共查询到20条相似文献，搜索用时 23 毫秒
1.
Background
In developed countries, 10% of labors occur prematurely and are mainly due to contractions appearing too soon during the pregnancy. To detect such contractions, we developed a wearable device able to record both the electrical activity of the uterus, electrohysterograms (EHG), thanks to 18 electrodes, but also the mother movements, thanks to an embedded accelerometer.Methods
In this study, we investigated the detectability of a begin/end of contraction by analyzing EHG signals with the Bayes Information Criterion, and we analyzed the three axis accelerometer signals to characterize the mother activity when she is carrying the device (such movements being possible sources of artifacts in the EHG signals).Results
For the contraction detections, we obtained 68.38% (599/876) of good detection but a too high number of false alarms (1073). To reduce this false alarm number, we analyzed the three accelerometer signals and detected 98.7% of static phases of the mother and 95% of dynamic ones.Discussion
The detection of precise movements inside the dynamic cluster still has to be investigated to improve the first obtained results, as well as the combination of these two research ways (EHG and accelerometer) applied at the same time during recording. 相似文献2.
Background
Label fusion is a core step of Multi-Atlas Segmentation (MAS), which has a decisive effect on segmentation results. Although existed strategies using image intensity or image shape to fuse labels have got acceptable results, there is still necessity for further performance improvement. Here, we propose a new label fusion strategy, which considers the joint information of intensity and registration quality.Methods
The correlation between any two atlases is taken into account and the probability that two atlases both give wrong label is used to compute the fusion weights. The probability is jointly determined by the registration error and intensity similarity of the two corresponding atlas-target image pairs. The proposed label fusion algorithm is named Registration Error and Intensity Similarity based Label Fusion (REIS-LF).Results
Using 3D Magnetic Resonance (MR) images, the proposed REIS-LF algorithm is validated in brain structure segmentation including the hippocampus, the thalamus and the nuclei of the basal ganglia. The REIS-LF algorithm has higher segmentation accuracy and robustness than the baseline AQUIRC-W algorithm.Conclusions
Taking the registration quality, the inter-atlas correlations and intensity differences into account in label fusion benefits to improve the object segmentation accuracy and robustness. 相似文献3.
Background
Convenient and precise measurement of the Cobb angle using a small size X-ray detector has been required for local clinics.Methods
Cobb angle measurement system using a conventional X-ray source and detector is proposed for accurate Cobb angle measurement. The system consists of a conventional X-ray source, a ruler-added X-ray table, a conventional X-ray detector, and an image processing program. The X-ray table has the lead ruler patterns. The patterns remain white ruler patterns on X-ray images. The proposed image processing program merges the three spinal X-ray images into one whole spinal X-ray image by detecting the ruler patterns on the three spinal X-ray images.Results
In order to evaluate our program, Cobb angle measured in the merged image is compared with Cobb angle measured in the X-ray image taken by a large X-ray detector. Average of difference between them is 2.251 degree and standard deviation is 1.339.Conclusion
The developed measurement system demonstrated its measurement performance accurately and practically. 相似文献4.
Purpose
As the elder population grows, the need for domestic healthcare is on the rise. Both robotics and smart environments, including smart homes, provide a promising solution to monitor, interact and keep company to users. However, in real case scenarios, sensors data are not perfect and the environment changes over time, leading to erroneous understanding of the context and inappropriate responses. The purpose of this work is to tackle those challenges in order to improve the autonomy and efficiency of robots in smart environments.Methods
The problematic was structured into three steps: (1) perception, (2) cognition and (3) action. We proposed and evaluated a software framework that covers the challenges of each step. It includes respectively: (1) a context acquisition method that supports and models the uncertainty of data by using complex event processing, fuzzy logic and ontologies; (2) an activity recognition system that combines vision, context knowledge and semantic reasoning; (3) a dynamic hierarchical task planner that alternates planning and execution. For each step, the framework was evaluated through simulations and/or experiments using a robot and a smart room.Results
The quality of the perception was assessed by measuring the efficiency of a cognition process using the acquired context knowledge. An uncertain environment was simulated, and results show our framework to enable a gain of 10% of correctness for an activity recognition process. The cognition part of the framework was evaluated by observing several persons performing activities. It achieved an overall 90% correct recognition, yet, such result questions the relevance of our approach. Finally, the action step was confronted a simulated scenario with various levels of dynamism. Our task planner appeared to reduce, by up to 23%, the number of tasks required to reach a goal in a dynamic environment.Conclusion
Our framework provides software tools that make robots and smart environments more relevant in real housings. By supporting the uncertainty of context data and the dynamism of the environment, robots and smart environments can achieve more effectively their purposes in domestic healthcare applications. 相似文献5.
H. Tannous D. Istrate A. Benlarbi-Delai J. Sarrazin M.-C. Ho Ba Tho T.T. Dao 《IRBM》2018,39(6):440-444
Background
Serious games have recently immerged as a good tool for physical rehabilitation. This new technology can be used at home, to complement a traditional, clinic based, rehabilitation program. To implement a serious game at home, we need to use multiple sensors to record patients' data. Many serious games use visual motion capture techniques, like the Kinect camera, due to their low price and high portability. On the other hand, some other systems use inertial sensors to collect data at a higher degree of accuracy. In previous works, we showed that a serious gaming system could benefit from combining data from different sensors. However, the use of inertial sensors, in a home-based setting, remains a challenge since they need to be supplied by an independent battery source, which could influence the acceptability of such systems.Methods
In this paper, we present an energy consumption study, performed on the inertial sensors used in our serious game system.Results
The results show that the sensors are rarely affected by environmental factors. They also show that the sensors can function continuously for about 14 hours without battery recharge.Conclusion
Finally, these results allowed us to establish an optimal set up configuration for home based rehabilitation using serious games. 相似文献6.
Background
Aiming for autonomous living for the people after a stroke is the challenge these days especially for swallowing disorders or dysphagia. The most common cause of dysphagia is stroke. In France, stroke occurs every 4 minutes, which implies 13000 hospitalizations per year. Currently, continuous medical home monitoring of patients is not available. The patient must be hospitalized or visit the medical community for possible follow-up. It is in this context that E-SwallHome (Swallowing & Breathing: Modelling and e-Health at Home) project proposes to develop tools, from hospital care until the patient returns home, which are able to monitor in real time the process of swallowing.Method
This paper presents a relevant health problem affecting patient recovering from stroke. We propose a frequency acoustical analysis for automatic detection of swallowing process and a non-invasive acoustic based method to differentiate between swallowing sounds and other sounds in normal ambient environment during food intake.Result
The proposal algorithm for events detection gives a global rate of good detection of 87.31%. Classification of sounds of swallowing and other sounds based on Gaussian Mixture Models (GMM), using the leave-one-out approach according to the small amount of data in our database, gives a good recognition rate of swallowing sounds of 84.57%.Conclusion
The proposal method has great potential to assist in the clinical evaluation using only swallowing sounds, which is a non-invasive technic for swallowing studies. 相似文献7.
Background
Epileptic seizures are unpredictable in nature and its quick detection is important for immediate treatment of patients. In last few decades researchers have proposed different algorithms for onset and offset detection of seizure using Electroencephalogram (EEG) signals.Methods
In this paper, a combined approach for onset and offset detection is proposed using Triadic wavelet decomposition based features. Standard deviation, variance and higher order moments, extracted as significant features to represent different EEG activities.Classification between seizure and non-seizure EEG was carried out using linear discriminant analysis (LDA) and k-nearest neighbour (KNN) classifiers. The method was tested using two benchmark EEG datasets in the field of seizure detection.CHBMIT EEG dataset was used for evaluating the performance of proposed seizure onset and offset detection method.Further for testing the robustness of the algorithm, the effect of the signal-to-noise ratio on the detection accuracy has been also investigated using Bonn University EEG dataset.Results
The seizure onset and offset detection method yielded classification accuracy, specificity and sensitivity of 99.45%, 99.62% and 98.36% respectively with 6.3 s onset and ?1.17 s offset latency using KNN classifier.The seizure detection method using Bonn University EEG dataset got classification accuracy of 92% when SNR = 5 dB, 94% when SNR = 10 dB, and 96% when SNR = 20 dB, while it also yielded 96% accuracy for noiseless EEG.Conclusion
The present study focuses on detection of seizure onset and offset rather than only seizure detection. The major contribution of this work is that the novel triadic wavelet transform based method is developed for the analysis of EEG signals. The results show improvement over other existing dyadic wavelet based Triadic techniques. 相似文献8.
A. Jabeen B. Reeder D. Svistunenko S. Hisaindee S. Ashraf S. Al-Zuhair S. Battah 《IRBM》2019,40(1):51-61
Background
In recent years, microalgae (MA) have attracted much interest considering their possible therapeutic application. They contain active natural compounds or derivatives (extracts, pure or chemically modified compounds) that have increasing applications in the pharmaceutical industry.Methods
The present study aims to examine microalgae for new photosensitizers, with a potential to be used in the light-associated treatment of tumors. Semi-purified extracts of several microalgae strains were evaluated as photosensitizers for photodynamic therapy (PDT) applications. Four tumor cell lines (A549, LNCap, MCF-7, and MDA-MB 435) were used to assess 34 samples extracted by three methods: cellulase enzyme, lysozyme enzyme and ultra-sonication. The fluorescence measurements and the recorded images alongside the spectral intensities between 650–800 nm wavelengths provided characteristic features to some of the contents of the examined extracts.Results
Several microalgae constituents activated by blue light (BL), red light (RL) or both (in sequence) exhibited significant effects on the viability of the tumor cell lines, decreasing it as much as 95% for certain MA constituents. Majority of the MA constituents showed a higher phototoxicity after exposure to both blue and red lights than the photo-induced toxicity when exposed to a single light source. The viability of the tumor cells exhibited the dose dependent response with the MA constituents.Conclusion
The results clearly showed that MA constituents are potential photosensitizers that have a significant photo-damage effects on the tested cancer cells. 相似文献9.
Background
Fatty Liver Disease (FLD) is one of the most critical diseases that should be detected and cured at the earlier stage in order to decrease the mortality rate. To identify the FLD, ultrasound images have been widely used by the radiologists. However, due to poor quality of ultrasound images, they found difficulties in recognizing FLD. To resolve this problem, many researchers have developed various Computer Aided Diagnosis (CAD) systems for the classification of fatty and normal liver ultrasound images. However, the performance of existing CAD systems is not good in terms of sensitivity while classifying the FLD.Methods
In this paper, an attempt has been made to present a CAD system for the classification of liver ultrasound images. For this purpose, texture features are extracted by using seven different texture models to represent the texture of Region of Interest (ROI). Highly discriminating features are selected by using Mutual Information (MI) feature selection method.Results
Extensive experiments have been carried out with four different classifiers, and for carrying out this study, 90 liver ultrasound images have been taken. From the experimental results, it has been found that the proposed CAD system is able to give 95.55% accuracy and sensitivity of 97.77% with the 20 best features selected by the MI feature selection technique.Conclusion
The experimental results show that the proposed system can be used for the classification of fatty and normal liver ultrasound images with higher accuracy. 相似文献10.
Background
Living alone can be tough and risky for the elderly, typically, a fall can have serious consequences for them. Consequently, smart homes are becoming more and more popular. Such sensors-enriched environments can be exploited for health-care applications, in particular, Anomaly Detection (AD). Currently, most AD solutions only focus on detecting anomalies in the user daily activities while omitting the ones coming from the environment itself. However, it appears that serious anomalies can be caused by the environment during the user activity such as getting sick during sleeping when it is cold and the window is open.Methods
In order to consider environmental context with user activities, in this paper, we present a novel approach for detecting anomalous situations occurring in the smart home environment. To that end, we propose as a first step, an activity recognition method based on an hybridization of a knowledge-based technique, taking full advantage of the semantic representation and the reasoning properties of ontologies and a data driven technique based on Dempster Shafer theory. In the second step, given the recognized activity and its surrounding context, we propose an approach that is able to built situations and detect anomalies with a level of uncertainty.Results
Our system is implemented, tested and evaluated using real data obtained from the Hadaptic platform1 and opportunity dataset. The former dataset is used to evaluate the detection of anomalies and the latter is for the recognition of activities. Experimental results prove that with suitable time window size, the activity recognizer and the anomaly detector are efficient having respectively 91% of recognition rate and 100% of precision.Conclusion
Our method allows, on one hand, recognizing user activities and, on the other hand, detecting eventual occurrence of anomalies in the user's situation. It proves to be efficient using the tested datasets for each module. However, in order to obtain a more general conclusion we plan to evaluate the method using more different datasets. 相似文献11.
12.
JC. Brisset F. Gazeau C. Corot N. Nighoghossian Y. Berthezène E. Canet-Soulas M. Wiart 《IRBM》2018,39(2):93-102
Background
Inflammation is a share process in atherosclerosis and stroke and is thought to be a key player in the evolution of these diseases. Ten years ago, inflammation imaging with magnetic resonance imaging (MRI) was considered very promising for both pre-clinical and clinical studies of atherosclerosis and stroke.Contribution
We report here contributions to the field of inflammation imaging with USPIO-enhanced MRI. The goal was to investigate the life cycle of USPIOs in the body, and how the MRI signal has been impacted during their bio-interactions and bioprocessing. Those mechanisms were applied to pre-clinical longitudinal studies of inflammation in atherosclerosis and at the acute stage of ischemic stroke thus allowing the monitoring of treatment effects.Conclusion
This review presents the contribution of the collaborative research project under the “TecSan” grant from the French Research Agency (ANR) as well as pre-clinical and clinical perspectives of USPIO's inflammation MRI in atherosclerosis and stroke. 相似文献13.
K. Langlois H. Pillet F. Lavaste G. Rochcongar P. Rouch P. Thoreux W. Skalli 《IRBM》2018,39(4):251-260
Background
Several methods can be used to assess joint kinematics going from optoelectronic motion analysis to biplanar fluoroscopy. The aim of the present work was to evaluate the reliability of the use of biplane radiography to quantify the sequential 3D kinematics of the femoro-tibial joint.Methods
Bi-planar X-rays (EOS imaging) of 12 lower limbs (6 specimens in vitro and 6 subjects in vivo) were taken for various knee flexion angles. 3D personalized models of the femur and the tibia were registered on each pair of views. To quantify the bias, the kinematic parameters calculated from the registered models were compared to those obtained from the tripods embedded in the specimens. Intra and inter-operator repeatability of each parameter were assessed from the registrations made by 3 operators in vivo.Results
In vitro, the bias of the tibia pose estimation obtained from the registration method was inferior to 1.6 mm and 0.4°. In vivo, the repeatability of the sequential kinematic parameters was inferior to 0.3°, 2.1° and 1.8°, for respectively flexion, varus-valgus and medial-lateral rotation and inferior to 1.8 mm for translations.Conclusion
Compared to simple fluoroscopy, the accuracy of our method based on sequential images was of the same order of magnitude, with better results for the translation in the frontal plane. The low dose of radiation of the EOS system offers promising prospects for a clinical use of this method to assess the femoro-tibial sequential kinematics. 相似文献14.
Objective
Recently, Electroencephalogram (EEG) shows potential in the diagnosis of Alzheimer's disease and other dementia. We aim to investigate whether EEG and selected cognitive biomarkers can classify mild cognitive impairment (MCI), dementia and healthy subjects using support vector machine classifier in Indian cohort.Methods
Eight EEG biomarkers, power spectral density, skewness, kurtosis, spectral skewness, spectral kurtosis, spectral crest factor, spectral entropy (SE), fractal dimension (FD) were analyzed from 44 subjects in four conditions; eye-open, eye-close, finger tapping test (FTT) and continuous performance test (CPT). FFT and CPT are used to measure motor speed and sustained attention as these cognitive biomarkers are free from the educational barrier.Results
We achieved very good accuracy for each event from 73.4% to 89.8% for three binary classes. We investigated that FTT (84% accuracy), CPT (88% accuracy) were the most efficient events to diagnose MCI from dementia. MCI from control successfully diagnosed with 89.8% accuracy in FTT, 73.4% accuracy in CPT and 84.1% accuracy in eye open resting state. Even though cognitive biomarkers were also adequately diagnosed MCI from other groups.Conclusions
Our classifier findings are consistent with the utmost evidence. Yet, our results are promising and especially newfangled in the case of FTT and CPT from the prior studies. We developed an experimental protocol and proposed a novel technique to classify MCI with efficient biomarkers. 相似文献15.
Background
Polycaprolactone (PCL) is a biodegradable polymer which is used in tissue engineering applications thanks to its many favorable characteristics. However, PCL surfaces are known as hydrophobic leading to a lack of favorable cell response. To overcome this problem, PCL surfaces will undergo a surface functionalization by grafting bioactive polymers bearing ionic groups.Objective
Our laboratory has demonstrated that the grafting of bioactive polymers onto biomaterials can improve cell and antibacterial response. The objective of this work is to functionalize PCL surfaces by the grafting of a bioactive polymer.Methods
The grafting of an ionic polymer poly(sodium styrene sulfonate) (polyNaSS), using UV irradiation on PCL surfaces was carried out in a two-steps reaction process. PCL surfaces were (1) chemically oxidized in order to allow the formation of (hydro)peroxide species. (2) Then immersed in a sodium styrene sulfonate (NaSS) solution and placed under UV irradiation to induce the decomposition of (hydro)peroxides to form radicals able to initiate the polymerization of the NaSS monomer. Various parameters, such as polymerization time, the effect of the surface activation, lamp power and monomer concentration were investigated in order to optimize the yield of polyNaSS grafting. The amount of polyNaSS grafted onto PCL surfaces was first determined by toluidine blue colorimetric method and characterized by contact angle measurement, Fourier-transform infrared spectra recorded in attenuated total reflection mode (ATR-FTIR), scanning electron microscopy with Oxford energy dispersive spectroscopy (SEM-EDS).Results
Various techniques showed that the grafting of ionic polymer polyNaSS bearing sulfonate groups was successful by using radicals from (hydro)peroxides able to initiate the radical polymerization of ionic monomers onto PCL surfaces.Conclusion
We developed a new approach of radical grafting which allows us to successfully graft bioactive polymer polyNaSS covalently to PCL surfaces using UV irradiation. 相似文献16.
S. Mirzaei M. El Yacoubi S. Garcia-Salicetti J. Boudy C. Kahindo V. Cristancho-Lacroix H. Kerhervé A.-S. Rigaud 《IRBM》2018,39(6):430-435
Background
The goal of this work is to develop a non-invasive method in order to help detecting Alzheimer's disease in its early stages, by implementing voice analysis techniques based on machine learning algorithms.Methods
We extract temporal and acoustical voice features (e.g. Jitter and Harmonics-to-Noise Ratio) from read speech of patients in Early Stage of Alzheimer's Disease (ES-AD), with Mild Cognitive Impairment (MCI), and from a Healthy Control (HC) group. Three classification methods are used to evaluate the efficiency of these features, namely kNN, SVM and decision Tree. To assess the effectiveness of this set of features, we compare them with two sets of feature parameters that are widely used in speech and speaker recognition applications. A two-stage feature selection process is conducted to optimize classification performance. For these experiments, the data samples of HC, ES-AD and MCI groups were collected at AP-HP Broca Hospital, in Paris.Results
First, a wrapper feature selection method for each feature set is evaluated and the relevant features for each classifier are selected. By combining, for each classifier, the features selected from each initial set, we improve the classification accuracy by a relative gain of more than 30% for all classifiers. Then the same feature selection procedure is performed anew on the combination of selected feature sets, resulting in an additional significant improvement of classification accuracy.Conclusion
The proposed method improved the classification accuracy for ES-AD, MCI and HC groups and promises the effectiveness of speech analysis and machine learning techniques to help detect pathological diseases. 相似文献17.
Background
In response to the increasing dependence issue and the spectacular development of automated applications, our research aims at answering the following question: Is building automation an acceptable solution to dependence? Under which conditions is building automation perceived as acceptable by organisations caring for dependent people? To answer those questions, we based on the CoCaPs project, which is a French national supported research project with the ambition to develop a low-cost sensors platform capable to provide enriched information about people's behaviour inside a building and aiming at better controlling energy and improving people's security and comfort. We had the opportunity to join the project team with the task to undertake a usage investigation.Methods
In this context, we interviewed 26 professionals involved in organisations dedicated to dependent people (nursing homes for dependent elderly people, nursing homes for dependant disable people, home-care organisations). All interviews were recorded and the completely verbatim of the interviews data were transcribed and analysed manually.Results
The data analysis revealed that surveyed people mainly react positively to the presentation of the sensors platform developed by the CoCaPs project team. However, surveyed people pointed out to a number data of criteria about acceptability related to automated systems dedicated to dependent persons.Conclusion
Our results suggest that an automated system is acceptable by the target organisations under various practical, ethical, social and financial criteria. Our research thus also demonstrate the importance to adopt a social-technique approach that means to consider simultaneously social and technical dimensions in any system development 相似文献18.
Background
Many human tissues are comprised of multilayered tissue structures in which spatial organization is essential to provide biological tissue functions.Methods
Recently, strategies such as 3D bioprinting, photolithography, 3D auto-assembly, molding or bulk acoustic cells manipulation have been developed to fabricate layered tissue mimics. These methods have broad applications in tissue engineering for the bioengineering of multilayered structures, and for the fundamental understanding of many microphysiological and pathological process like cell differentiation. Each method relies on the use of a special scaffold structure made of natural or artificially created biopolymers, and of specific cell types.In the field of neuronal 3D constructs fabrication, where ex-vivo samples are difficult to get, different strategies have been developed going from rat neurons culture to embryonic stem cells culture and differentiation into neurons after their encapsulation in 3D scaffolds.Conclusion
All those possibilities open new perspectives for the future, aiming to the development of different types of tissues composed of different multilayer structures. 相似文献19.
Background
The anterior cruciate ligament rupture is a common injury which mainly affects young and active population. Faced to this problem, the development of synthetic structures for ligament reconstruction is increasing. The most recent researches focused on the development of biodegradable structures that could be functionalized to enhance host integration. This work describes the elaboration of different poly(ε-caprolactone) prototypes for the rat anterior cruciate ligament replacement in order to found the best design for further in vivo assays.Methods
According to the literature, it was decided to elaborate two different poly(ε-caprolactone) prototypes: a braided one and a free-fibers one. A chemical grafting of a bioactive polymer–poly(sodium styrene sulfonate) – was performed on both prototypes and mechanical and biological testing were assessed. Based on these results, one rat was implanted with the best prototype.Results
The mechanical and biological results demonstrated that the best prototype to implant was the poly(sodium styrene sulfonate)-grafted braided prototype. After one-month implantation, no inflammation was observable around the scar. The rat demonstrated good flexion and extension of the lower limb without any anterior drawer. The prototype was highly anchored to the bone. ESEM images of the explanted prototype showed the presence of cells and tissue ingrowth along and around the fibers.Conclusion
This work demonstrates the feasibility to implant a bioactive and biodegradable synthetic ligament in the rat model without any inflammation and with a good tissue anchoring at a short-term time. This will lead to an extensive in vivo assay. 相似文献20.
Alexandra V. Chatzikonstantinou Maria V. Chatziathanasiadou Enrico Ravera Marco Fragai Giacomo Parigi Ioannis P. Gerothanassis Claudio Luchinat Haralambos Stamatis Andreas G. Tzakos 《Biochimica et Biophysica Acta (BBA)/General Subjects》2018,1862(1):1-8