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The Impact of Scalp's Temperature on the Choice of the Best Layout for TTFields Treatment 头皮温度对TTFields治疗最佳布局选择的影响
IF 4.8 4区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2023-06-01 DOI: 10.1016/j.irbm.2023.100768
N. Gentilal , A. Naveh , T. Marciano , P.C. Miranda

Background and Objectives

Tumor Treating Fields (TTFields) is an FDA-approved technique used in the treatment of glioblastoma. It consists in applying an electric field (EF) with a frequency of 200 kHz using two pairs of transducer arrays. During treatment planning, the NovoTAL system is used to strategically place the arrays on the head in regions that maximize the EF at the tumor. Current should be injected at least 18 hours/day and induce a minimum EF of 1 V/cm at the tumor. To avoid any thermal harm to the patient, the temperature of the scalp is kept around 39.5 °C by changing the injected current. The goal of this study was to investigate how accounting for the temperature of the scalp during treatment planning might affect the choice of the best layout suggested by NovoTAL. Furthermore, we also studied the sensitivity of the results to the metric used to evaluate the layouts.

Methods

We used a realistic head model with a virtual glioblastoma in our studies. Through the NovoTAL system we obtained five realistic array layouts and we predicted the best one for our model based on the approach currently implemented in this system. We then repeated the same type of analysis, but also accounting for the temperature during planning. In both cases we ranked the layouts based on three different criteria: the LMiPD and the LAPD (local minimum and local average power densities, respectively) in the tumor and the SAR (specific absorption rate) in the head

Results

Accounting for the temperature does not significantly affect the choice of the best layout provided that the arrays are at least 1 cm apart from each other. Otherwise, a common temperature hotspot occurs in the scalp between the closest transducers of the adjacent arrays, which limits how much current can be injected and consequently treatment effectiveness. Also, the choice of the best layout depends on the criterion used.

Conclusions

Accounting for the temperature might led to significant variations in the current injected. The LMiPD might be used as a first criterion to choose the best treatment layout, followed by the LAPD and then the SAR.

背景与目的肿瘤治疗场(TTFields)是fda批准的用于胶质母细胞瘤治疗的技术。它包括使用两对换能器阵列施加频率为200 kHz的电场(EF)。在治疗计划期间,NovoTAL系统被用于有策略地将阵列放置在头部肿瘤的最大EF区域。电流应注射至少18小时/天,并在肿瘤处诱导至少1 V/cm的EF。为了避免对患者造成热伤害,通过改变注射电流将头皮温度保持在39.5°C左右。本研究的目的是调查在治疗计划中考虑头皮温度如何影响NovoTAL建议的最佳布局的选择。此外,我们还研究了结果对用于评估布局的度量的敏感性。方法采用真实的头部模型和虚拟的胶质母细胞瘤。通过NovoTAL系统,我们得到了五种真实的阵列布局,并根据该系统目前实现的方法预测了我们模型的最佳布局。然后我们重复了相同类型的分析,但也考虑了计划期间的温度。在这两种情况下,我们根据三个不同的标准对布局进行排名:肿瘤中的LMiPD和LAPD(分别为局部最小和局部平均功率密度)和头部的SAR(比吸收率)。结果考虑温度不会显著影响最佳布局的选择,只要阵列彼此之间至少相距1cm。否则,在相邻阵列的最接近的换能器之间的头皮上会出现一个常见的温度热点,这限制了可以注入的电流的大小,从而限制了治疗效果。此外,最佳布局的选择取决于所使用的标准。结论考虑温度可能会导致注射电流的显著变化。LMiPD可作为选择最佳治疗方案的第一标准,其次是LAPD,最后是SAR。
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引用次数: 1
Early Detection of Pressure Ulcers: Considering the Reperfusion 早期发现压疮:考虑再灌注
IF 4.8 4区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2023-06-01 DOI: 10.1016/j.irbm.2023.100753
N. Gillard , A. Leong-Hoi , J.P. Departe , P. Coignard , J. Kerdraon , W. Allegre

Objectives

Pressure ulcers are a great handicap for those who develop one. Pressure ulcers can take a long time to heal especially if detected late. These afflictions require a lot of time from the medical personnel and thus a great amount of money. We aim here to check the impact of continuous measurement on the performance of early pressure ulcer detection algorithms.

Material and methods

To detect pressure ulcers early on we use a simulation of a human buttocks to simulate the reaction of it to pressure. This simulation considers the most recent findings about pressure ulcers. In particular, the phenomenon of muscle stiffening when pressure is applied for a long period of time, and the reperfusion phenomenon. We can then simulate pressure captors on the outside interface of the buttocks to use these measurements for detection. We then determine the best threshold on the measured pressures to create standard algorithms that we compare to novel algorithms using an optimized threshold on a calculated damage based on the pressure measurement of the last 2 hours.

Results

We compare these different algorithms for the early detection of pressure ulcers and show the need to take the measurement variation in time for a better detection. The detection error is improved by 7.3% for balanced classes and 2.7% for a dataset with a majority of healthy buttocks.

Conclusion

We showed that taking the evolution of pressure instead of only instantaneous measurement can improve the early detection of pressure ulcer.

目的压疮对那些患有压疮的人来说是一个很大的障碍。压疮可能需要很长时间才能愈合,特别是如果发现晚了。这些疾病需要医务人员花费大量的时间,因此也需要大量的金钱。我们的目的是检查连续测量对早期压疮检测算法性能的影响。材料和方法为了早期发现压疮,我们使用模拟人体臀部来模拟它对压力的反应。这个模拟考虑了关于压疮的最新发现。特别是长时间施加压力时肌肉僵硬的现象,以及再灌注现象。然后我们可以在臀部的外部界面上模拟压力捕捉器,用这些测量来进行检测。然后,我们根据测量的压力确定最佳阈值,以创建标准算法,并将其与基于过去2小时压力测量的计算损伤的优化阈值的新算法进行比较。结果我们比较了这些不同的早期检测压疮的算法,并表明需要在时间上采取测量变化,以便更好地检测。对于平衡类,检测误差提高了7.3%,对于大多数健康臀部的数据集,检测误差提高了2.7%。结论采用压力演变法代替单纯的瞬时测量可提高压疮的早期检出率。
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引用次数: 0
Device Attitude and Real-Time 3D Visualization: An Interface for Elderly Care 设备姿态与实时三维可视化:一种老年人护理界面
IF 4.8 4区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2023-06-01 DOI: 10.1016/j.irbm.2022.100746
M. Abbas , M. Saleh , J. Prud'Homm , F. Lemoine , D. Somme , R. Le Bouquin Jeannès

Objective

this paper presents an innovative graphical user interface to visualize the attitude of a sensing device in a three-dimensional space, serving a wide-range of medical applications.

Material and methods

based on inertial measurement units (IMU) or on magnetic, angular rate and gravity (MARG) sensors, a processing unit provides Euler angles using a sensor fusion technique to display the orientation of the device relative to the Earth frame in real-time. The device is schematized by linking six polygonal regions, and is subject to sequential rotations by updating the graph each 350 ms. We conduct comparative studies between the two sensing devices, i.e. IMUs and MARGs, as well as two orientation filters, namely Madgwick's algorithm and Mahony's algorithm.

Results

the accuracy of the system is reported as a function of (i) the sampling frequency, (ii) the sensing unit, and (iii) the orientation filter, following two elderly care applications, namely fall risk assessment and body posture monitoring. The experiments are conducted using public datasets. The corresponding results show that Madgwick's algorithm is best suited for low sampling rates, whereas MARG sensors are best suited for the detection of postural transitions.

Conclusion

this paper addresses the different aspects and discusses the limitations of attitude estimation systems, which are important tools to help clinicians in their diagnosis.

目的提出一种创新的图形用户界面,用于在三维空间中可视化传感装置的姿态,服务于广泛的医疗应用。材料和方法:基于惯性测量单元(IMU)或磁、角速率和重力(MARG)传感器,处理单元使用传感器融合技术提供欧拉角,以实时显示设备相对于地球框架的方向。该装置通过连接6个多边形区域来进行示意图,并通过每350毫秒更新图形来进行顺序旋转。我们对imu和marg这两种传感器件以及Madgwick算法和Mahony算法这两种方向滤波器进行了比较研究。结果该系统的准确性报告为(i)采样频率,(ii)传感单元和(iii)方向滤波器的函数,遵循两种老年人护理应用,即跌倒风险评估和身体姿势监测。实验是使用公共数据集进行的。相应的结果表明,Madgwick算法最适合低采样率,而MARG传感器最适合检测姿势转换。结论对姿态估计系统的不同方面进行了阐述,并讨论了姿态估计系统的局限性,该系统是帮助临床医生进行诊断的重要工具。
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引用次数: 0
Deep Learning-Based Metaheuristic Weighted K-Nearest Neighbor Algorithm for the Severity Classification of Breast Cancer 基于深度学习的加权k近邻元启发式乳腺癌严重程度分类算法
IF 4.8 4区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2023-06-01 DOI: 10.1016/j.irbm.2022.100749
S.R. Sannasi Chakravarthy , N. Bharanidharan , H. Rajaguru

Objective

The most widespread and intrusive cancer type among women is breast cancer. Globally, this type of cancer causes more mortality among women, next to lung cancer. This made the researchers to focus more on developing effective Computer-Aided Detection (CAD) methodologies for the classification of such deadly cancer types. In order to improve the rate of survival and earlier diagnosis, an optimistic research methodology is required in the classification of breast cancer. Consequently, an improved methodology that integrates the principle of deep learning with metaheuristic and classification algorithms is proposed for the severity classification of breast cancer. Hence to enhance the recent findings, an improved CAD methodology is proposed for redressing the healthcare problem.

Material and Methods

The work intends to cast a light-of-research towards classifying the severities present in digital mammogram images. For evaluating the work, the publicly available MIAS, INbreast, and WDBC databases are utilized. The proposed work employs transfer learning for extricating the features. The novelty of the work lies in improving the classification performance of the weighted k-nearest neighbor (wKNN) algorithm using particle swarm optimization (PSO), dragon-fly optimization algorithm (DFOA), and crow-search optimization algorithm (CSOA) as a transformation technique i.e., transforming non-linear input features into minimal linear separable feature vectors.

Results

The results obtained for the proposed work are compared then with the Gaussian Naïve Bayes and linear Support Vector Machine algorithms, where the highest accuracy for classification is attained for the proposed work (CSOA-wKNN) with 84.35% for MIAS, 83.19% for INbreast, and 97.36% for WDBC datasets respectively.

Conclusion

The obtained results reveal that the proposed Computer-Aided-Diagnosis (CAD) tool is robust for the severity classification of breast cancer.

癌症在女性中最广泛和最具侵入性的类型是癌症。在全球范围内,这种类型的癌症导致的女性死亡率更高,仅次于癌症。这使得研究人员更加专注于开发有效的计算机辅助检测(CAD)方法来对这种致命的癌症类型进行分类。为了提高生存率和早期诊断,癌症的分类需要一种乐观的研究方法。因此,提出了一种将深度学习原理与元启发式和分类算法相结合的改进方法,用于癌症的严重程度分类。因此,为了加强最近的发现,提出了一种改进的CAD方法来解决医疗保健问题。材料和方法这项工作旨在为对数字乳房X光图像中存在的严重程度进行分类提供研究。为了评估工作,使用了公开的MIAS、INbreast和WDBC数据库。拟议的工作采用迁移学习来提取特征。该工作的新颖性在于,使用粒子群优化(PSO)、龙飞优化算法(DFOA)和乌鸦搜索优化算法(CSOA)作为一种转换技术,即将非线性输入特征转换为最小线性可分离特征向量,提高了加权k近邻(wKNN)算法的分类性能。结果将所提出的工作获得的结果与高斯朴素贝叶斯算法和线性支持向量机算法进行比较,其中所提出工作(CSOA wKNN)的分类精度最高,MIAS数据集的分类精度为84.35%,INbreast数据集为83.19%,WDBC数据集为97.36%。结论所提出的计算机辅助诊断(CAD)工具对癌症的严重程度分类具有较强的鲁棒性。
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引用次数: 11
Optimal Sensor Placement in Smart Home Using Building Information Modeling: A Home Support Application 基于建筑信息模型的智能家居传感器优化配置:家庭支持应用
IF 4.8 4区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2023-06-01 DOI: 10.1016/j.irbm.2022.100745
R. Ben Bachouch, Y. Fousseret, Y. Parmantier

Objectives

In this paper, we present a plugin for the optimal placement of sensors in a smart home. Our approach includes the Building Information Modeling (BIM) which is a plan that describes the building layout.

Material and methods

This plugin uses the CSTB EveBim viewer for loading IFC file representing the digital building's model. We use then, a mathematical model based on a mixed integer linear program, to determine the optimal sensor placement according to building and sensors characteristics.

Results

The results show the efficiency of the proposed algorithm and the developed plugin. We obtain an optimal solution after few seconds, and we show the sensor placement on the building digital model.

Conclusion

We show the relevance of the proposed plugin to equip room of retirement home or ambient assisted living in order to identify occupant activity for home support application.

在本文中,我们提出了一个插件,用于智能家居中传感器的最佳放置。我们的方法包括建筑信息模型(BIM),这是一个描述建筑布局的计划。材料和方法这个插件使用CSTB evbim查看器来加载代表数字建筑模型的IFC文件。然后,我们使用基于混合整数线性规划的数学模型,根据建筑物和传感器特性确定最佳传感器位置。结果验证了所提算法和所开发插件的有效性。我们在几秒钟后得到了最优解,并在建筑数字模型上展示了传感器的位置。我们展示了拟议的插件与养老院或环境辅助生活的相关性,以确定家庭支持应用的居住者活动。
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引用次数: 2
An Image Recognition Method for Urine Sediment Based on Semi-supervised Learning 基于半监督学习的尿液沉积物图像识别方法
IF 4.8 4区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2023-04-01 DOI: 10.1016/j.irbm.2022.09.006
Q. Ji , Y. Jiang , Z. Wu , Q. Liu , L. Qu

Objectives

Because there are many categories, large morphological differences and few labels of urinary sediment components, and uneven data in urine sediment images recognition, the accuracy and recall rate of the existing urine sediment images recognition methods are not ideal. The main purpose of this paper is to improve the accuracy and recall of urine sediment image recognition by proposing a urine sediment image classification method based on semi-supervised learning.

Methods

This paper proposes a method based on semi-supervised learning to classify urine sediment images. This algorithm designs a re-parameterization network (US-RepNet) for low-resolution urine sediment microscopic images to extract complex features of urine sediment images. The dual attention module is introduced on Us-RepNet to increase the extraction of fine-grained features from urine sediment images. And the cross-entropy loss (C.E. loss) function is optimized to train an unbiased classifier to improve long-tailed distribution image classification.

Results

The experimental results show that the accuracy of proposed method can reach 94% with only a small amount of labeled data for 16 types of urine sediment images under long-tail distribution.

Conclusion

The algorithm can recognize most types, and reduces the need for labeled information, while achieving excellent recognition and classification performance. Comprehensive analysis shows that it can be used as a practical reference for urine sediment analysis.

目的由于尿沉渣成分分类多、形态差异大、标签少,以及尿沉渣图像识别数据不均匀,现有尿沉渣图像的识别方法的准确率和召回率都不理想。本文的主要目的是通过提出一种基于半监督学习的尿沉渣图像分类方法,提高尿沉渣图像识别的准确性和召回率。方法提出一种基于半监督学习的尿沉渣图像分类方法。该算法为低分辨率尿沉渣显微图像设计了一个重新参数化网络(US RepNet),以提取尿沉渣图像的复杂特征。在Us RepNet上引入了双注意力模块,以增加对尿液沉积物图像中细粒度特征的提取。并对交叉熵损失(C.E.损失)函数进行了优化,以训练一个无偏分类器来改进长尾分布图像的分类。结果实验结果表明,对于长尾分布下的16种类型的尿沉渣图像,该方法仅需少量标记数据,准确率即可达到94%。结论该算法能够识别大多数类型,减少了对标记信息的需求,同时实现了良好的识别和分类性能。综合分析表明,该方法可作为尿沉渣分析的实用参考。
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引用次数: 2
Evaluation of a Wireless Home Sleep Monitoring System Compared to Polysomnography 无线家庭睡眠监测系统与多导睡眠描记仪的比较
IF 4.8 4区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2023-04-01 DOI: 10.1016/j.irbm.2022.09.002
Q. Pan, D. Brulin, E. Campo

Objective

Sleep is essential for human health. Bad sleep and sleep disorders have been increasingly prevalent and are gradually becoming a social problem that cannot be ignored. The current gold standard in sleep monitoring is polysomnography (PSG) allowing nearly complete approach. Unfortunately, this wealth of information is obtained at the cost of invasive system, only usable in hospital environment under the control of sleep experts. Therefore, the development of a wireless body network for long-term home sleep monitoring is a good way to achieve this in a less-intrusive, portable and autonomous way. In this paper, an overall architecture from the sensors to the user's display is presented with a focus on the main functions and hardware.

Method

The hardware architecture is composed of simple miniaturized wearable devices. Then, we introduce the chosen indicators for sleep monitoring and the algorithms developed for sleep stages classification. Finally we show the evaluation of our approach compared to the PSG. We illustrate the sleep stage classification during one night in the sleep unit of Toulouse University Hospital and highlight correlation between body temperature on extremities and Periodic Limb Movement during Sleep.

Results

Based on the confusion matrix analysis, the results show that the T1 method appears to be effective for the detection of awake and deep sleep in particular. For PLMS detection, we define the detection rules based on the foot movement data. The results show that the total number of PLMS and the number of PLMS distributed in each sleep stage detected by our foot module are both very close to the PSG. Furthermore, we have found correlations between body temperature and hypnogram and between body temperature on extremities and PLMS.

Conclusion

A wearable sensor system could be an alternative to PSG for long-term monitoring. Validation of the two proposed threshold-based algorithmic methods for sleep stage classification compared to the PSG gold standard shows good agreement, while the k-means based approach shows poor agreement with PSG. Furthermore, this method could be a good candidate for predicting periodic leg movements in sleep.

睡眠对人类健康至关重要。不良睡眠和睡眠障碍日益普遍,并逐渐成为一个不可忽视的社会问题。目前睡眠监测的黄金标准是多导睡眠图(PSG),允许几乎完全的方法。不幸的是,这些丰富的信息是以侵入性系统为代价获得的,只有在睡眠专家的控制下才能在医院环境中使用。因此,开发一种用于长期家庭睡眠监测的无线身体网络是一种以侵入性小、便携和自主的方式实现这一目标的好方法。本文介绍了从传感器到用户显示器的整体架构,重点介绍了主要功能和硬件。方法硬件结构由简单的微型可穿戴设备组成。然后,我们介绍了所选择的睡眠监测指标和为睡眠阶段分类开发的算法。最后,我们展示了与PSG相比我们的方法的评估。我们展示了图卢兹大学医院睡眠病房一个晚上的睡眠阶段分类,并强调了四肢体温与睡眠期间肢体周期性运动之间的相关性。结果基于混淆矩阵分析,结果表明T1方法似乎特别适用于清醒和深睡的检测。对于PLMS检测,我们基于足部运动数据定义检测规则。结果表明,我们的足部模块检测到的PLMS总数和分布在每个睡眠阶段的PLMS数量都非常接近PSG。此外,我们还发现体温与睡眠图之间以及四肢体温与PLMS之间存在相关性。结论可穿戴传感器系统可能是PSG的替代品,用于长期监测。与PSG黄金标准相比,所提出的两种基于阈值的睡眠阶段分类算法的验证显示出良好的一致性,而基于k均值的方法显示出与PSG的较差一致性。此外,这种方法可以很好地预测睡眠中的周期性腿部运动。
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引用次数: 2
Grouping Intrinsic Mode Functions and Residue for Pathological Classifications via Electroglottograms 通过声门电图分组本征模式功能和残差进行病理分类
IF 4.8 4区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2023-04-01 DOI: 10.1016/j.irbm.2022.11.001
G. Liao, B.W.-K. Ling, K.-G. Pang
<div><h3>Objectives</h3><p>The electroglottogram<span> (EGG) is a signal used for measuring the change of the relative contact area in the vocal cord during the throat production. In the recent years, the low cost and the non-invasive applications have been derived. Hence, the EGG has been applied in various science, engineering and medical fields such as in the basic voice science including the phonetics, the singing and the hearing as well as in the speech and the language therapy and the related clinical works including the voice production physiology, the swallowing and the psychology. However, the pathological classifications using the EGGs usually yield the poor performances. This is because the EGGs are required to decompose into the various components for extracting the features for performing the classifications. Nevertheless, the total numbers of the components decomposed by some time frequency representation such as the empirical mode decomposition (EMD) for different EGGs are different. Hence, the dimension of the feature vectors extracted from different EGGs is different. This introduces to the difficulty for building a machine learning model for performing the classification. This paper is to address this issue.</span></p></div><div><h3>Material and methods</h3><p>This paper proposes a method for grouping the intrinsic mode functions<span><span> (IMFs) and the residue obtained by applying the EMD to the EGGs for classifying between the healthy subjects and the pathological subjects. More precisely, this paper proposes a clustering based method to group the IMFs and the residue so that the total numbers of the grouped IMFs of different EGGs are the same. First, the IMFs and the residue of the EGGs are categorized into a desired number of groups based on their correlation coefficients. Second, the IMFs or the residue in each group are summed together to obtain the grouped IMF. Third, the mean frequency and the first formant of each grouped IMF are computed. Finally, a random forest is employed for performing the classification. To our best knowledge, this joint EMD and clustering based method is firstly proposed to preform the pathological voice detection. The </span>computer numerical simulations are conducted using the online available Saarbrücken voice database.</span></p></div><div><h3>Results</h3><p>Here, five cross validations have been performed. The mean accuracy, the mean specificity and the mean sensitivity among these five validations are 86.98, 79.92 and 91.57, respectively. The standard deviation of the accuracy, the specificity and the sensitivity among these five validations are ±2.00%, ±3.71% and ±2.13%, respectively. The simulation results show that our proposed method outperforms the common EGG or speech processing based methods.</p></div><div><h3>Conclusion</h3><p><span>This paper proposes a clustering based method for grouping the IMFs and the residue for performing the pathological classifications via the EGGs. Th
目的声门电图(EGG)是一种用于测量喉咙生产过程中声带相对接触面积变化的信号。近年来,低成本和非侵入性的应用已经出现。因此,EGG已被应用于各种科学、工程和医学领域,如语音基础科学,包括语音、歌唱和听力,语音和语言治疗以及相关的临床工作,包括语音产生生理学、吞咽和心理学。然而,使用EGG的病理分类通常产生较差的表现。这是因为需要将EGG分解为各种组件,以提取用于执行分类的特征。然而,对于不同的EGG,通过一些时间频率表示(例如经验模式分解(EMD))分解的分量的总数是不同的。因此,从不同的EGG提取的特征向量的维度是不同的。这介绍了建立用于执行分类的机器学习模型的困难。这篇论文就是为了解决这个问题。材料和方法本文提出了一种将EMD应用于EGG获得的固有模函数(IMF)和残差分组的方法,用于在健康受试者和病理受试者之间进行分类。更准确地说,本文提出了一种基于聚类的方法来对IMF和残差进行分组,以使不同EGG的分组IMF的总数相同。首先,根据其相关系数,将IMF和EGG的残差分类为所需数量的组。其次,将每组中的IMF或残差相加在一起,以获得分组的IMF。第三,计算每个分组IMF的平均频率和第一共振峰。最后,采用随机森林进行分类。据我们所知,这种基于EMD和聚类的联合方法首次被提出用于病理语音检测。计算机数值模拟是使用在线可用的萨尔布吕肯语音数据库进行的。结果在这里,进行了五次交叉验证。这五种验证的平均准确度、平均特异性和平均灵敏度分别为86.98、79.92和91.57。这五种验证的准确度、特异性和灵敏度的标准偏差分别为±2.00%、±3.71%和±2.13%。仿真结果表明,我们提出的方法优于常见的基于EGG或语音处理的方法。结论本文提出了一种基于聚类的IMF和残差分组方法,用于通过EGG进行病理分类。分组标准基于相关系数。发现与现有技术的方法相比,我们提出的方法可以实现大多数信噪比的最高分类。
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引用次数: 0
MonEco: a Novel Health Monitoring Ecosystem to Predict Respiratory and Cardiovascular Disorders MonEco:预测呼吸和心血管疾病的新型健康监测生态系统
IF 4.8 4区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2023-04-01 DOI: 10.1016/j.irbm.2022.09.003
Remo Lazazzera, Guy Carrault

Objectives: the present manuscript introduces a health monitoring ecosystem called MonEco, to monitor and predict respiratory and cardiovascular disorders.

Material and methods: the system comprehends a tablet application called eCardio and two smart devices named CareUp and UpNEA. eCardio is an application available for iOS devices that predicts cardiovascular risk based on user' data and habits. CareUp is a smartwatch for blood pressure estimation and fitness tracking. UpNEA is a smart glove for sleep monitoring, detecting sleep disruptive breathing events.

Results: MonEco smart devices embed novel algorithms and top-notch home health care monitoring technologies. The user can access data collected via a web application hosted by a remote server (AeneA), allowing clinicians to follow up on a patient's health.

Conclusion: MonEco wants to inspire and disclose the architecture of a connected health monitoring ecosystem.

目的:本文介绍了一个名为MonEco的健康监测生态系统,用于监测和预测呼吸和心血管疾病。材料和方法:该系统包括一个名为eCardio的平板电脑应用程序和两个名为CareUp和UpNEA的智能设备。eCardio是一款适用于iOS设备的应用程序,可根据用户的数据和习惯预测心血管风险。CareUp是一款用于血压估计和健身跟踪的智能手表。UpNEA是一款用于睡眠监测的智能手套,可检测睡眠中断性呼吸事件。结果:MonEco智能设备嵌入了新颖的算法和一流的家庭医疗保健监测技术。用户可以访问通过远程服务器(AeneA)托管的网络应用程序收集的数据,从而使临床医生能够跟踪患者的健康状况。结论:MonEco希望启发和揭示一个互联健康监测生态系统的架构。
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引用次数: 0
Therapeutic Ultrasound Applications in Cardiovascular Diseases: A Review 超声治疗在心血管疾病中的应用综述
IF 4.8 4区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2023-04-01 DOI: 10.1016/j.irbm.2022.07.001
G. Ditac , F. Bessière , C. Lafon

This review describes the use of ultrasound as a treatment modality for cardiovascular diseases. Ultrasound is widely used for diagnosis in cardiovascular pathology. However, it is not much used for therapeutic purposes. Therapeutic ultrasound includes thermal and mechanical effects. Therapeutic applications already available or still under research in venous, arterial, and cardiac diseases will be described.

这篇综述描述了超声作为心血管疾病治疗方式的应用。超声被广泛用于心血管病理学的诊断。然而,它并没有太多用于治疗目的。治疗超声包括热效应和机械效应。将描述在静脉、动脉和心脏疾病中已经可用或仍在研究中的治疗应用。
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引用次数: 0
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Irbm
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