IF 4.8 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2023-09-07 DOI: 10.1016/j.irbm.2023.100799

Shijie Fang , Jia Fu , Chen Du , Tong Lin , Yan Yan

Objectives

Laryngoscopy is a medical procedure for obtaining a view of the human larynx. It is challenging for clinicians to distinguish laryngeal neoplasms by human visual observation. Recent deep learning methods can assist clinicians in improving the accuracy of distinguishing. However, existed methods are often trained on large-scale private datasets, while other researchers and hospitals can neither access these private datasets nor afford to build such large-scale datasets. In this paper, we focus on identifying laryngeal neoplasms under the “small data” regime, which is more important for many small hospitals to investigate deep learning models for diagnosis.

Material and methods

We build an extremely small dataset consisting of 279 laryngoscopic images of different categories. We found that traditional deep learning models for image classification cannot achieve satisfactory performance for small data, due to the great variability of recording laryngoscopic images and the small area of the neoplasms. To address these difficulties, we propose to employ object detection methods for this small data problem. Concretely, a Faster R-CNN is implemented here, which combines the DropBlock regularization technique to alleviate overfitting additionally.

Results

Compared to previous methods, our model is more robust to overfitting and can predict the location and category of detected neoplasms simultaneously. Our method achieves 73.00% overall accuracy, which is higher than the average of clinicians (65.05%) and the recent state-of-the-art classification method (65.00%).

Conclusion

The proposed method shows great ability to detect both the category and location of neoplasms and can be served as a screening tool to help the final decisions of clinicians.

目的喉镜检查是一种获取人类喉部视野的医学程序。对于临床医生来说，通过人类视觉观察来区分喉部肿瘤是一项具有挑战性的工作。最近的深度学习方法可以帮助临床医生提高区分的准确性。然而，现有的方法通常是在大规模的私人数据集上进行训练的，而其他研究人员和医院既无法访问这些私人数据集，也无力构建这样的大规模数据集。在本文中，我们专注于在“小数据”机制下识别喉部肿瘤，这对许多小型医院研究诊断的深度学习模型更为重要。材料和方法我们建立了一个极小的数据集，由279张不同类别的喉镜图像组成。我们发现，由于记录喉镜图像的可变性大，肿瘤面积小，传统的图像分类深度学习模型无法在小数据下实现令人满意的性能。为了解决这些困难，我们建议对这个小数据问题使用对象检测方法。具体来说，这里实现了一个更快的R-CNN，它结合了DropBlock正则化技术来额外缓解过拟合。结果与以往的方法相比，我们的模型对过拟合更具鲁棒性，可以同时预测检测到的肿瘤的位置和类别。我们的方法总体准确率达到73.00%，高于临床医生的平均水平（65.05%）和最新的分类方法（65.00%）。

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引用次数: 0

Simplified Muscle-Recruitment Strategy During Walking in Parkinson's Disease People: A Time-Frequency Analysis of EMG Signal 帕金森病患者行走过程中简化的肌肉补充策略:肌电图信号的时频分析

IF 4.8 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2023-09-04 DOI: 10.1016/j.irbm.2023.100798

Francesco Di Nardo , Marco Romanato , Fabiola Spolaor , Daniele Volpe , Sandro Fioretti , Zimi Sawacha

Objective

Although gait analysis has been widely adopted to describe Parkinson's disease (PD) dysfunctions during walking, few efforts have been made to understand muscle activity role. The current study aims to characterize lower-limb-muscle recruitment during walking in time-frequency domain, based on Continuous Wavelet Transform (CWT) analysis of surface-electromyography (sEMG) signal from lower-limb muscles.

Materials and methods

sEMG signals from Tibialis Anterior (TA), Gastrocnemius Lateralis (GL), Rectus Femoris (RF), and Biceps Femoris (BF) of 20 people with PD and 10 age-matched healthy controls (HC) were acquired during gait. sEMG signals were processed applying a CWT-based approach to assess the occurrence frequency (OF, i.e., the percentage of strides of each muscle activation occurrence) and the frequency content of each muscle activation (in Hz). These parameters are rarely quantified in PD.

Results

Compared to HC, people with PD displayed a significant decrease (p<0.05) in median OF on RF, BF, and TA, indicating a tendency to reduce the global involvement of lower-limb muscles. No significant differences (p>0.05) in OF were detected among muscle within the same population. No significant changes (p>0.05) in frequency content were revealed in PD.

Conclusion

This analysis suggests that people with PD are characterized by a reduced recruitment of those muscles typically adopted to finely control body-segment motion and a concomitant increased recruitment of those muscles mainly involved in locomotion. No substantial alteration in recruiting muscle fibers is associated with PD. These findings suggest that people with PD are inclined to adopt simpler muscular-recruitment strategies during walking, compared to HC.

尽管步态分析已被广泛用于描述帕金森病（PD）在行走过程中的功能障碍，但很少有人致力于了解肌肉活动的作用。本研究旨在基于对下肢肌肉表面肌电信号的连续小波变换（CWT）分析，在时频域中表征步行过程中下肢肌肉的募集。材料和方法在步态过程中采集20例帕金森病患者和10例年龄匹配的健康对照（HC）的胫骨前肌（TA）、腓肠肌外侧肌（GL）、股直肌（RF）和股二头肌（BF）的肌电信号。应用基于CWT的方法来处理sEMG信号，以评估发生频率（OF，即每次肌肉激活发生的步幅百分比）和每次肌肉激活的频率含量（以Hz为单位）。这些参数很少在PD中量化。结果与HC相比，PD患者在RF、BF和TA上的中值OF显著降低（p＜0.05），表明有减少下肢肌肉整体受累的趋势。在同一群体内的肌肉之间没有检测到OF的显著差异（p＞0.05）。PD中的频率含量没有显著变化（p＞0.05）。结论该分析表明，PD患者的特征是那些通常用于精细控制身体节段运动的肌肉的募集减少，同时主要参与运动的肌肉募集增加。肌纤维募集的实质性改变与帕金森病无关。这些发现表明，与HC相比，帕金森病患者在行走过程中倾向于采用更简单的肌肉募集策略。

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引用次数: 0

Design of Novel Artificial Anal Sphincter 新型人工肛门括约肌的设计

IF 4.8 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2023-08-18 DOI: 10.1016/j.irbm.2023.100796

MingHui Wang , YuCheng Liao , YunLong Liu , BoLin Liu , HongLiu Yu

Objectives

Artificial anal sphincter is considered to be a good method for the treatment of severe fecal incontinence, but there are problems of low biomechanical compatibility in clinical application. The purpose of this study is to design a novel artificial anal sphincter that can output stable loading force to solve the mechanical mismatch between artificial anal sphincter and intestinal tissue caused by excessive or too small local pressure on the intestine.

Material and methods

Aiming at the shortcomings of the existing artificial anal sphincter, a novel artificial anal sphincter with constant force mechanism is designed based on the C-shaped SMA sheet of Ti-55.9at%Ni and combined with the normal defecation mechanism of human body. In this paper, the chord length l of C-shaped SMA sheet is used as the design variable, and the artificial anal sphincter is optimized and verified initially by using finite element analysis method.

Results

The results show that the artificial anal sphincter can achieve a constant force to clamp the intestine in a large displacement range when the chord length l of the C-shaped SMA is 13 mm, the chord height h is 1.5 mm, the width w is 2 mm, and the thickness δ is 0.2 mm.

Conclusion

The design of artificial anal sphincter with constant force loading mechanism has a good effect in solving the biomechanical compatibility problem between the implant device and the intestinal tissue. In addition, the designed artificial anal sphincter occupies a small space, which provides a new idea for future clinical application.

目的人工肛门括约肌被认为是治疗严重大便失禁的好方法，但在临床应用中存在生物力学兼容性低的问题。本研究的目的是设计一种能够输出稳定加载力的新型人工肛门括约肌，以解决由于肠道局部压力过大或过小而导致的人工肛门括约肌与肠道组织之间的机械失配问题。材料与方法针对现有人工肛门括约肌的不足，以Ti-55.9at%Ni的C形SMA片为基础，结合人体正常排便机制，设计了一种新型的恒力机构人工肛门括约肌。本文以C形SMA板的弦长l为设计变量，采用有限元分析方法对人工肛门括约肌进行了初步优化验证。结果当C形SMA的弦长l为13mm、弦高h为1.5mm、宽度w为2mm时，结论采用恒力加载机制设计的人工肛门括约肌，在解决植入装置与肠道组织的生物力学相容性问题方面有较好的效果。此外，设计的人工肛门括约肌占地面积小，为未来的临床应用提供了新的思路。

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引用次数: 0

Effect of Local Network Characteristics on the Performance of the SSVEP Brain-Computer Interface 局域网络特性对SSVEP脑机接口性能的影响

IF 4.8 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2023-08-01 DOI: 10.1016/j.irbm.2023.100781

Pengfei Ma , Chaoyi Dong , Ruijing Lin , Shuang Ma , Huanzi Liu , Dongyang Lei , Xiaoyan Chen

Objective

For decades, a great deal of interest in investigating brain network functional connective features has arisen in brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs). Traditional decoding algorithms, for example, canonical correlation analysis (CCA), only consider the inherent properties of each channel in terms of feature extraction for the single channel electroencephalogram (EEG) signal, with inadequate features that cannot fully utilize the information transmitted by the brain.

Material and methods

This paper proposes a fused feature extraction method, CCA-DTF, which combines CCA with a direct transfer function (DTF) to construct local brain network features with seven leads in the occipital region. First, the features extracted by the CCA algorithm were combined with these features extracted by DTF to analyze the EEG data from 20 subjects. Then, two methods, support vector machine (SVM) and random forest (RF), were used in constructing the classifiers for the four tasks classification of the SSVEP-BCI.

Results

The experimental results showed that incorporating local network features (extracted from DTF or Pearson correlation coefficient) can effectively improve the classification average accuracy and the information transfer rate (ITR) of SSVEP, not only for SVM but also for the ensemble method RF. In particular, CCA-DTF plus SVM obtained a 94.52% classification average accuracy and a 49.23 bits/min ITR in a time window of 2 seconds. The performance was 5.57% and 8.01 bits/min higher than those of traditional CCA plus SVM, respectively.

Conclusion

The proposed feature extraction method based on local network features is robust for improving the performance of SSVEP-BCI significantly, which has a perspective of being used in neural rehabilitation engineering field.

几十年来，基于稳态视觉诱发电位（SSVEP）的脑机接口（BCI）对研究脑网络功能连接特征产生了极大的兴趣。传统的解码算法，例如典型相关分析（CCA），在单通道脑电图（EEG）信号的特征提取方面只考虑每个通道的固有特性，而不充分的特征不能充分利用大脑传输的信息。材料和方法本文提出了一种融合特征提取方法CCA-DTF，该方法将CCA-DTF与直接传递函数（DTF）相结合，构建枕部七导联的局部脑网络特征。首先，将CCA算法提取的特征与DTF提取的特征相结合，对20名受试者的脑电图数据进行分析。然后分别采用支持向量机（SVM）和随机森林（RF）两种方法，结果实验结果表明，结合局部网络特征（从DTF或Pearson相关系数中提取）可以有效地提高SSVEP的分类平均精度和信息传递率（ITR），不仅对于SVM，而且对于集成方法RF。特别地，CCA-DTF加SVM在2秒的时间窗口内获得了94.52%的分类平均准确度和49.23比特/分钟的ITR。与传统的CCA加SVM相比，性能分别提高了5.57%和8.01比特/分钟。结论所提出的基于局部网络特征的特征提取方法具有较强的鲁棒性，可以显著提高SSVEP-BCI的性能，具有在神经康复工程领域应用的前景。

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引用次数: 1

On the Utility of Parents' Historical Data to Investigate the Causes of Autism Spectrum Disorder: A Data Mining-Based Framework 父母历史数据在自闭症谱系障碍病因调查中的应用:基于数据挖掘的框架

IF 4.8 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2023-08-01 DOI: 10.1016/j.irbm.2023.100780

Zahid Halim , Gohar Khan , Babar Shah , Rabia Naseer , Sajid Anwar , Ahsan Shah

Objective

Autism Spectrum Disorder (ASD) is acknowledged as a challenge that influences the learning ability of adolescents and also negatively impacts their families. Autism may be caused due to environmental exposure or genetically inherited disorder, however, no definitive or universally customary reasons are known. This makes the issue fairly challenging.

Material and methods

This work focuses on identifying the reasons of ASD utilizing computational methods. For this, data is collected that focuses on parental history for finding the trigged features by reviewing antenatal, perinatal, and infant hazard factors of ASD. Afterwards, ML techniques are applied on the collected instances to develop a predictive model and identify the reasons to ASD. While collecting the data, samples are obtained for ASD and non-ASD individuals both. A total of 115 features are obtained from each subject. The collected dataset has 47% samples of the subjects with ASD. Dimensionality reduction, and four feature selection methods are applied on the data to eliminate noise and least valued features. The data is verified using two clustering techniques, i.e., k-means and k-medoid. To validate the clustering results five clustering validation indices are used. Later, three classifiers, i.e. k-nearest neighbor (k-NN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) are trained to predict cases with ASD. The frequent items mining technique and the descriptive analysis of the clustered data are utilized to identify the factors that may cause ASD.

Results

The proposed framework enables to identify the features that may contribute towards ASD. Whereas, for the classification part, SVM classifier performs better than others do with an average accuracy of 98.34% in predicting the ASD cases.

Conclusion

The results identified stress as the dominant feature and environmental factors, like frequent use of canned food and plastic/steel bottles during fertilization period that may contribute towards ASD.

自闭症谱系障碍（ASD）被认为是一个影响青少年学习能力的挑战，也会对他们的家庭产生负面影响。自闭症可能是由环境暴露或遗传性疾病引起的，但目前还不知道确切的或普遍习惯的原因。这使得这个问题相当具有挑战性。材料和方法本工作的重点是利用计算方法确定ASD的原因。为此，收集的数据侧重于父母病史，通过回顾ASD的产前、围产期和婴儿危险因素来寻找触发特征。然后，将ML技术应用于收集的实例，以开发预测模型并确定ASD的原因。在收集数据的同时，获得了ASD和非ASD个体的样本。每个受试者总共获得115个特征。收集的数据集有47%的ASD受试者样本。对数据应用降维和四种特征选择方法来消除噪声和最小值特征。使用两种聚类技术验证数据，即k-means和k-medoid。为了验证聚类结果，使用了五个聚类验证指数。随后，训练三个分类器，即k近邻（k-NN）、支持向量机（SVM）和人工神经网络（ANN）来预测ASD病例。利用频繁项挖掘技术和聚类数据的描述性分析来识别可能导致ASD的因素。结果所提出的框架能够识别可能导致自闭症的特征。然而，在分类部分，SVM分类器在预测ASD病例方面比其他分类器表现更好，平均准确率为98.34%。结论压力是ASD的主要特征，环境因素，如受精期频繁使用罐头食品和塑料/钢瓶，可能导致ASD。

{"title":"On the Utility of Parents' Historical Data to Investigate the Causes of Autism Spectrum Disorder: A Data Mining-Based Framework","authors":"Zahid Halim , Gohar Khan , Babar Shah , Rabia Naseer , Sajid Anwar , Ahsan Shah","doi":"10.1016/j.irbm.2023.100780","DOIUrl":"https://doi.org/10.1016/j.irbm.2023.100780","url":null,"abstract":"<div><h3>Objective</h3><p>Autism Spectrum Disorder (ASD) is acknowledged as a challenge that influences the learning ability of adolescents and also negatively impacts their families. Autism may be caused due to environmental exposure or genetically inherited disorder, however, no definitive or universally customary reasons are known. This makes the issue fairly challenging.</p></div><div><h3>Material and methods</h3><p><span>This work focuses on identifying the reasons of ASD utilizing computational methods. For this, data is collected that focuses on parental history for finding the trigged features by reviewing antenatal, perinatal, and infant hazard factors of ASD. Afterwards, ML techniques are applied on the collected instances to develop a predictive model and identify the reasons to ASD. While collecting the data, samples are obtained for ASD and non-ASD individuals both. A total of 115 features are obtained from each subject. The collected dataset has 47% samples of the subjects with ASD. Dimensionality reduction, and four feature selection methods are applied on the data to eliminate noise and least valued features. The data is verified using two clustering techniques, i.e., </span><em>k</em>-means and <em>k</em>-medoid. To validate the clustering results five clustering validation indices are used. Later, three classifiers, i.e. <em>k</em>-nearest neighbor (<em>k</em><span><span>-NN), Support Vector Machine (SVM), and </span>Artificial Neural Network (ANN) are trained to predict cases with ASD. The frequent items mining technique and the descriptive analysis of the clustered data are utilized to identify the factors that may cause ASD.</span></p></div><div><h3>Results</h3><p>The proposed framework enables to identify the features that may contribute towards ASD. Whereas, for the classification part, SVM classifier performs better than others do with an average accuracy of 98.34% in predicting the ASD cases.</p></div><div><h3>Conclusion</h3><p><span>The results identified stress as the dominant feature and environmental factors, like frequent use of canned food and plastic/steel bottles during </span>fertilization period that may contribute towards ASD.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"44 4","pages":"Article 100780"},"PeriodicalIF":4.8,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49700778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The Effect of Mesh Orientation, Defect Location and Size on the Biomechanical Compatibility of Hernia Mesh 补片方向、缺损位置和尺寸对疝补片生物力学相容性的影响

IF 4.8 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2023-08-01 DOI: 10.1016/j.irbm.2023.100777

Wei He , Fei Shen , Zhiwei Xu , Baoqing Pei , Huiqi Xie , Xiaoming Li

Objectives

Satisfactory biomechanical compatibility of implants is important for obtaining desired tissue repair efficiency. Here, we investigated the combined effects of three important influencing factors, mesh orientation, defect location and size, on biomechanical compatibility of a typical anisotropic mesh by both computational simulation and animal experiment.

Methods

Numerical models of rabbits were developed based on CT images and material constitutive models obtained by uniaxial tests, during which two orientations, two defect locations and two defect sizes were investigated. Corresponding pneumoperitoneum tests on rabbits and non-invasive measurements on the displacement of abdominal wall surface were performed for validation.

Results

Numerical results showed that the displacement of abdominal wall was limited when the stiffest direction of mesh was parallel to the cranio-caudal direction, but the stress in suture area was greatly reduced. When the defect was located at the junction of different muscles, the strain distribution became uneven. In addition, for the defects with smaller size, difference between the results caused by different mesh orientations was smaller. Animal experimental results were in good agreement with the numerical results. Further simulations for a hypothetical mesh orientation showed that the meshes exhibited better biomechanical compatibility when their stiffest direction was consistent with that of oblique muscles for all four different defects.

Conclusion

The mesh orientation was the most influential factor and the proper orientation of the mesh was not necessarily consistent with the anisotropy of the defect tissue. In addition, the mesh design with asymmetric stiffness should be considered for defects at the junction of different tissues. Finally, it is possible to align the stiffest direction of the mesh with that of the defect tissue in repairing small defects to achieve better compliance. Our findings could provide some reliable and instructive guidelines for high-performance anisotropic meshes development and their appropriate selection and placement in surgery. And methods proposed in this study could be used to comprehensively and instructively evaluate the biomechanical compatibility of hernia meshes, predict their repair effect, and determine their appropriate positioning before they are put into clinical use.

目的种植体良好的生物力学相容性对于获得所需的组织修复效率至关重要。在这里，我们通过计算模拟和动物实验研究了网格方向、缺陷位置和尺寸三个重要影响因素对典型各向异性网格生物力学兼容性的综合影响。方法根据CT图像和单轴试验获得的材料本构模型建立兔的数值模型，研究两个方向、两个缺陷位置和两个缺陷尺寸。对兔子进行了相应的气腹试验，并对腹壁表面的位移进行了无创测量以进行验证。结果数值结果表明，当网状物的最硬方向与头尾方向平行时，腹壁的位移受到限制，但缝合区的应力大大降低。当缺陷位于不同肌肉的交界处时，应变分布变得不均匀。此外，对于尺寸较小的缺陷，不同网格方向引起的结果之间的差异较小。动物实验结果与数值结果吻合较好。对假设网格方向的进一步模拟表明，对于所有四种不同的缺陷，当网格的最硬方向与斜肌的方向一致时，网格表现出更好的生物力学兼容性。结论网状物的取向是影响缺损组织各向异性的主要因素，网状物的正确取向与缺损组织的各向异性不一定一致。此外，对于不同组织交界处的缺陷，应考虑具有不对称刚度的网格设计。最后，在修复小缺陷时，可以将网状物的最硬方向与缺陷组织的方向对齐，以实现更好的顺应性。我们的发现可以为高性能各向异性网格的开发及其在外科手术中的适当选择和放置提供一些可靠和有指导意义的指导。本研究提出的方法可用于全面、指导性地评估疝环网片的生物力学相容性，预测其修复效果，并在投入临床使用前确定其合适的位置。

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引用次数: 0

BCDNet: An Optimized Deep Network for Ultrasound Breast Cancer Detection BCDNet:一个优化的超声乳腺癌检测深度网络

IF 4.8 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2023-08-01 DOI: 10.1016/j.irbm.2023.100774

S.-Y. Lu , S.-H. Wang , Y.-D. Zhang

Objectives

Breast cancer is a common but deadly disease among women. Medical imaging is an effective method to diagnose breast cancer, but manual image screening is time-consuming. In this study, a novel computer-aided diagnosis system for breast cancer detection called BCDNet is proposed.

Material and Methods

We leverage pre-trained convolutional neural networks (CNNs) for representation learning and propose an adaptive backbone selection algorithm to obtain the best CNN model. An extreme learning machine serves as the classifier in the BCDNet, and a bat algorithm with chaotic maps is put forward to further optimize the parameters in the classifiers. A public ultrasound image dataset is used in the experiments based on 5-fold cross-validation.

Results

Simulation results suggest that our BCDNet outperforms several state-of-the-art breast cancer detection methods in terms of accuracy.

Conclusion

The proposed BCDNet is a useful auxiliary tool that can be applied in clinical screening for breast cancer.

目的：癌症是一种常见但致命的女性疾病。医学影像学是诊断癌症的有效方法，但人工影像筛查耗时。本研究提出了一种新型的乳腺癌症计算机辅助诊断系统BCDNet。材料和方法我们利用预先训练的卷积神经网络（CNNs）进行表示学习，并提出了一种自适应骨干选择算法来获得最佳的CNN模型。BCDNet中使用了一个极限学习机作为分类器，并提出了一种带有混沌映射的bat算法来进一步优化分类器中的参数。实验中使用了基于5倍交叉验证的公共超声图像数据集。结果仿真结果表明，我们的BCDNet在准确性方面优于几种最先进的癌症检测方法。结论BCDNet是一种可用于癌症临床筛查的辅助工具。

引用次数: 1

The Impact of Missing Data on Heart Rate Variability Features: A Comparative Study of Interpolation Methods for Ambulatory Health Monitoring 缺失数据对心率变异性特征的影响:动态健康监测插值方法的比较研究

IF 4.8 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2023-08-01 DOI: 10.1016/j.irbm.2023.100776

Mouna Benchekroun , Baptiste Chevallier , Vincent Zalc , Dan Istrate , Dominique Lenne , Nicolas Vera

Objectives

Heart rate variability (HRV) is a valuable indicator of both physiological and psychological states. However, the accuracy of HRV measurements taken by wearable devices can be compromised by errors during transmission and acquisition. These errors can significantly affect HRV features and are not acceptable for precise HRV analysis used for medical diagnosis. This study aims to address this issue by investigating the effectiveness of four different interpolation methods (Nearest Neighbour - NN, Linear, Shape-preserving piecewise cubic Hermite - Pchip, and cubic spline) in tackling missing RR values in real-time HRV analysis.

Materials and Methods

In this study, HRV signals were obtained from Electrocardiograms (ECG) through automatic detection and manually corrected by a specialist, resulting in high-quality signals with no missing or ectopic peaks. To simulate low-quality data acquisition, values were iteratively deleted from each HRV analysis window. The deleted values were then replaced using four different interpolation methods. Time and frequency domain features were computed from both the original and reconstructed signals, and the Mean Absolute Percentage Error (MAPE) was used to compare these features.

Results

Results showed that as the percentage of missing values increased, some interpolation methods were more suitable for RR time-series with a greater number of missing data. Furthermore, the study suggests that the impact of interpolation on HRV features varied across different features and that SDNN is the least affected by interpolation. In the time domain, nearest neighbour interpolation gives the best results for up to 50% missing data. Beyond this threshold, it seems better not to use any interpolation for RMSSD. In the frequency domain however, the lowest errors of HRV feature estimation are obtained using linear or Pchip interpolation. To achieve maximum performance, it is recommended to adapt the interpolation method to both the percentage of missing values and the targeted HRV feature.

Conclusion

Results highlight the importance of choosing the appropriate interpolation method to accurately estimate HRV features in real-time analysis. Overall, the Pchip interpolation seems to yield the best results on most HRV features as it preserves the linear trend of the data while adding very light waves. The findings can be beneficial in the development of more precise and reliable wearable devices for real-time HRV monitoring.

目的心率变异性（HRV）是反映生理和心理状态的重要指标。然而，可穿戴设备进行的HRV测量的准确性可能会因传输和采集过程中的错误而受到影响。这些误差会显著影响HRV特征，并且对于用于医学诊断的精确HRV分析是不可接受的。本研究旨在通过研究四种不同插值方法（最近邻NN、线性、保形分段三次Hermite-Pchip和三次样条）在实时HRV分析中处理缺失RR值的有效性来解决这一问题。材料和方法在本研究中，通过自动检测和专家手动校正，从心电图中获得HRV信号，得到高质量的信号，没有缺失或异位峰值。为了模拟低质量数据采集，从每个HRV分析窗口中反复删除值。然后使用四种不同的插值方法替换删除的值。根据原始信号和重建信号计算时域和频域特征，并使用平均绝对百分比误差（MAPE）来比较这些特征。结果随着缺失值百分比的增加，某些插值方法更适合于缺失数据较多的RR时间序列。此外，该研究表明，插值对HRV特征的影响因不同特征而异，SDNN受插值的影响最小。在时域中，对于高达50%的缺失数据，最近邻插值给出了最佳结果。超过这个阈值，似乎最好不要对RMSSD使用任何插值。然而，在频域中，使用线性或Pchip插值来获得HRV特征估计的最低误差。为了实现最大性能，建议根据缺失值的百分比和目标HRV特征调整插值方法。结论结果突出了在实时分析中选择合适的插值方法来准确估计HRV特征的重要性。总的来说，Pchip插值似乎在大多数HRV特征上产生了最好的结果，因为它在添加非常光波的同时保持了数据的线性趋势。这些发现有利于开发更精确、更可靠的可穿戴设备，用于实时HRV监测。

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引用次数: 1

MCFA-UNet: Multiscale Cascaded Feature Attention U-Net for Liver Segmentation MCFA-UNet:用于肝脏分割的多尺度级联特征关注U-Net

IF 4.8 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2023-08-01 DOI: 10.1016/j.irbm.2023.100789

Yuran Zhou , Qianqian Kong , Yan Zhu , Zhen Su

Objectives

Accurate automatic liver segmentation has important value for subsequent tumor segmentation, diagnosis, and treatment. In this paper, a Multiscale Cascaded Feature Attention U-Net (MCFA-UNet) neural network model was proposed to solve the problem of edge detail feature loss caused by insufficient feature extraction in existing segmentation methods.

Material and methods

MCFA-UNet is a 3D segmentation network based on U-Net encoding and decoding structure. First, this paper proposes a multiscale feature cascaded attention (MCFA) module, which extracts multiscale feature information through multiple continuous convolution paths, and uses double attention to realize multiscale feature information fusion of different paths. Second, the attention-gate mechanism is used to fuse different levels of feature information, which reduces the semantic difference between coding and decoding paths. Finally, the deep supervision learning method was employed to optimize the network segmentation effect through the feature information of each hidden layer in the decoding path.

Results

MCFA-UNet was evaluated on LiTS and 3DIRCADb datasets. The Dice scores of 0.955 and 0.981 are obtained respectively. Compared with the baseline network, the segmentation accuracy is improved by 5% and 3.5%.

Conclusion

Experimental results show that MCFA-UNet has more accurate segmentation performance than baseline model and other advanced methods.

目的准确的肝脏自动分割对后续的肿瘤分割、诊断和治疗具有重要价值。针对现有分割方法中特征提取不足导致边缘细节特征丢失的问题，提出了一种多尺度级联特征注意力U-Net（MCFA-UNet）神经网络模型。材料和方法CFA-UNet是一种基于U-Net编解码结构的三维分割网络。首先，本文提出了一种多尺度特征级联注意力（MCFA）模块，该模块通过多条连续卷积路径提取多尺度特征信息，并利用双重注意力实现不同路径的多尺度特征信息融合。其次，注意力门机制用于融合不同级别的特征信息，减少了编码和解码路径之间的语义差异。最后，采用深度监督学习方法，通过解码路径中每个隐藏层的特征信息来优化网络分割效果。结果在LiTS和3DIRCADb数据集上对MCFA-UNet进行了评价。骰子得分分别为0.955和0.981。与基线网络相比，分割准确率分别提高了5%和3.5%。结论实验结果表明，MCFA-UNet比基线模型和其他先进方法具有更准确的分割性能。

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引用次数: 0

Bioactivation of New Harmonic Titanium Alloy to Improve and Control Cellular Response and Differentiation 新型谐波钛合金生物活化改善和控制细胞反应和分化

IF 4.8 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2023-08-01 DOI: 10.1016/j.irbm.2023.100771

A. Rangel , M. Lam , A. Hocini , V. Humblot , K. Ameyama , V. Migonney , G. Dirras , C. Falentin-Daudre

Objective

The purpose of this research article is to present the functionalization of a new titanium alloy of the system TiNbZr, by the grafting of a bioactive polymer (poly(sodium styrene sulfonate), PNaSS) using the “grafting from” technique to improve the osseointegration. The resulting grafted polymer is covalently bonded to the substrate in this procedure thanks to surface-induced polymerization.

Material and Method

Colorimetric assay, Fourier-transform infrared spectra recorded in attenuated total reflection mode (ATR-FTIR), Scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and water contact angle measurements (WCA) were applied to characterize the surfaces. In addition, the effect of the grafting on the biological response was assessed using MC3T3-E1 pre-osteoblast cells line.

Results

This study showed that grafting rates obtained on these new alloy are as good (around 4.5 μg/cm²) as on classical alloys. In parallel, in vitro biological response study was carried out to assess toxicity, cell viability, and morphology on titanium alloys TiNbZr functionalized. Moreover, results showed superior alkaline phosphatase activity and higher calcium deposition on grafted samples, implying a beneficial effect of the PNaSS in osteoinduction activity.

Conclusions

Grafted TiNbZr improves the cell response, in particular, the osseointegration.

目的本文的目的是介绍一种新的TiNbZr系钛合金的功能化，通过使用“接枝自”技术接枝生物活性聚合物（聚苯乙烯磺酸钠，PNaSS）来改善骨整合。在该过程中，由于表面诱导的聚合作用，所得接枝聚合物共价结合到基底上。材料和方法采用比色法、衰减全反射模式下记录的傅立叶变换红外光谱（ATR-FTIR）、扫描电子显微镜（SEM）、X射线光电子能谱（XPS）和水接触角测量（WCA）对表面进行表征。此外，使用MC3T3-E1前成骨细胞系评估移植对生物反应的影响。结果该合金的接枝率与传统合金相当（约4.5μg/cm2）。同时，进行了体外生物反应研究，以评估TiNbZr功能化钛合金的毒性、细胞活力和形态。此外，结果显示，在移植的样品上具有优异的碱性磷酸酶活性和较高的钙沉积，这意味着PNaSS在骨诱导活性中具有有益的作用。结论TiNbZr的移植可改善细胞反应，尤其是骨整合。

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