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Deep Learning for Personalized Electrocardiogram Diagnosis: A Review 用于个性化心电图诊断的深度学习:综述
Pub Date : 2024-09-12 DOI: arxiv-2409.07975
Cheng Ding, Tianliang Yao, Chenwei Wu, Jianyuan Ni
The electrocardiogram (ECG) remains a fundamental tool in cardiacdiagnostics, yet its interpretation traditionally reliant on the expertise ofcardiologists. The emergence of deep learning has heralded a revolutionary erain medical data analysis, particularly in the domain of ECG diagnostics.However, inter-patient variability prohibit the generalibility of ECG-AI modeltrained on a population dataset, hence degrade the performance of ECG-AI onspecific patient or patient group. Many studies have address this challengeusing different deep learning technologies. This comprehensive reviewsystematically synthesizes research from a wide range of studies to provide anin-depth examination of cutting-edge deep-learning techniques in personalizedECG diagnosis. The review outlines a rigorous methodology for the selection ofpertinent scholarly articles and offers a comprehensive overview of deeplearning approaches applied to personalized ECG diagnostics. Moreover, thechallenges these methods encounter are investigated, along with future researchdirections, culminating in insights into how the integration of deep learningcan transform personalized ECG diagnosis and enhance cardiac care. Byemphasizing both the strengths and limitations of current methodologies, thisreview underscores the immense potential of deep learning to refine andredefine ECG analysis in clinical practice, paving the way for more accurate,efficient, and personalized cardiac diagnostics.
心电图(ECG)仍然是心脏诊断的基本工具,但其解读传统上依赖于心内科医生的专业知识。深度学习的出现预示着医疗数据分析领域的一场革命,尤其是在心电图诊断领域。然而,患者间的差异性使得在群体数据集上训练的心电图人工智能模型无法通用,从而降低了心电图人工智能在特定患者或患者群体上的性能。许多研究利用不同的深度学习技术来应对这一挑战。这篇综合性综述系统地综合了大量研究,深入探讨了个性化心电图诊断中的前沿深度学习技术。综述概述了选择相关学术文章的严格方法,并全面概述了应用于个性化心电图诊断的深度学习方法。此外,还研究了这些方法遇到的挑战以及未来的研究方向,最终深入探讨了深度学习的整合如何改变个性化心电图诊断并提高心脏护理水平。通过强调当前方法的优势和局限性,这篇综述强调了深度学习在完善和重新定义临床实践中的心电图分析方面的巨大潜力,为更准确、高效和个性化的心脏诊断铺平了道路。
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引用次数: 0
Smart CSI Processing for Accruate Commodity WiFi-based Humidity Sensing 基于 WiFi 的智能商品湿度传感 CSI 处理技术
Pub Date : 2024-09-12 DOI: arxiv-2409.07857
Yirui Deng, Deepak Mishra, Shaghik Atakaramians, Aruna Seneviratne
Indoor humidity is a crucial factor affecting people's health and well-being.Wireless humidity sensing techniques are scalable and low-cost, making them apromising solution for measuring humidity in indoor environments withoutrequiring additional devices. Such, machine learning (ML) assisted WiFi sensingis being envisioned as the key enabler for integrated sensing and communication(ISAC). However, the current WiFi-based sensing systems, such as WiHumidity,suffer from low accuracy. We propose an enhanced WiFi-based humidity detectionframework to address this issue that utilizes innovative filtering and dataprocessing techniques to exploit humidity-specific channel state information(CSI) signatures during RF sensing. These signals are then fed into MLalgorithms for detecting different humidity levels. Specifically, our improvedde-noising solution for the CSI captured by commodity hardware for WiFisensing, combined with the k-th nearest neighbour ML algorithm and resolutiontuning technique, helps improve humidity sensing accuracy. Our commerciallyavailable hardware-based experiments provide insights into achievable sensingresolution. Our empirical investigation shows that our enhanced framework canimprove the accuracy of humidity sensing to 97%.
无线湿度传感技术具有可扩展性和低成本的特点,是测量室内环境湿度而无需额外设备的理想解决方案。因此,机器学习(ML)辅助的 WiFi 传感被视为集成传感与通信(ISAC)的关键推动因素。然而,目前基于 WiFi 的传感系统(如 WiHumidity)精度较低。为解决这一问题,我们提出了一种基于 WiFi 的增强型湿度检测框架,该框架利用创新的滤波和数据处理技术,在射频传感过程中利用湿度特有的信道状态信息(CSI)特征。然后将这些信号输入多项式算法,以检测不同的湿度水平。具体来说,我们改进了用于 WiFisensing 的商品硬件捕获 CSI 的去噪解决方案,结合 kth 近邻 ML 算法和分辨率调整技术,有助于提高湿度感应的准确性。我们基于商用硬件的实验提供了对可实现的传感分辨率的深入了解。我们的实证调查表明,我们的增强型框架可将湿度传感精度提高到 97%。
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引用次数: 0
Non-negative Weighted DAG Structure Learning 非负加权 DAG 结构学习
Pub Date : 2024-09-12 DOI: arxiv-2409.07880
Samuel Rey, Seyed Saman Saboksayr, Gonzalo Mateos
We address the problem of learning the topology of directed acyclic graphs(DAGs) from nodal observations, which adhere to a linear structural equationmodel. Recent advances framed the combinatorial DAG structure learning task asa continuous optimization problem, yet existing methods must contend with thecomplexities of non-convex optimization. To overcome this limitation, we assumethat the latent DAG contains only non-negative edge weights. Leveraging thisadditional structure, we argue that cycles can be effectively characterized(and prevented) using a convex acyclicity function based on the log-determinantof the adjacency matrix. This convexity allows us to relax the task of learningthe non-negative weighted DAG as an abstract convex optimization problem. Wepropose a DAG recovery algorithm based on the method of multipliers, that isguaranteed to return a global minimizer. Furthermore, we prove that in theinfinite sample size regime, the convexity of our approach ensures the recoveryof the true DAG structure. We empirically validate the performance of ouralgorithm in several reproducible synthetic-data test cases, showing that itoutperforms state-of-the-art alternatives.
我们要解决的问题是从节点观测中学习有向无环图(DAG)拓扑结构的问题,而节点观测遵循线性结构方程模型。最近的进展将组合 DAG 结构学习任务框定为一个连续优化问题,但现有方法必须与非凸优化的复杂性作斗争。为了克服这一局限,我们假设潜在 DAG 只包含非负边权重。利用这一附加结构,我们认为可以使用基于邻接矩阵对数确定的凸非周期性函数来有效地描述(和防止)周期。这种凸性允许我们将学习非负加权 DAG 的任务放宽为一个抽象的凸优化问题。我们提出了一种基于乘法的 DAG 恢复算法,它能保证返回全局最小值。此外,我们还证明了在样本量无限大的情况下,我们方法的凸性可以确保恢复真实的 DAG 结构。我们在几个可重复的合成数据测试案例中实证验证了我们算法的性能,结果表明它优于最先进的替代方法。
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引用次数: 0
Efficient Deep Learning-based Cascaded Channel Feedback in RIS-Assisted Communications RIS 辅助通信中基于深度学习的高效级联信道反馈
Pub Date : 2024-09-12 DOI: arxiv-2409.08149
Yiming Cui, Jiajia Guo, Chao-Kai Wen, Shi Jin
In the realm of reconfigurable intelligent surface (RIS)-assistedcommunication systems, the connection between a base station (BS) and userequipment (UE) is formed by a cascaded channel, merging the BS-RIS and RIS-UEchannels. Due to the fixed positioning of the BS and RIS and the mobility ofUE, these two channels generally exhibit different time-varyingcharacteristics, which are challenging to identify and exploit for feedbackoverhead reduction, given the separate channel estimation difficulty. Toaddress this challenge, this letter introduces an innovative deeplearning-based framework tailored for cascaded channel feedback, ingeniouslycapturing the intrinsic time variation in the cascaded channel. When an entirecascaded channel has been sent to the BS, this framework advocates the feedbackof an efficient representation of this variation within a subsequent periodthrough an extraction-compression scheme. This scheme involves RIS unit-grainedchannel variation extraction, followed by autoencoder-based deep compression toenhance compactness. Numerical simulations confirm that this feedback frameworksignificantly reduces both the feedback and computational burdens.
在可重构智能表面(RIS)辅助通信系统领域,基站(BS)和用户设备(UE)之间的连接由一个级联信道构成,合并了 BS-RIS 和 RIS-UE 信道。由于 BS 和 RIS 的固定位置以及 UE 的移动性,这两个信道通常表现出不同的时变特性,鉴于单独的信道估计困难,要识别和利用这些特性来减少反馈开销具有挑战性。为解决这一难题,本文介绍了一种基于深度学习的创新框架,该框架专为级联信道反馈量身定制,巧妙地捕捉了级联信道的内在时间变化。当整个级联信道被发送到 BS 时,该框架主张通过提取-压缩方案在随后的时间段内反馈这种变化的有效表示。该方案包括 RIS 单位粒度信道变化提取,然后是基于自动编码器的深度压缩,以提高压缩率。数值模拟证实,这种反馈框架显著减少了反馈和计算负担。
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引用次数: 0
Noisy Low Rank Column-wise Sensing 噪声低等级列式传感
Pub Date : 2024-09-12 DOI: arxiv-2409.08384
Ankit Pratap Singh, Namrata Vaswani
This letter studies the AltGDmin algorithm for solving the noisy low rankcolumn-wise sensing (LRCS) problem. Our sample complexity guarantee improvesupon the best existing one by a factor $max(r, log(1/epsilon))/r$ where $r$is the rank of the unknown matrix and $epsilon$ is the final desired accuracy.A second contribution of this work is a detailed comparison of guarantees fromall work that studies the exact same mathematical problem as LRCS, but refersto it by different names.
这篇文章研究了解决有噪声低秩列智传感(LRCS)问题的 AltGDmin 算法。我们的样本复杂度保证在现有最佳保证的基础上提高了一个因子 $max(r,log(1/epsilon))/r$,其中 $r$ 是未知矩阵的秩,$epsilon$ 是最终期望的精确度。
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引用次数: 0
Polarforming for Wireless Communications: Modeling and Performance Analysis 无线通信的极化成形:建模与性能分析
Pub Date : 2024-09-12 DOI: arxiv-2409.07771
Zijian Zhou, Jingze Ding, Chenbo Wang, Bingli Jiao, Rui Zhang
This paper presents, for the first time, the concept of textit{polarforming}for wireless communications. Polarforming refers to a novel technique thatenables dynamic adjustment of antenna polarization using reconfigurablepolarized antennas (RPAs). It can fully leverage polarization diversity toimprove the performance of wireless communication systems by aligning theeffective polarization state of the incoming electromagnetic (EM) wave with theantenna polarization. To better demonstrate the benefits of polarforming, wepropose a general RPA-aided system that allows for tunable antennapolarization. A wavefront-based channel model is developed to properly capturedepolarization behaviors in both line-of-sight (LoS) and non-line-of-sight(NLoS) channels. Based on this model, we provide a detailed description oftransmit and receive polarforming on planes of polarization (PoPs). We alsoevaluate the performance gains provided by polarforming under stochasticchannel conditions. Specifically, we derive a closed-form expression for therelative signal-to-noise ratio (SNR) gain compared to conventionalfixed-polarization antenna (FPA) systems and approximate the cumulativedistribution function (CDF) for the RPA system. Our analysis reveals thatpolarforming offers a diversity gain of two, indicating full utilization ofpolarization diversity for dual-polarized antennas. Furthermore, extensivesimulation results validate the effectiveness of polarforming and exhibitsubstantial improvements over conventional FPA systems. The results alsoindicate that polarforming not only can combat depolarization effects caused bywireless channels but also can overcome channel correlation when scattering isinsufficient.
本文首次提出了用于无线通信的极化(textit{polarforming})概念。极化成形指的是一种新技术,它能利用可重构极化天线(RPA)对天线极化进行动态调整。它能充分利用极化分集,通过使传入电磁波的有效极化状态与天线极化保持一致来提高无线通信系统的性能。为了更好地展示极化形成的优势,我们提出了一种允许可调天线极化的通用 RPA 辅助系统。我们开发了一个基于波前的信道模型,以正确捕捉视距(LoS)和非视距(NLoS)信道中的极化行为。基于该模型,我们对极化平面(PoPs)上的发送和接收极化进行了详细描述。我们还评估了极化格式在随机信道条件下带来的性能提升。具体来说,与传统的固定极化天线(FPA)系统相比,我们得出了信噪比(SNR)增益的闭式表达式,并对 RPA 系统的累积分布函数(CDF)进行了近似。我们的分析表明,极化可提供两个分集增益,表明双极化天线可充分利用极化分集。此外,扩展仿真结果验证了极化成形的有效性,并显示出与传统 FPA 系统相比的实质性改进。结果还表明,极化成形不仅能消除由无线信道引起的去极化效应,还能在散射不足时克服信道相关性。
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引用次数: 0
Spatial Adaptation Layer: Interpretable Domain Adaptation For Biosignal Sensor Array Applications 空间适配层:用于生物信号传感器阵列应用的可解释域自适应
Pub Date : 2024-09-12 DOI: arxiv-2409.08058
Joao Pereira, Michael Alummoottil, Dimitrios Halatsis, Dario Farina
Biosignal acquisition is key for healthcare applications and wearabledevices, with machine learning offering promising methods for processingsignals like surface electromyography (sEMG) and electroencephalography (EEG).Despite high within-session performance, intersession performance is hinderedby electrode shift, a known issue across modalities. Existing solutions oftenrequire large and expensive datasets and/or lack robustness andinterpretability. Thus, we propose the Spatial Adaptation Layer (SAL), whichcan be prepended to any biosignal array model and learns a parametrized affinetransformation at the input between two recording sessions. We also introducelearnable baseline normalization (LBN) to reduce baseline fluctuations. Testedon two HD-sEMG gesture recognition datasets, SAL and LBN outperform standardfine-tuning on regular arrays, achieving competitive performance even with alogistic regressor, with orders of magnitude less, physically interpretableparameters. Our ablation study shows that forearm circumferential translationsaccount for the majority of performance improvements, in line with sEMGphysiological expectations.
生物信号采集是医疗保健应用和穿戴设备的关键,机器学习为表面肌电图(sEMG)和脑电图(EEG)等信号的处理提供了前景广阔的方法。尽管会话内性能很高,但会话间性能却受到电极偏移的阻碍,这是众所周知的跨模态问题。现有的解决方案通常需要大量昂贵的数据集,并且/或者缺乏鲁棒性和可解释性。因此,我们提出了空间适配层(Space Adaptation Layer,SAL),它可以预置到任何生物信号阵列模型中,并在两次记录会话之间的输入端学习参数化的亲和变换。我们还引入了可学习基线归一化(LBN),以减少基线波动。在两个 HD-sEMG 手势识别数据集上的测试结果表明,SAL 和 LBN 优于常规阵列上的标准微调,甚至在使用对数回归器的情况下也能获得具有竞争力的性能,而且参数数量级更低,物理上可解释。我们的消融研究表明,前臂圆周平移占了性能改进的大部分,这与 sEMG 生理预期相符。
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引用次数: 0
Tracking Network Dynamics using Probabilistic State-Space Models 利用概率状态空间模型追踪网络动态
Pub Date : 2024-09-12 DOI: arxiv-2409.08238
Victor M. Tenorio, Elvin Isufi, Geert Leus, Antonio G. Marques
This paper introduces a probabilistic approach for tracking the dynamics ofunweighted and directed graphs using state-space models (SSMs). Unlikeconventional topology inference methods that assume static graphs and generatepoint-wise estimates, our method accounts for dynamic changes in the networkstructure over time. We model the network at each timestep as the state of theSSM, and use observations to update beliefs that quantify the probability ofthe network being in a particular state. Then, by considering the dynamics oftransition and observation models through the update and prediction steps,respectively, the proposed method can incorporate the information of real-timegraph signals into the beliefs. These beliefs provide a probabilitydistribution of the network at each timestep, being able to provide both anestimate for the network and the uncertainty it entails. Our approach isevaluated through experiments with synthetic and real-world networks. Theresults demonstrate that our method effectively estimates network states andaccounts for the uncertainty in the data, outperforming traditional techniquessuch as recursive least squares.
本文介绍了一种使用状态空间模型(SSM)跟踪无权图和有向图动态的概率方法。传统的拓扑推断方法假定图是静态的,并生成按点估算的结果,而我们的方法则不同,它考虑了网络结构随时间的动态变化。我们将每个时间步的网络建模为 SSM 的状态,并利用观测结果更新信念,量化网络处于特定状态的概率。然后,通过分别在更新和预测步骤中考虑过渡模型和观测模型的动态,所提出的方法可以将实时图信号的信息纳入信念中。这些信念提供了网络在每个时间步的概率分布,能够同时提供网络的估计值及其带来的不确定性。通过对合成网络和真实世界网络的实验,对我们的方法进行了评估。结果表明,我们的方法能有效估计网络状态并考虑数据的不确定性,优于递归最小二乘法等传统技术。
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引用次数: 0
Positioning and transmission in cell-free networks: ambiguity function, and MRC/MRT array gains 无细胞网络中的定位和传输:模糊函数和 MRC/MRT 阵列增益
Pub Date : 2024-09-12 DOI: arxiv-2409.08187
Luc Vandendorpe, Laurence Defraigne, Guillaume Thiran, Thomas Pairon, Christophe Craeye
Cell-free network is a new paradigm, originating from distributed MIMO, thathas been investigated for a few recent years as an alternative to thecelebrated cellular structure. Future networks not only consider classical datatransmission but also positioning, along the lines of Integrated Communicationsand Sensing (ISAC). The goal of this paper is to investigate at the same timethe ambiguity function which is an important metric for positioning and theunderstanding of its associated resolution and ambiguities, and the array gainwhen maximum ratio transmission (MRT) or MR combining (MRC) is implemented fordata communications. In particular, the role and impact of using a waveformwith non-zero bandwidth is investigated. The theoretical findings areillustrated by means of computational results.
无蜂窝网络是一种新的模式,源于分布式多输入多输出(MIMO),近几年已被研究用于替代著名的蜂窝结构。未来的网络不仅要考虑传统的数据传输,还要按照集成通信和传感(ISAC)的思路进行定位。本文的目标是同时研究作为定位重要指标的模糊函数,了解其相关的分辨率和模糊性,以及为数据通信实施最大比率传输(MRT)或磁共振结合(MRC)时的阵列增益。特别是,研究了使用非零带宽波形的作用和影响。计算结果对理论结论进行了说明。
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引用次数: 0
Identification of head impact locations, speeds, and force based on head kinematics 根据头部运动学确定头部撞击位置、速度和力度
Pub Date : 2024-09-12 DOI: arxiv-2409.08177
Xianghao Zhan, Yuzhe Liu, Nicholas J. Cecchi, Jessica Towns, Ashlyn A. Callan, Olivier Gevaert, Michael M. Zeineh, David B. Camarillo
Objective: Head impact information including impact directions, speeds andforce are important to study traumatic brain injury, design and evaluateprotective gears. This study presents a deep learning model developed toaccurately predict head impact information, including location, speed,orientation, and force, based on head kinematics during helmeted impacts.Methods: Leveraging a dataset of 16,000 simulated helmeted head impacts usingthe Riddell helmet finite element model, we implemented a Long Short-TermMemory (LSTM) network to process the head kinematics: tri-axial linearaccelerations and angular velocities. Results: The models accurately predictthe impact parameters describing impact location, direction, speed, and theimpact force profile with R2 exceeding 70% for all tasks. Further validationwas conducted using an on-field dataset recorded by instrumented mouthguardsand videos, consisting of 79 head impacts in which the impact location can beclearly identified. The deep learning model significantly outperformed existingmethods, achieving a 79.7% accuracy in identifying impact locations, comparedto lower accuracies with traditional methods (the highest accuracy of existingmethods is 49.4%). Conclusion: The precision underscores the model's potentialin enhancing helmet design and safety in sports by providing more accurateimpact data. Future studies should test the models across various helmets andsports on large in vivo datasets to validate the accuracy of the models,employing techniques like transfer learning to broaden its effectiveness.
目的:包括撞击方向、速度和力量在内的头部撞击信息对于研究创伤性脑损伤、设计和评估防护装备非常重要。本研究介绍了一种深度学习模型,该模型可根据头盔撞击时头部的运动学特性准确预测头部撞击信息,包括位置、速度、方向和力:利用 16,000 个使用 Riddell 头盔有限元模型模拟的头盔头部撞击数据集,我们实施了一个长短期记忆(LSTM)网络来处理头部运动学:三轴线性加速度和角速度。结果这些模型准确预测了描述撞击位置、方向、速度和撞击力曲线的撞击参数,所有任务的 R2 均超过 70%。进一步验证使用了由仪器护齿和视频记录的现场数据集,该数据集包括 79 次头部撞击,其中撞击位置可以清晰识别。深度学习模型的表现明显优于现有方法,在识别撞击位置方面达到了 79.7% 的准确率,而传统方法的准确率较低(现有方法的最高准确率为 49.4%)。结论精确度强调了该模型通过提供更准确的撞击数据来提高头盔设计和运动安全的潜力。未来的研究应在大型活体数据集上测试各种头盔和运动的模型,以验证模型的准确性,并采用迁移学习等技术扩大其有效性。
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引用次数: 0
期刊
arXiv - EE - Signal Processing
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