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2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)最新文献

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Machine Learning for Automated Bladder Event Classification from Single-Channel Vesical Pressure Recordings 基于单通道膀胱压力记录的膀胱事件自动分类的机器学习
Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014792
V. Abbaraju, K. Lewis, S. Majerus
Analyzing urodynamic study (UDS) tracings can be prone to error in the presence of artifacts and subjective due to lack of standardization in clinical UDS interpretation. As such, the diagnosis of patients undergoing UDS would greatly benefit from a standardized, automated method to assist clinicians in interpreting UDS tracings. In this work, we evaluated a machine learning framework for automatically classifying bladder events from single-channel vesical pressure recordings $(P_{VES}) (N=60)$ into 4 possible classes: abdominal event (i.e., cough or Valsalva), voiding contraction, detrusor overactivity (DO) and no event. Wavelet multiresolution analysis of $P_{VES}$ was used to extract time-frequency localized wavelet coefficient vectors which were segmented into 0.8 second segments with 55 statistical features per segment. Feature selection was subsequently applied for three classifier architectures: a k-nearest classifier (KNN), an artificial neural network classifier (ANN) and a support vector machine classifier (SVM). Each classifier was trained and evaluated using five-fold cross validation, from which we derived the sensitivity, specificity, F1 score and AUC for all four classes and the overall classification accuracy for each classifier. The KNN, ANN and SVM classifiers labeled 7,861 0.8 second $P_{VES}$ segments with 91.5%, 90.8% and 82.4% accuracy, respectively. We have thus proposed the first framework for automated multi-event bladder classification using single-channel UDS data.
由于缺乏标准化的临床UDS解释,分析尿动力学研究(UDS)追踪可能容易出现人工制品和主观错误。因此,采用标准化、自动化的方法来帮助临床医生解释UDS追踪,将极大地有利于对接受UDS的患者进行诊断。在这项工作中,我们评估了一个机器学习框架,用于从单通道膀胱压力记录$(P_{VES}) (N=60)$自动将膀胱事件分类为4种可能的类别:腹部事件(即咳嗽或尿漏)、排尿收缩、逼尿肌过度活动(DO)和无事件。利用$P_{VES}$的小波多分辨率分析提取时频局部化小波系数向量,将小波系数向量分割为0.8 s的小波段,每段有55个统计特征。随后将特征选择应用于三种分类器架构:k-最近分类器(KNN)、人工神经网络分类器(ANN)和支持向量机分类器(SVM)。每个分类器使用五重交叉验证进行训练和评估,从中我们得出所有四个类别的敏感性,特异性,F1评分和AUC以及每个分类器的总体分类精度。KNN、ANN和SVM分类器分别标记了7861个0.8秒$P_{VES}$的片段,准确率分别为91.5%、90.8%和82.4%。因此,我们提出了第一个使用单通道UDS数据进行自动多事件膀胱分类的框架。
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引用次数: 0
The Effect of Graph Frequencies on Dynamic Structures in Graph Signal Processing 图信号处理中图频率对动态结构的影响
Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014954
S. Goerttler, M. Wu, F. He
Multivariate signals are signals consisting of multiple signals measured simultaneously over time and are most commonly acquired by sensor networks. The emerging field of graph signal processing (GSP) promises to analyse dynamic characteristics of multivariate signals, while at the same time taking the network, or spatial structure between the signals into account. To do so, GSP decomposes the multivariate signals into graph frequency signals, which are ordered by their magnitude. However, the meaning of the graph frequencies in terms of this ordering remains poorly understood. Here, we investigate the role the ordering plays in preserving valuable dynamic structures in the signals, with neuroimaging applications in mind. In order to overcome the limitations in sample size common to neurophysiological data sets, we introduce a minimalist simulation framework to generate arbitrary amounts of data. Using this artificial data, we find that lower graph frequency signals are less suitable for classifying neurophysiological data than higher graph frequency signals. We further introduce a baseline testing framework for GSP. Using this framework, we conclude that dynamic, or spectral structures are poorly preserved in GSP, high-lighting current limitations of GSP for neuroimaging.
多元信号是由多个信号在一段时间内同时测量的信号组成的信号,最常由传感器网络获取。新兴的图信号处理(GSP)领域有望分析多元信号的动态特性,同时考虑信号之间的网络或空间结构。为此,GSP将多变量信号分解成图形频率信号,这些信号按其幅度排序。然而,图形频率在这种顺序方面的含义仍然知之甚少。在这里,我们研究了排序在保留信号中有价值的动态结构方面所起的作用,并考虑了神经成像的应用。为了克服神经生理学数据集常见的样本量限制,我们引入了一个极简模拟框架来生成任意数量的数据。利用这些人工数据,我们发现较低的图频信号比较高的图频信号更不适合对神经生理数据进行分类。我们进一步引入普惠制的基准测试框架。使用这个框架,我们得出结论,动态或光谱结构在GSP中保存得很差,GSP用于神经成像的高照明电流限制。
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引用次数: 0
An LSTM-based Recurrent Neural Network for Neonatal Sepsis Detection in Preterm Infants 基于lstm的递归神经网络在早产儿新生儿败血症检测中的应用
Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014948
Antoine Honoré, H. Siren, R. Vinuesa, S. Chatterjee, E. Herlenius
Early and accurate neonatal sepsis detection (NSD) can help reduce mortality, morbidity and antibiotic consumption in premature infants. NSD models are often designed and evaluated in case control setups and using data derived from patient electrocardiogram (ECG) only. In this article, we evaluate our models in a more realistic retrospective cohort study setup. We use data from different modalities, including ECG, chest impedance, pulse oximetry, demographics factors and repetitive measurements of body weights. We study both the vanilla and long-short-term-memory (LSTM) Recurrent Neural Networks (RNN) architectures in a sequence to sequence mapping framework for NSD. We compare the performances of the models with logistic regression (LR) on a variety of classification metrics in a leave-one-out cross validation framework. The population we used contains 118 very low birth weight infants, among which 10 experienced sepsis. We showed that LSTM-based RNNs are both (1) more conservative and (2) more precise than LR or vanilla RNN, with a true negative rate at least +26% higher and a precision score of 0.16 compared to 0.06 for LR. This indicates that LSTM-based RNNs have the potential to reduce the false alarm rate of existing linear models, while providing a reliable diagnostic aid for neonatal sepsis.
早期和准确的新生儿败血症检测(NSD)可以帮助降低早产儿的死亡率、发病率和抗生素消耗。NSD模型通常在病例对照设置中设计和评估,并且仅使用来自患者心电图(ECG)的数据。在本文中,我们在一个更现实的回顾性队列研究设置中评估我们的模型。我们使用不同方式的数据,包括心电图、胸阻抗、脉搏血氧仪、人口统计学因素和体重的重复测量。我们在NSD的序列到序列映射框架中研究了vanilla和长短期记忆(LSTM)递归神经网络(RNN)架构。我们在留一交叉验证框架中比较了模型与逻辑回归(LR)在各种分类指标上的性能。我们使用的人群包含118名极低出生体重婴儿,其中10名患有败血症。我们发现,基于lstm的RNN比LR或vanilla RNN(1)更保守,(2)更精确,其真阴性率至少高出+26%,精度分数为0.16,而LR为0.06。这表明基于lstm的rnn有可能降低现有线性模型的虚警率,同时为新生儿败血症提供可靠的诊断辅助。
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引用次数: 0
A Study of How Abnormalities of the CREB Protein Affect a Neuronal System and Its Signals: Modeling and Analysis Using Experimental Data CREB蛋白异常如何影响神经元系统及其信号的研究:使用实验数据建模和分析
Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014785
A. Emadi, A. Abdi
It is well understood that the CREB protein is highly involved in neuronal mechanisms underlying memory and learning in mammalian brain, and deficiencies in CREB activity can result in transition to certain pathological conditions. In this paper, we use some published experimental data, along with a neuronal system composed of the Izhikevich neuron model, to characterize how CREB abnormalities can alter neuronal signals and the system behavior. The abnormal data are extracted from intracellular recordings collected from the neurons of transgenic mice expressing VP16-CREB - a constitutively active form of CREB - whereas the normal data are obtained from the wild-type mice neurons. Upon estimating the neuron model parameters from the experimental data, we observe that the model exhibits good fit to both normal and abnormal data, for various synaptic input currents. To study the effect of CREB abnormalities on the considered neuronal system, we use the information theoretic redundancy parameter. It basically measures - for the system output neuron - the amount of spike count information overlap that exists between the states of the stimulus currents injected to the input neurons. Our analysis reveals a noticeable increase in the information redundancy, when CREB behaves abnormally. This finding motivates further exploration of the biological implications of the information redundancy in neuronal systems, and its use as a parameter to model abnormalities in CREB and perhaps other important transcription factors involved in learning and memory.
众所周知,CREB蛋白高度参与哺乳动物大脑记忆和学习的神经元机制,CREB活性的缺乏可导致向某些病理状况的转变。在本文中,我们使用一些已发表的实验数据,以及由Izhikevich神经元模型组成的神经元系统,来表征CREB异常如何改变神经元信号和系统行为。异常数据是从表达VP16-CREB的转基因小鼠神经元的细胞内记录中提取的,VP16-CREB是CREB的组成活性形式,而正常数据是从野生型小鼠神经元中获得的。根据实验数据估计神经元模型参数,我们观察到该模型对各种突触输入电流的正常和异常数据都有很好的拟合。为了研究CREB异常对所考虑的神经元系统的影响,我们使用了信息论冗余参数。对于系统输出神经元来说,它基本上测量的是在注入到输入神经元的刺激电流状态之间存在的尖峰计数信息重叠的数量。我们的分析显示,当CREB行为异常时,信息冗余显著增加。这一发现激发了对神经系统中信息冗余的生物学意义的进一步探索,并将其作为CREB异常模型的参数,以及其他与学习和记忆有关的重要转录因子。
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引用次数: 2
Analysis of Interpretable Handwriting Features to Evaluate Motoric Patterns in Different Neurodegenerative Diseases 不同神经退行性疾病运动模式的可解释笔迹特征分析
Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014966
D. D. Kairamkonda, P. S. Mandaleeka, A. Favaro, C. Motley, A. Butala, E. Oh, R. Stevens, N. Dehak, L. Moro-Velázquez
Clinicians currently use handwriting as one of the methods to establish the presence and monitor the progression of neurodegenerative diseases (NDs). While common handwriting evaluation methods are valuable means to detect fine motor and cognitive impairments associated with NDs, these are observer-dependent and subjective. In the present study, we analyzed a broad array of interpretable features, some proposed for the first time in this study, obtained from online handwriting data of participants with NDs and control subjects (CTRL). ND participants have Alzheimer's disease (AD), Parkinson's disease (PD), or Parkinson's disease mimics (PDM). Hand-writing data from three different neuropsychological tasks was used: Copy Text task, Copy Cube task, and Copy Image task. Then, we arranged three complementary sets of features and conducted a statistical analysis to test their significance between groups. Overall results suggested that subjects with AD reported a significantly higher $(p < 0.05)$ amount of data points and total duration with respect to the CTRL group in almost all the tasks under assessment. On the other hand, subjects with PD showed a significantly lower $(p < 0.05)$ horizontal width (both on tablet and in the air). Even though the AD and PDM groups showed a significantly lower velocity and acceleration $(p < 0.05)$, their number of inversions in velocity and acceleration was significantly greater $(p < 0.05)$, which indicates disfluency in writing. The features that we have used were found to provide good results in differentiating the studied groups and could be considered as part of diagnostic tools for the assessment and monitoring of NDs in clinical trials.
临床医生目前使用笔迹作为方法之一,以建立存在和监测神经退行性疾病(NDs)的进展。虽然常见的笔迹评估方法是检测与NDs相关的精细运动和认知障碍的有价值的手段,但这些方法依赖于观察者和主观。在本研究中,我们分析了一系列广泛的可解释特征,其中一些特征是本研究首次提出的,这些特征来自NDs参与者和对照受试者(CTRL)的在线手写数据。ND参与者患有阿尔茨海默病(AD)、帕金森氏病(PD)或帕金森氏病模拟(PDM)。研究人员使用了三种不同神经心理学任务的手写数据:复制文本任务、复制立方体任务和复制图像任务。然后,我们安排了三个互补的特征集,并进行了统计分析,以检验其组间显著性。总体结果表明,与对照组相比,AD组在几乎所有评估任务中报告的数据点数量和总持续时间显著增加(p < 0.05)。另一方面,PD受试者在平板电脑和空气中显示显著降低的水平宽度(p < 0.05)。尽管AD组和PDM组的速度和加速度$显著降低(p < 0.05)$,但其速度和加速度反转次数$显著增加(p < 0.05)$,这表明写作不流畅。我们使用的特征在区分研究群体方面提供了良好的结果,可以被认为是临床试验中评估和监测NDs的诊断工具的一部分。
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引用次数: 0
Playing by the Rules: Structural and Spatial Organization of Biofilm Communities 游戏规则:生物膜群落的结构和空间组织
Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014799
B. Buttaro
Bacterial biofilms are a ubiquitous form of bacterial growth. Biofilms consisting of bacterial, viruses, and protozoa exist in the environment and our gastrointestinal tract. Robust bacterial biofilms surviving off light and fixing carbon exist in deserts and on marble monuments. Biofilms can be a medical challenge when composed of a single pathogenic species or promote human health as a highly evolved microbiome ecology composed of hundreds of species. Regardless of their location, certain patterns emerge. Biofilms can behave as viscous liquids or rigid structures. The rigid structures can provide protection to more viscous biofilms. These structures can be intricately organized structures ready to respond to changes in environmental conditions at a moment's notice. Within complex multi-species communities, bacteria organize themselves into smaller communities, which are often interdependent of each other. Understanding the rules that govern their structural and spatial arrangements supporting their functions and interactions is a complex challenge best approached by a multidisciplinary approach of computational mathematics, mathematical modeling, machine learning, and engineering. In this talk, we will review biofilm basics defining what is a bacterial biofilm, their ubiquitous nature, and their roles in promoting health and producing difficult to treat diseases. Then we will explore processes shared by biofilms independent of their environment and specific bacterial species composition and the methods to study them. Studying these processes may reveal underlying principles driving the structural and spatial arrangements of most biofilms. Topics will include the composition and material properties of biofilms and how ordered matrix molecules, and possibly aggregation, contribute to rigid structuredevelopment. The next part of the talk will review the function of rigid structures. Rigid structures form when bacteria are under stress, including antibiotic stress, to provide protection to the community allowing survival and even continued growth. This suggests multicellular behavior with parts of the community providing protection to other regions that are actively growing to replace the dying cells resulting in steady state survival of a community including the formation of regions of viscous biofilm behind rigid structures under flow. Thiswill include a discussion on how mobile genetic elements can reshape biofilms and possibly make commensal microbiota bacteria more pathogenic (able to cause disease). Finally, the use of simple interdependent communities in extreme environments will be discussed as a model for spatial organization of biofilms communities, which may have implications for establishment of interdependent smaller communities within the context of larger multi-kingdom species.
细菌生物膜是细菌生长的一种普遍形式。由细菌、病毒和原生动物组成的生物膜存在于环境和我们的胃肠道中。在沙漠和大理石纪念碑上存在着能在光线下生存并固定碳的强健细菌生物膜。当生物膜由单一致病物种组成时,可能是一个医学挑战,或作为由数百种物种组成的高度进化的微生物组生态促进人类健康。不管它们的位置如何,都会出现某些模式。生物膜可以表现为粘性液体或刚性结构。刚性结构可以为粘性较大的生物膜提供保护。这些结构可以是复杂的组织结构,随时准备响应环境条件的变化。在复杂的多物种群落中,细菌将自己组织成更小的群落,这些群落往往相互依存。理解控制它们的结构和空间安排、支持它们的功能和相互作用的规则是一项复杂的挑战,最好通过计算数学、数学建模、机器学习和工程等多学科方法来解决。在这次演讲中,我们将回顾生物膜的基础知识,定义什么是细菌生物膜,它们无处不在的性质,以及它们在促进健康和产生难以治疗的疾病中的作用。然后,我们将探索独立于环境和特定细菌种类组成的生物膜共享的过程以及研究它们的方法。研究这些过程可能揭示驱动大多数生物膜的结构和空间排列的基本原理。主题将包括生物膜的组成和材料特性,以及有序的基质分子和可能的聚集如何促进刚性结构的发展。讲座的下一部分将回顾刚性结构的功能。当细菌在压力下,包括抗生素压力下,形成刚性结构,为群落提供保护,使其能够生存甚至继续生长。这表明,在多细胞行为中,群落的一部分为正在积极生长的其他区域提供保护,以取代死亡的细胞,从而导致群落的稳定生存,包括在流动下刚性结构后面形成粘性生物膜区域。这将包括关于移动遗传元素如何重塑生物膜和可能使共生微生物群细菌更具致病性的讨论。最后,将讨论在极端环境中使用简单的相互依赖群落作为生物膜群落空间组织的模型,这可能对在较大的多界物种背景下建立相互依赖的较小群落具有启示意义。
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引用次数: 0
Computational Cellular Model of Heart Rate Variability During Controlled Respiration 控制呼吸过程中心率变异性的计算细胞模型
Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014961
N. Sadowski, G. Drzewiecki
The sinoatrial node (SAN), located in the right atrium wall, is the heart's biological pacemaker and determines heart rate due to the repetitive spontaneous action potentials for cardiac rhythmic contractions in the heart pacemaker cells. The funny current (If) and SAN, together with regulation by the sympathetic and parasympathetic nervous systems, modulate the frequency of the SAN action potential. The interaction of these systems is responsible for the rhythmic pacemaker activity, controlling heart rate, and abnormalities resulting in arrhythmias.
窦房结(SAN)位于右心房壁上,是心脏的生物起搏器,通过心脏起搏器细胞中心脏节律性收缩的重复自发动作电位来决定心率。趣味电流(If)和SAN在交感神经系统和副交感神经系统的调控下,共同调节SAN动作电位的频率。这些系统的相互作用负责节律性起搏器活动,控制心率和导致心律失常的异常。
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引用次数: 0
Calibration of Automatic Seizure Detection Algorithms 自动缉获检测算法的校准
Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014868
A. Borovac, T. Runarsson, G. Thorvardsson, S. Gudmundsson
An EEG seizure detection algorithm employed in a clinical setting is likely to encounter many EEG segments that are difficult to classify due to the complexity of EEG signals and small data sets frequently used to train seizure detectors. The detectors should therefore be able to notify the clinician when they are uncertain in their predictions and they should also be accurate for confident predictions. This would enable the clinician to focus mainly on the parts of the recording where confidence in predictions is low. Here we analyse the calibration of neonatal and adult seizure detection algorithms based on a convolutional neural network in terms of how well the output seizure/non-seizure probabilities estimate the corresponding empirical frequencies. We found that the detectors turned out to be overconfident, in particular when incorrectly predicting seizure segments as non-seizure segments. The calibration of both detectors, measured in terms of expected calibration error and overconfidence error, was improved noticeably with the use of Monte Carlo dropout. We find that a straightforward application of dropout during training and classification leads to a noticeable improvement in the calibration of EEG seizure detectors based on a convolutional neural network.
由于脑电图信号的复杂性和训练癫痫发作检测器所用的小数据集,临床上应用的脑电图发作检测算法可能会遇到许多难以分类的脑电图片段。因此,当他们的预测不确定时,检测器应该能够通知临床医生,并且对于有信心的预测也应该是准确的。这将使临床医生能够主要集中在对预测的信心较低的记录部分。在这里,我们分析了基于卷积神经网络的新生儿和成人癫痫检测算法的校准,根据输出癫痫/非癫痫概率对相应经验频率的估计程度。我们发现检测器被证明过于自信,特别是当错误地预测癫痫发作段为非癫痫发作段时。使用蒙特卡罗dropout后,两个检测器的校准(以预期校准误差和过置信度误差衡量)都得到了显著改善。我们发现,在训练和分类过程中直接应用dropout可以显著改善基于卷积神经网络的脑电图发作检测器的校准。
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引用次数: 1
Analysis of the Impact of the Presence of Physical Pain in fNIRS-based BCI Systems 基于fnir的脑机接口系统中存在身体疼痛的影响分析
Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014741
A. Subramanian, F. Shamsi, L. Najafizadeh
An important application of brain computer interface devices (BCIs) is in assistive systems for patients with motor and communication disabilities. Due to their condition, these patients may experience pain. However, how the presence of pain influences the operation of such BCIs has not been fully investigated. This paper studies the impact of the presence of acute pain on the classification accuracy of a BCI, which employs functional near infrared spectroscopy (fNIRS) for brain signal acquisition. Cortical signals are obtained in the presence and absence of an external pain stimulus, while participants perform two mental arithmetic tasks. Convolutional neural network (CNN) is used to classify the tasks. It is observed that when the classifier is trained on pain-free data and tested on data obtained in the presence of pain, the classification accuracy significantly drops. Next, multi-label classification is performed to simultaneously identify the presence of pain and classify the tasks, further demonstrating that the distinction of tasks in the presence of pain is challenging. Finally, to mitigate the impact of pain, it is proposed to train the model collectively on data obtained in the presence and the absence of pain. It is observed that using this approach significantly improves the classification accuracy. Our results suggest that it is critical to include data obtained in the presence of pain in the training process of the classification models, when designing BCIs in assistive systems for patients.
脑机接口设备(BCIs)的一个重要应用是在运动和交流障碍患者的辅助系统中。由于他们的病情,这些患者可能会感到疼痛。然而,疼痛的存在如何影响此类脑机接口的操作尚未得到充分的研究。本文研究了急性疼痛的存在对脑机接口(BCI)分类准确度的影响,该脑机接口采用功能性近红外光谱(fNIRS)进行脑信号采集。当参与者执行两项心算任务时,在有和没有外部疼痛刺激的情况下获得皮层信号。使用卷积神经网络(CNN)对任务进行分类。可以观察到,当分类器在无痛的数据上进行训练,并在有痛的数据上进行测试时,分类准确率明显下降。接下来,进行多标签分类,同时识别疼痛的存在并对任务进行分类,进一步证明在疼痛存在的情况下区分任务是具有挑战性的。最后,为了减轻疼痛的影响,建议在存在和不存在疼痛的情况下对模型进行集体训练。使用该方法可以显著提高分类精度。我们的研究结果表明,在为患者设计辅助系统中的脑机接口时,在分类模型的训练过程中包括疼痛存在的数据是至关重要的。
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引用次数: 0
Smart Walker: an IMU-Based Device for Patient Activity Logging and Fall Detection 智能步行者:一种基于imu的患者活动记录和跌倒检测设备
Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014869
M. Huang, E. Clancy
Hip fractures are common in the geriatric population and represent a growing social and economic burden [1]. They are associated with decreased mobility, and the recovery period can be prolonged. Early mobilization is a critical component of the recovery process. Few studies have quantitatively measured activity levels in patients after hip fracture surgery, resulting in a lack of objective data about mobility status after hospital discharge. Furthermore, each year about 1.5 million elderly people are injured falling, and about 47,300 people aged ≥65 years suffer injuries from falls using walking aids that require an emergency room visit [2]. Patients using walkers are seven times more likely to fall than those that use canes. The risk of repeat falls has been shown to be especially high in patients who have already sustained a hip fracture.
髋部骨折在老年人群中很常见,并成为日益增长的社会和经济负担[1]。它们与活动能力下降有关,并且恢复期可以延长。早期动员是恢复进程的关键组成部分。很少有研究定量测量髋部骨折术后患者的活动水平,导致缺乏关于出院后活动状况的客观数据。此外,每年约有150万老年人摔倒受伤,约有47300名≥65岁的老年人使用助行器摔倒受伤,需要急诊[2]。使用助行器的患者摔倒的可能性是使用拐杖的患者的7倍。对于已经经历过髋部骨折的患者,再次跌倒的风险尤其高。
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引用次数: 0
期刊
2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)
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