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mCardia: A Context-Aware ECG Collection System for Ambulatory Arrhythmia Screening mccardia:动态心律失常筛查的上下文感知ECG收集系统
Pub Date : 2022-03-03 DOI: 10.1145/3494581
Devender Kumar, Raju Maharjan, Alban Maxhuni, Helena Domínguez, A. Frølich, J. Bardram
This article presents the design, technical implementation, and feasibility evaluation of mCardia—a context-aware, mobile electrocardiogram (ECG) collection system for longitudinal arrhythmia screening under free-living conditions. Along with ECG, mCardia also records active and passive contextual data, including patient-reported symptoms and physical activity. This contextual data can provide a more accurate understanding of what happens before, during, and after an arrhythmia event, thereby providing additional information in the diagnosis of arrhythmia. By using a plugin-based architecture for ECG and contextual sensing, mCardia is device-agnostic and can integrate with various wireless ECG devices and supports cross-platform deployment. We deployed the mCardia system in a feasibility study involving 24 patients who used the system over a two-week period. During the study, we observed high patient acceptance and compliance with a satisfactory yield of collected ECG and contextual data. The results demonstrate the high usability and feasibility of mCardia for longitudinal ambulatory monitoring under free-living conditions. The article also reports from two clinical cases, which demonstrate how a cardiologist can utilize the collected contextual data to improve the accuracy of arrhythmia analysis. Finally, the article discusses the lessons learned and the challenges found in the mCardia design and the feasibility study.
本文介绍了mcardia的设计、技术实现和可行性评估——一种在自由生活条件下用于纵向心律失常筛查的情境感知移动心电图(ECG)收集系统。除了ECG, mCardia还记录主动和被动的背景数据,包括患者报告的症状和身体活动。这些上下文数据可以更准确地了解心律失常事件发生之前、期间和之后发生的情况,从而为心律失常的诊断提供额外的信息。通过使用基于插件的ECG和上下文感知架构,mccardia与设备无关,可以与各种无线ECG设备集成,并支持跨平台部署。我们在一项可行性研究中部署了mCardia系统,涉及24名使用该系统超过两周的患者。在研究期间,我们观察到患者对所收集的心电图和相关数据的接受度和依从性很高。结果表明,mCardia在自由生活条件下进行纵向动态监测具有较高的可用性和可行性。本文还报道了两个临床病例,这表明心脏病专家如何利用收集的上下文数据来提高心律失常分析的准确性。最后,文章讨论了mccardia设计和可行性研究的经验教训和面临的挑战。
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引用次数: 8
MARS: Assisting Human with Information Processing Tasks Using Machine Learning MARS:利用机器学习协助人类完成信息处理任务
Pub Date : 2022-03-03 DOI: 10.1145/3494582
Cong Shen, Z. Qian, Alihan Hüyük, M. Schaar
This article studies the problem of automated information processing from large volumes of unstructured, heterogeneous, and sometimes untrustworthy data sources. The main contribution is a novel framework called Machine Assisted Record Selection (MARS). Instead of today’s standard practice of relying on human experts to manually decide the order of records for processing, MARS learns the optimal record selection via an online learning algorithm. It further integrates algorithm-based record selection and processing with human-based error resolution to achieve a balanced task allocation between machine and human. Both fixed and adaptive MARS algorithms are proposed, leveraging different statistical knowledge about the existence, quality, and cost associated with the records. Experiments using semi-synthetic data that are generated from real-world patients record processing in the UK national cancer registry are carried out, which demonstrate significant (3 to 4 fold) performance gain over the fixed-order processing. MARS represents one of the few examples demonstrating that machine learning can assist humans with complex jobs by automating complex triaging tasks.
本文研究了从大量非结构化的、异构的、有时是不可信的数据源中自动处理信息的问题。主要的贡献是一个叫做机器辅助记录选择(MARS)的新框架。与今天依靠人类专家手动决定记录处理顺序的标准做法不同,MARS通过在线学习算法学习最佳记录选择。它进一步将基于算法的记录选择和处理与基于人为的错误解决相结合,实现机器和人之间的平衡任务分配。提出了固定的和自适应的MARS算法,利用与记录相关的存在、质量和成本的不同统计知识。实验使用半合成数据生成的真实世界的病人记录处理在英国国家癌症登记处进行,这表明显著(3至4倍)的性能增益比固定顺序的处理。MARS是少数几个证明机器学习可以通过自动化复杂的分类任务来帮助人类完成复杂工作的例子之一。
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引用次数: 0
A Survey on Healthy Food Decision Influences Through Technological Innovations 技术创新对健康食品决策影响的调查
Pub Date : 2022-03-03 DOI: 10.1145/3494580
Jermaine Marshall, Priscilla Jiménez-Pazmino, Ronald Metoyer, N. Chawla
It is well known that unhealthy food consumption plays a significant role in dietary and lifestyle-related diseases. Therefore, it is important for researchers to examine methods that may encourage the consumer to consider healthier dietary and lifestyle habits as diseases such as obesity, heart disease, and high blood pressure remain a worldwide issue. One promising approach to influencing healthy dietary and lifestyle habits is food recommendation models that recommend food to users based on various factors such as health effects, nutrition, preferences, and daily habits. Unfortunately, much of this work has focused on individual factors such as taste preferences and often neglects to understand other factors that influence our choices. Additionally, the evaluation of technological approaches often lacks user studies in the context of intended use. In this systematic review of food choice technology, we focus on the factors that may influence food choices and how technology can play a role in supporting those choices. We also describe existing work, approaches, trends, and issues in current food choice technology and give advice for future work areas in this space.
众所周知,不健康的食物消费在饮食和生活方式相关疾病中起着重要作用。因此,对于研究人员来说,研究可以鼓励消费者考虑更健康的饮食和生活习惯的方法是很重要的,因为肥胖、心脏病和高血压等疾病仍然是一个世界性的问题。影响健康饮食和生活习惯的一个有希望的方法是食物推荐模型,它根据各种因素(如健康影响、营养、偏好和日常习惯)向用户推荐食物。不幸的是,这项工作的大部分都集中在个人因素上,比如口味偏好,而往往忽视了影响我们选择的其他因素。此外,对技术方法的评价往往缺乏对预期用途的用户研究。在对食物选择技术的系统回顾中,我们关注可能影响食物选择的因素以及技术如何在支持这些选择中发挥作用。我们还描述了当前食品选择技术的现有工作、方法、趋势和问题,并对该领域未来的工作领域提出了建议。
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引用次数: 3
Embedding Temporal Convolutional Networks for Energy-efficient PPG-based Heart Rate Monitoring 基于时间卷积网络的高效ppg心率监测
Pub Date : 2022-03-01 DOI: 10.1145/3487910
A. Burrello, D. J. Pagliari, Pierangelo Maria Rapa, Matilde Semilia, Matteo Risso, T. Polonelli, M. Poncino, L. Benini, S. Benatti
Photoplethysmography (PPG) sensors allow for non-invasive and comfortable heart rate (HR) monitoring, suitable for compact wrist-worn devices. Unfortunately, motion artifacts (MAs) severely impact the monitoring accuracy, causing high variability in the skin-to-sensor interface. Several data fusion techniques have been introduced to cope with this problem, based on combining PPG signals with inertial sensor data. Until now, both commercial and reasearch solutions are computationally efficient but not very robust, or strongly dependent on hand-tuned parameters, which leads to poor generalization performance. In this work, we tackle these limitations by proposing a computationally lightweight yet robust deep learning-based approach for PPG-based HR estimation. Specifically, we derive a diverse set of Temporal Convolutional Networks for HR estimation, leveraging Neural Architecture Search. Moreover, we also introduce ActPPG, an adaptive algorithm that selects among multiple HR estimators depending on the amount of MAs, to improve energy efficiency. We validate our approaches on two benchmark datasets, achieving as low as 3.84 beats per minute of Mean Absolute Error on PPG-Dalia, which outperforms the previous state of the art. Moreover, we deploy our models on a low-power commercial microcontroller (STM32L4), obtaining a rich set of Pareto optimal solutions in the complexity vs. accuracy space.
光电容积脉搏波(PPG)传感器允许无创和舒适的心率(HR)监测,适用于紧凑型腕戴设备。不幸的是,运动伪影(MAs)严重影响监测精度,导致皮肤到传感器界面的高度可变性。为了解决这一问题,已经引入了几种数据融合技术,将PPG信号与惯性传感器数据相结合。到目前为止,商业和研究解决方案都是计算效率高,但不是很健壮,或者强烈依赖于手动调整的参数,这导致了较差的泛化性能。在这项工作中,我们通过提出一种基于ppg的人力资源估计的计算轻量级但鲁棒的基于深度学习的方法来解决这些限制。具体来说,我们推导了一组不同的时间卷积网络用于人力资源估计,利用神经架构搜索。此外,我们还引入了一种自适应算法ActPPG,该算法根据MAs的数量在多个HR估计器中进行选择,以提高能源效率。我们在两个基准数据集上验证了我们的方法,在PPG-Dalia上实现了每分钟3.84次的平均绝对误差,优于之前的技术水平。此外,我们将我们的模型部署在低功耗商用微控制器(STM32L4)上,在复杂性与精度空间中获得了丰富的Pareto最优解集。
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引用次数: 9
Introduction to the Special Issue on Computational Methods for Biomedical NLP 生物医学NLP计算方法特刊导论
Pub Date : 2022-01-12 DOI: 10.1145/3492302
M. Devarakonda, E. Voorhees
It is now well established that biomedical text requires methods targeted for the domain. Developments in deep learning and a series of successful shared challenges have contributed to a steady progress in techniques for natural language processing of biomedical text. Contributing to this on-going progress and particularly focusing on computational methods, this special issue was created to encourage research in novel approaches for analyzing biomedical text. The six papers selected for the issue offer a diversity of novel methods that leverage biomedical text for research and clinical uses. A well-established practice in pretraining deep learning models for biomedical applications has been to adopt a most promising model that was already pretrained on general domain natural language corpus and then “add” additional pre-training with biomedical corpora. In “Domain-specific language model pretraining for biomedical natural language processing”, Gu et al. successfully challenge this approach. The authors conducted an experiment where multiple standard benchmarks were used to compare a model that was pre-trained entirely and only on biomedical corpus with models that were pretrained using the “add” on approach. Results showed an impressive improvement in favor of pretraining only with biomedical corpus. The study provides an excellent data-point in support of clarity in model training rather than accumulation. Tariq et al. also find using domain-aware tokenization and embeddings to be more effective in their paper “Bridging the Gap Between Structured and Free-form Radiology Reporting: A Case-study on Coronary CT Angiography”. They compare a variety of models constructed to predict the severity of cardiovascular disease from the language used within free-text radiology reports. Models that used medical-domain-aware tokenization and word embeddings of the reports were consistently more effective than raw word-based. The better models are able to accurately predict disease severity under real-world conditions of diverse terminology from different radiologists and unbalanced class size. Two papers address the problem of maintaining the privacy of clinical documents, though from widely different perspectives. De-identification is the most used approach to eliminate PHI (Protected Health Information) in clinical documents before making the data available to NLP researchers. In “A Context-enhanced De-identification System”, Kahyun et al. describe an improved de-identification technique for clinical records. Their context-enhanced de-identification system called CEDI uses attention mechanisms in a long short-term memory (LSTM) network to capture the appropriate context. This context allows the system to detect dependencies that cross sentence boundaries, an important feature since clinical reports often contain such dependencies. Nonetheless, accurate and broad-coverage de-identification of unstructured data remains challenging, and lack of trust in the pro
现在已经确定,生物医学文本需要针对该领域的方法。深度学习的发展和一系列成功的共同挑战促进了生物医学文本自然语言处理技术的稳步发展。为了促进这一持续的进展,特别是关注计算方法,本期特刊的创建是为了鼓励对分析生物医学文本的新方法的研究。为该问题选择的六篇论文提供了多种利用生物医学文本进行研究和临床应用的新方法。在生物医学应用深度学习模型的预训练中,一个公认的做法是采用一个最有前途的模型,该模型已经在一般领域的自然语言语料库上进行了预训练,然后在生物医学语料库上“添加”额外的预训练。在“针对生物医学自然语言处理的领域特定语言模型预训练”中,Gu等人成功地挑战了这种方法。作者进行了一个实验,使用多个标准基准来比较完全预训练的模型,只在生物医学语料库上与使用“add”on方法预训练的模型。结果显示,仅使用生物医学语料库进行预训练的效果显著改善。该研究提供了一个很好的数据点,支持清晰的模型训练,而不是积累。Tariq等人在他们的论文《弥合结构化和自由形式放射学报告之间的差距:冠状动脉CT血管造影案例研究》中也发现,使用域感知标记化和嵌入更有效。他们比较了根据自由文本放射学报告中使用的语言来预测心血管疾病严重程度的各种模型。使用医学领域感知的标记化和报告的单词嵌入的模型始终比原始的基于单词的模型更有效。更好的模型能够在不同放射科医生的不同术语和不平衡的班级规模的现实条件下准确预测疾病的严重程度。两篇论文解决了维护临床文件隐私的问题,尽管从广泛不同的角度。在向NLP研究人员提供数据之前,去识别是消除临床文件中PHI(受保护的健康信息)的最常用方法。在“上下文增强的去识别系统”中,Kahyun等人描述了一种改进的临床记录去识别技术。他们的情境增强去识别系统称为CEDI,使用长短期记忆(LSTM)网络中的注意机制来捕捉适当的情境。这个上下文允许系统检测跨句子边界的依赖关系,这是一个重要的特性,因为临床报告经常包含这种依赖关系。尽管如此,非结构化数据的准确和广泛的去标识化仍然具有挑战性,并且对(去标识化)过程缺乏信任可能是数据发布的严重限制因素。Aziz等人在“使用生成式神经网络生成差异化私有医学文本”一文中采用了不同的方法来处理临床文件的隐私。他们提出以高准确度合成临床文件作为一种实用的替代方法。在他们的方法中使用基于自我关注的神经网络和差分隐私(即控制相对于原始文档的隐私级别的能力),
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引用次数: 0
Emotion Recognition Robust to Indoor Environmental Distortions and Non-targeted Emotions Using Out-of-distribution Detection 基于分布外检测的对室内环境扭曲和非目标情绪的鲁棒情绪识别
Pub Date : 2021-12-20 DOI: 10.1145/3492300
Ye Gao, Asif Salekin, Kristin D. Gordon, Karen Rose, Hongning Wang, J. Stankovic
The rapid development of machine learning on acoustic signal processing has resulted in many solutions for detecting emotions from speech. Early works were developed for clean and acted speech and for a fixed set of emotions. Importantly, the datasets and solutions assumed that a person only exhibited one of these emotions. More recent work has continually been adding realism to emotion detection by considering issues such as reverberation, de-amplification, and background noise, but often considering one dataset at a time, and also assuming all emotions are accounted for in the model. We significantly improve realistic considerations for emotion detection by (i) more comprehensively assessing different situations by combining the five common publicly available datasets as one and enhancing the new dataset with data augmentation that considers reverberation and de-amplification, (ii) incorporating 11 typical home noises into the acoustics, and (iii) considering that in real situations a person may be exhibiting many emotions that are not currently of interest and they should not have to fit into a pre-fixed category nor be improperly labeled. Our novel solution combines CNN with out-of-data distribution detection. Our solution increases the situations where emotions can be effectively detected and outperforms a state-of-the-art baseline.
机器学习在声学信号处理方面的快速发展为从语音中检测情绪提供了许多解决方案。早期的作品是为干净和表演的语言和一套固定的情感而开发的。重要的是,数据集和解决方案假设一个人只表现出其中一种情绪。最近的工作通过考虑混响、去放大和背景噪声等问题,不断为情绪检测添加现实性,但通常一次只考虑一个数据集,并且假设模型中考虑了所有情绪。我们通过(i)将五个常见的公开数据集合并为一个,并通过考虑混响和去放大的数据增强新数据集,更全面地评估不同的情况,从而显著改善了情感检测的现实考虑因素;(ii)将11种典型的家庭噪音纳入声学;(三)考虑到在现实情况下,一个人可能会表现出许多目前不感兴趣的情绪,他们不应该被归入预先确定的类别,也不应该被不恰当地贴上标签。我们的新解决方案结合了CNN和数据外分布检测。我们的解决方案增加了可以有效检测情绪的情况,并且优于最先进的基线。
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引用次数: 0
A Minimalist Method Toward Severity Assessment and Progression Monitoring of Obstructive Sleep Apnea on the Edge 边缘阻塞性睡眠呼吸暂停严重程度评估和进展监测的极简方法
Pub Date : 2021-12-20 DOI: 10.1145/3479432
Md Juber Rahman, B. Morshed
Artificial Intelligence-enabled applications on edge devices have the potential to revolutionize disease detection and monitoring in future smart health (sHealth) systems. In this study, we investigated a minimalist approach for the severity classification, severity estimation, and progression monitoring of obstructive sleep apnea (OSA) in a home environment using wearables. We used the recursive feature elimination technique to select the best feature set of 70 features from a total of 200 features extracted from polysomnogram. We used a multi-layer perceptron model to investigate the performance of OSA severity classification with all the ranked features to a subset of features available from either Electroencephalography or Heart Rate Variability (HRV) and time duration of SpO2 level. The results indicate that using only computationally inexpensive features from HRV and SpO2, an area under the curve of 0.91 and an accuracy of 83.97% can be achieved for the severity classification of OSA. For estimation of the apnea-hypopnea index, the accuracy of RMSE = 4.6 and R-squared value = 0.71 have been achieved in the test set using only ranked HRV and SpO2 features. The Wilcoxon-signed-rank test indicates a significant change (p < 0.05) in the selected feature values for a progression in the disease over 2.5 years. The method has the potential for integration with edge computing for deployment on everyday wearables. This may facilitate the preliminary severity estimation, monitoring, and management of OSA patients and reduce associated healthcare costs as well as the prevalence of untreated OSA.
边缘设备上支持人工智能的应用程序有可能在未来的智能健康系统中彻底改变疾病检测和监测。在这项研究中,我们研究了一种使用可穿戴设备在家庭环境中进行阻塞性睡眠呼吸暂停(OSA)严重程度分类、严重程度估计和进展监测的极简方法。采用递归特征消去技术,从多导睡眠图提取的200个特征中选出70个最优特征集。我们使用多层感知器模型来研究OSA严重程度分类的性能,将所有排序的特征与脑电图或心率变异性(HRV)和SpO2水平持续时间的特征子集相结合。结果表明,仅使用HRV和SpO2的计算成本较低的特征,曲线下面积为0.91,OSA的严重程度分类准确率为83.97%。对于呼吸暂停-低通气指数的估计,仅使用排名HRV和SpO2特征,测试集中RMSE = 4.6, r²值= 0.71的准确性已经实现。Wilcoxon-signed-rank检验表明,在2.5年以上的疾病进展中,所选择的特征值有显著变化(p < 0.05)。该方法有可能与边缘计算集成,部署在日常可穿戴设备上。这可能有助于对OSA患者进行初步的严重程度评估、监测和管理,并降低相关的医疗费用以及未经治疗的OSA患病率。
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引用次数: 0
Computer-Assisted Cohort Identification in Practice 计算机辅助队列识别在实践中
Pub Date : 2021-12-20 DOI: 10.1145/3483411
Besat Kassaie, E. Irving, Frank Wm. Tompa
The standard approach to expert-in-the-loop machine learning is active learning, where, repeatedly, an expert is asked to annotate one or more records and the machine finds a classifier that respects all annotations made until that point. We propose an alternative approach, IQRef, in which the expert iteratively designs a classifier and the machine helps him or her to determine how well it is performing and, importantly, when to stop, by reporting statistics on a fixed, hold-out sample of annotated records. We justify our approach based on prior work giving a theoretical model of how to re-use hold-out data. We compare the two approaches in the context of identifying a cohort of EHRs and examine their strengths and weaknesses through a case study arising from an optometric research problem. We conclude that both approaches are complementary, and we recommend that they both be employed in conjunction to address the problem of cohort identification in health research.
专家在循环机器学习的标准方法是主动学习,其中,反复要求专家注释一条或多条记录,机器找到一个分类器,该分类器尊重在此之前所做的所有注释。我们提出了另一种方法,IQRef,在这种方法中,专家迭代地设计一个分类器,机器通过报告固定的、保留的注释记录样本的统计数据,帮助他或她确定它的表现如何,更重要的是,何时停止。我们基于先前的工作证明了我们的方法,给出了如何重用保留数据的理论模型。我们比较了这两种方法的背景下,确定一个队列的电子病历,并检查其优势和劣势,通过一个案例研究产生的验光研究问题。我们的结论是,这两种方法是互补的,我们建议将它们结合起来使用,以解决健康研究中的队列识别问题。
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引用次数: 0
Automatic Parotid Gland Segmentation in MVCT Using Deep Convolutional Neural Networks 基于深度卷积神经网络的MVCT腮腺自动分割
Pub Date : 2021-12-20 DOI: 10.1145/3485278
Junqian Zhang, Ying-Zhi Sun, Hongen Liao, Jian Zhu, Yuan Zhang
Radiation-induced xerostomia, as a major problem in radiation treatment of the head and neck cancer, is mainly due to the overdose irradiation injury to the parotid glands. Helical Tomotherapy-based megavoltage computed tomography (MVCT) imaging during the Tomotherapy treatment can be applied to monitor the successive variations in the parotid glands. While manual segmentation is time consuming, laborious, and subjective, automatic segmentation is quite challenging due to the complicated anatomical environment of head and neck as well as noises in MVCT images. In this article, we propose a localization-refinement scheme to segment the parotid gland in MVCT. After data pre-processing we use mask region convolutional neural network (Mask R-CNN) in the localization stage after data pre-processing, and design a modified U-Net in the following fine segmentation stage. To the best of our knowledge, this study is a pioneering work of deep learning on MVCT segmentation. Comprehensive experiments based on different data distribution of head and neck MVCTs and different segmentation models have demonstrated the superiority of our approach in terms of accuracy, effectiveness, flexibility, and practicability. Our method can be adopted as a powerful tool for radiation-induced injury studies, where accurate organ segmentation is crucial.
放射性口干症是头颈部肿瘤放射治疗中的一个主要问题,其主要原因是过量照射对腮腺的损伤。基于螺旋断层扫描的MVCT成像可用于监测腮腺的连续变化。人工分割耗时、费力、主观,而自动分割由于头颈部复杂的解剖环境以及MVCT图像中存在的噪声,具有很大的挑战性。在本文中,我们提出了一种定位-细化方案来分割MVCT中的腮腺。在数据预处理后,我们在数据预处理后的定位阶段使用掩模区域卷积神经网络(mask R-CNN),并在接下来的精细分割阶段设计改进的U-Net。据我们所知,这项研究是深度学习在MVCT分割方面的开创性工作。基于不同的头颈部mvct数据分布和不同的分割模型的综合实验证明了我们的方法在准确性、有效性、灵活性和实用性方面的优势。我们的方法可以作为辐射损伤研究的有力工具,其中准确的器官分割是至关重要的。
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引用次数: 3
An Energy Efficient Health Monitoring Approach with Wireless Body Area Networks 基于无线体域网络的节能健康监测方法
Pub Date : 2021-09-27 DOI: 10.1145/3501773
Seemandhar Jain, Prarthi Jain, P. K. Upadhyay, J. M. Moualeu, Abhishek Srivastava
Wireless Body Area Networks (WBANs) comprise a network of sensors subcutaneously implanted or placed near the body surface and facilitate continuous monitoring of health parameters of a patient. Research endeavours involving WBAN are directed towards effective transmission of detected parameters to a Local Processing Unit (LPU, usually a mobile device) and analysis of the parameters at the LPU or a back-end cloud. An important concern in WBAN is the lightweight nature of WBAN nodes and the need to conserve their energy. This is especially true for subcutaneously implanted nodes that cannot be recharged or regularly replaced. Work in energy conservation is mostly aimed at optimising the routing of signals to minimise energy expended. In this article, a simple yet innovative approach to energy conservation and detection of alarming health status is proposed. Energy conservation is ensured through a two-tier approach wherein the first tier eliminates “uninteresting” health parameter readings at the site of a sensing node and prevents these from being transmitted across the WBAN to the LPU. The second tier of assessment includes a proposed anomaly detection model at the LPU that is capable of identifying anomalies from streaming health parameter readings and indicates an adverse medical condition. In addition to being able to handle streaming data, the model works within the resource-constrained environments of an LPU and eliminates the need of transmitting the data to a back-end cloud, ensuring further energy savings. The anomaly detection capability of the model is validated using data available from the critical care units of hospitals and is shown to be superior to other anomaly detection techniques.
无线身体区域网络(wban)包括皮下植入或放置在体表附近的传感器网络,并有助于对患者的健康参数进行连续监测。涉及WBAN的研究工作旨在将检测到的参数有效地传输到本地处理单元(LPU,通常是移动设备),并在LPU或后端云上分析参数。WBAN中一个重要的问题是WBAN节点的轻量特性和节省其能量的需要。对于不能充电或定期更换的皮下植入淋巴结尤其如此。节能工作的主要目的是优化信号的路由,以尽量减少能量消耗。本文提出了一种简单而创新的节能和报警健康状态检测方法。通过两层方法确保节能,其中第一层消除了传感节点站点的“无趣”健康参数读数,并防止这些读数通过WBAN传输到LPU。第二层评估包括提议的LPU异常检测模型,该模型能够从流式健康参数读数中识别异常,并指示不良医疗状况。除了能够处理流数据之外,该模型还可以在LPU的资源受限环境中工作,并且不需要将数据传输到后端云,从而确保进一步节能。该模型的异常检测能力通过使用医院重症监护病房的可用数据进行验证,并被证明优于其他异常检测技术。
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引用次数: 2
期刊
ACM Transactions on Computing for Healthcare (HEALTH)
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