Irbm最新文献

{"title":"Device Attitude and Real-Time 3D Visualization: An Interface for Elderly Care","authors":"M. Abbas ,&nbsp;M. Saleh ,&nbsp;J. Prud'Homm ,&nbsp;F. Lemoine ,&nbsp;D. Somme ,&nbsp;R. Le Bouquin Jeannès","doi":"10.1016/j.irbm.2022.100746","DOIUrl":"https://doi.org/10.1016/j.irbm.2022.100746","url":null,"abstract":"<div><h3>Objective</h3><p>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.</p></div><div><h3>Material and methods</h3><p><span><span>based on inertial measurement units (IMU) or on magnetic, angular rate and gravity (MARG) sensors, a processing unit provides </span>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, </span><em>i.e.</em> IMUs and MARGs, as well as two orientation filters, namely Madgwick's algorithm and Mahony's algorithm.</p></div><div><h3>Results</h3><p>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.</p></div><div><h3>Conclusion</h3><p>this paper addresses the different aspects and discusses the limitations of attitude estimation systems, which are important tools to help clinicians in their diagnosis.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49886754","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}

{"title":"Deep Learning-Based Metaheuristic Weighted K-Nearest Neighbor Algorithm for the Severity Classification of Breast Cancer","authors":"S.R. Sannasi Chakravarthy ,&nbsp;N. Bharanidharan ,&nbsp;H. Rajaguru","doi":"10.1016/j.irbm.2022.100749","DOIUrl":"https://doi.org/10.1016/j.irbm.2022.100749","url":null,"abstract":"<div><h3>Objective</h3><p><span>The most widespread and intrusive cancer type<span> 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 </span></span>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.</p></div><div><h3>Material and Methods</h3><p><span>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 </span>transfer learning<span> 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.</span></p></div><div><h3>Results</h3><p>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.</p></div><div><h3>Conclusion</h3><p>The obtained results reveal that the proposed Computer-Aided-Diagnosis (CAD) tool is robust for the severity classification of breast cancer.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49886765","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}

{"title":"Optimal Sensor Placement in Smart Home Using Building Information Modeling: A Home Support Application","authors":"R. Ben Bachouch,&nbsp;Y. Fousseret,&nbsp;Y. Parmantier","doi":"10.1016/j.irbm.2022.100745","DOIUrl":"https://doi.org/10.1016/j.irbm.2022.100745","url":null,"abstract":"<div><h3>Objectives</h3><p><span>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 </span>building layout.</p></div><div><h3>Material and methods</h3><p>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.</p></div><div><h3>Results</h3><p>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.</p></div><div><h3>Conclusion</h3><p>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.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49886763","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}

{"title":"An Image Recognition Method for Urine Sediment Based on Semi-supervised Learning","authors":"Q. Ji ,&nbsp;Y. Jiang ,&nbsp;Z. Wu ,&nbsp;Q. Liu ,&nbsp;L. Qu","doi":"10.1016/j.irbm.2022.09.006","DOIUrl":"https://doi.org/10.1016/j.irbm.2022.09.006","url":null,"abstract":"<div><h3>Objectives</h3><p><span>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 </span>image classification method based on semi-supervised learning.</p></div><div><h3>Methods</h3><p>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.</p></div><div><h3>Results</h3><p>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.</p></div><div><h3>Conclusion</h3><p>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.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49700162","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}

{"title":"Evaluation of a Wireless Home Sleep Monitoring System Compared to Polysomnography","authors":"Q. Pan,&nbsp;D. Brulin,&nbsp;E. Campo","doi":"10.1016/j.irbm.2022.09.002","DOIUrl":"https://doi.org/10.1016/j.irbm.2022.09.002","url":null,"abstract":"<div><h3>Objective</h3><p>Sleep is essential for human health<span>. 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.</span></p></div><div><h3>Method</h3><p><span>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 </span>Periodic Limb Movement during Sleep.</p></div><div><h3>Results</h3><p><span>Based on the confusion matrix<span> 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 </span></span>hypnogram and between body temperature on extremities and PLMS.</p></div><div><h3>Conclusion</h3><p>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.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49701286","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}

{"title":"MonEco: a Novel Health Monitoring Ecosystem to Predict Respiratory and Cardiovascular Disorders","authors":"Remo Lazazzera,&nbsp;Guy Carrault","doi":"10.1016/j.irbm.2022.09.003","DOIUrl":"https://doi.org/10.1016/j.irbm.2022.09.003","url":null,"abstract":"<div><p>Objectives: the present manuscript introduces a health monitoring ecosystem called MonEco, to monitor and predict respiratory and cardiovascular disorders.</p><p>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.</p><p>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.</p><p>Conclusion: MonEco wants to inspire and disclose the architecture of a connected health monitoring ecosystem.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49700317","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}

{"title":"Therapeutic Ultrasound Applications in Cardiovascular Diseases: A Review","authors":"G. Ditac ,&nbsp;F. Bessière ,&nbsp;C. Lafon","doi":"10.1016/j.irbm.2022.07.001","DOIUrl":"https://doi.org/10.1016/j.irbm.2022.07.001","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49727818","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}

{"title":"Grouping Intrinsic Mode Functions and Residue for Pathological Classifications via Electroglottograms","authors":"G. Liao,&nbsp;B.W.-K. Ling,&nbsp;K.-G. Pang","doi":"10.1016/j.irbm.2022.11.001","DOIUrl":"https://doi.org/10.1016/j.irbm.2022.11.001","url":null,"abstract":"<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","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49700166","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}

{"title":"Development of a Wearable Framework for the Assessment of a Mechanical-Based Indicator of Falling Risk in the Field","authors":"H. Pillet ,&nbsp;B. Watier","doi":"10.1016/j.irbm.2022.11.003","DOIUrl":"https://doi.org/10.1016/j.irbm.2022.11.003","url":null,"abstract":"<div><h3>Objectives</h3><p>The characterization of the instability of gait is a current challenge of biomechanics. Indeed, risks of falling naturally result from the difficulty to control perturbations of the locomotion pattern. Hence, the assessment of a synthetic parameter able to quantify the instability in real time will be useful for the prevention of falls occurring in this context. Thus, the objective of the present study, in two steps, was to propose and evaluate a relevant parameter to quantify the risk of fallings.</p></div><div><h3>Material and Methods</h3><p>Experimental analysis of the gait of 11 able-bodied subjects from a motion capture system in laboratory condition was performed. The distance of the Body Center of Mass (BCOM) to the Minimal Moment Axis (MMA) was computed as a proxy of whole-body angular momentum<span> variations. In a second step, we quantified the kinematics during gait with wearable Inertial Measurement Units (IMU) fixed on two individuals (one able bodied person and one person with transfemoral amputation). We compared the IMU-based BCOM kinematics with a motion capture reference system to verify the accuracy of our measures in the field.</span></p></div><div><h3>Results</h3><p>Normative thresholds of the distance of the Body Center of Mass (BCOM) to the Minimal Moment Axis (MMA) during able-bodied level walking were assessed. The average error between the BCoM displacement computed from the IMU and from the reference vicon data of 4 mm, 3 mm and 53 mm on the mediolateral, anteroposterior and vertical axes respectively.</p></div><div><h3>Conclusion</h3><p>All these results make it possible to consider the determination of the risks of falls in the field at mid-term. the research on an optimal configuration that maintain the performance while simplifying the device will be essential to make it acceptable by the individuals.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49701290","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}

{"title":"High Intensity Focused Ultrasound (HIFU) in Digestive Diseases: An Overview of Clinical Applications for Liver and Pancreatic Tumors","authors":"A. Dupré ,&nbsp;D. Melodelima ,&nbsp;C. Cilleros ,&nbsp;L. De Crignis ,&nbsp;P. Peyrat ,&nbsp;J. Vincenot ,&nbsp;M. Rivoire","doi":"10.1016/j.irbm.2022.09.005","DOIUrl":"https://doi.org/10.1016/j.irbm.2022.09.005","url":null,"abstract":"<div><p><span>High Intensity Focused Ultrasound (HIFU) is an emerging technology of focal destruction in daily clinical practice. Alternative techniques of focal destruction, such as </span>radiofrequency ablation<span><span><span> (RFA) or more recently irreversible electroporation, have been used in </span>digestive diseases<span> for many years, mainly in hepatobiliary and pancreatic systems. Likewise, HIFU is currently used in the treatment of tumors located in the liver and in the pancreas. HIFU is quite exclusively applied for the treatment of </span></span>malignancies<span><span>, with an extracorporeal<span><span> approach. Treatment of the liver is difficult because presence of the ribcage may stop propagation of ultrasound waves and respiratory motion may cause targeting problems. Pancreatic cancer<span> is also challenging to treat with HIFU because the pancreas is a deep-seated organ surrounded by major vessels. The interposition of bowel gas may significantly obstruct the acoustic window, potentially leading to incomplete tumor ablation and injury of the interposed bowel and/or other intra-abdominal organs. The two main applications of HIFU are the treatment of hepatocellular carcinoma (HCC) and pancreatic cancer. In the management of HCC, HIFU with </span></span>transarterial chemoembolization<span> (TACE) seems to provide a survival advantage compared to TACE alone. HIFU showed similar results when compared to RFA for small tumors. HIFU could be interesting for tumors located in difficult location. For pancreatic cancer, HIFU is mostly used in the palliative setting to treat cancer-related pain. Some publications showed encouraging results about downsizing when HIFU is used in combination with chemotherapy and/or radiotherapy, which could be interesting in locally advanced tumors. This review focused on the clinical applications of HIFU in liver and </span></span></span>pancreatic tumors.</span></span></p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49727819","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}