspects such as comfort, ease of use. Despite that, outcomes from our product design survey showed no significance difference in preference between eye-mask and wristband. We hope that our findings contribute to further development of home-based sleep solution with better usability, reliable sleep assessment, for early identification and treatment of sleep related problems.
睡眠是每个人日常生活中最重要的一部分。随着可穿戴和移动技术的普及,出现了几种针对家庭睡眠评估的消费者解决方案。本研究旨在探讨脑电图和活动描记仪在评估睡眠质量方面的性能。各种环境因素影响睡眠质量和这种可穿戴设备的可用性也进行了评估。研究方案包括在线意见调查、主观睡眠评估和有或没有白噪音的多夜数据记录。从调查结果来看,94.2%的受访者没有使用睡眠追踪设备的经验,但37.3%的受访者遇到睡眠问题。我们收集了18名高中生的多夜睡眠数据。睡眠参数与小睡、咖啡因和压力等因素之间存在显著相关性。在睡眠中播放白噪音可以改善深度睡眠的发生和持续时间,这可能是基于声音的睡眠辅助的积极作用。我们的分析表明,脑电图获得的睡眠质量参数比活动描记仪更完整和准确。然而,在易用性方面,如舒适性、易用性等,活动描记技术都超过了脑电图头带。尽管如此,我们的产品设计调查结果显示,眼罩和腕带的偏好没有显著差异。我们希望我们的发现有助于进一步发展以家庭为基础的睡眠解决方案,具有更好的可用性,可靠的睡眠评估,早期识别和治疗睡眠相关问题。
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引用次数: 1
Valence-arousal analysis for mental-health document retrieval 心理健康文献检索的效价唤醒分析
Pub Date : 2017-12-01 DOI: 10.1109/ICOT.2017.8336089
P. Hu, ShaoZhen Ye, Liang-Chih Yu, K. R. Lai
The increasing incidence of depression has attracted increased attention to mental-health document retrieval techniques which aims to help individuals efficiently locate documents and resources relevant to their depressive problems. However, current retrieval systems generally have low accuracy. We propose combining a Valence-Arousal-based (VA-based) retrieval model and other word-based retrieval models to improve the precision of retrieval results. The VA-based retrieval model considers affective words extracted from queries, which help provide a better understanding of user queries. Experimental results demonstrate that the combined methods outperform the word-based retrieval models which adopt word-level information alone, such as vector space model and BM25 model.
抑郁症发病率的增加引起了人们对心理健康文献检索技术的越来越多的关注,这些技术旨在帮助个人有效地找到与他们的抑郁问题相关的文献和资源。然而,目前的检索系统通常精度较低。我们提出将基于Valence-Arousal-based的检索模型与其他基于词的检索模型相结合,以提高检索结果的精度。基于va的检索模型考虑从查询中提取的情感词,这有助于更好地理解用户查询。实验结果表明,该组合方法优于单独采用词级信息的基于词的检索模型,如向量空间模型和BM25模型。
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引用次数: 0
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2017 International Conference on Orange Technologies (ICOT)
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