2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)最新文献_第10页

Statistical correlation analysis between kinematic features and clinical indexes and scales for obese patients 肥胖患者运动特征与临床指标及量表的统计相关分析

2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2021-06-23 DOI: 10.1109/MeMeA52024.2021.9478776

G. Cesarelli, L. Donisi, G. Caprio, M. Scioli, A. Biancardi, G. D'Addio

{"title":"Statistical correlation analysis between kinematic features and clinical indexes and scales for obese patients","authors":"G. Cesarelli, L. Donisi, G. Caprio, M. Scioli, A. Biancardi, G. D'Addio","doi":"10.1109/MeMeA52024.2021.9478776","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478776","url":null,"abstract":"The study of posture and gait abnormalities has revealed over years potential information to improve the rehabilitation outcome of several classes of ill patients; nevertheless, this results still an area of research almost completely unexplored in the case of obese patients. Consequently, this study was designed as a preliminary investigation to determine potential statistical correlations between kinematic features and “gold standard” methodologies in the field, e.g., the Western Ontario and Mc Master University scale and the Barthel index. To this aim, physicians prepared bioelectrical impedance analyses and clinical assessments to evaluate patients' clinical scores, while biomedical engineers have organized Instrumented Stand and Walking tests to quantify several kinematic features using a microelectromechanical system equipped by a series of inertial measurement units. Finally, a statistical correlation analysis has allowed to reveal several features – related to patients’ anticipatory postural adjustments movements and gait – demonstrated a mild and moderate correlation with some clinical indices. In conclusion, this paper presents a novel view to address and design innovative rehabilitation strategies for obese patients.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126873538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Wearable SSVEP BCI for AR-based, Real-time Monitoring Applications 基于ar的实时监控应用的可穿戴SSVEP BCI

2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2021-06-23 DOI: 10.1109/MeMeA52024.2021.9478593

P. Arpaia, E. D. Benedetto, N. Donato, Luigi Duraccio, N. Moccaldi

引用次数: 6

Densely Connected Convolutional Networks (DenseNet) for Diagnosing Coronavirus Disease (COVID-19) from Chest X-ray Imaging 基于胸部x线影像诊断冠状病毒病(COVID-19)的密集连接卷积网络(DenseNet

2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2021-06-23 DOI: 10.1109/MeMeA52024.2021.9478715

Hamed Tabrizchi, A. Mosavi, Z. Vámossy, A. Várkonyi-Kóczy

引用次数: 8

Efficient feature selection for electroencephalogram-based authentication 基于脑电图认证的高效特征选择

2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2021-06-23 DOI: 10.1109/MeMeA52024.2021.9478700

Nibras Abo Alzahab, M. Baldi, L. Scalise

{"title":"Efficient feature selection for electroencephalogram-based authentication","authors":"Nibras Abo Alzahab, M. Baldi, L. Scalise","doi":"10.1109/MeMeA52024.2021.9478700","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478700","url":null,"abstract":"Opposed to classic authentication protocols based on credentials, biometric-based authentication has recently emerged as a promising paradigm for achieving fast and secure authentication of users. Among the several families of biometric features, electroencephalogram (EEG)-based biometrics is considered as a promising approach due to its unique characteristics. Classification systems based on machine learning allow processing of large amounts of data and performing accurate attribution of each signal to the most relevant group, thus representing an invaluable tool for EEG-based biometrics. This paper provides an experimental evaluation of the performance achievable by EEG-based biometrics employing machine learning. We consider several groups of EEG signals and propose a suitable feature extraction criterion. Then, the extracted features are used along with neural network-based classification algorithms, K Nearest Neighbours (KNN), and eXtreme Gradient Boost (XGBoost) for attributing any EEG signal to a subject. A full feature set and a reduced feature sets are considered and tested on three public data sets. The feature selection criteria are based on a correlation map among features, ANOVA F-test, and logistic regression weights. The results show that the reduced feature sets achieves a significant reduction in computation time over the full feature set, while also providing some improvement in performance.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125968397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Application of Hybrid Network of UNet and Feature Pyramid Network in Spine Segmentation UNet混合网络与特征金字塔网络在脊柱分割中的应用

2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2021-06-23 DOI: 10.1109/MeMeA52024.2021.9478765

Xingxing Liu, Wenxiang Deng, Yang Liu

{"title":"Application of Hybrid Network of UNet and Feature Pyramid Network in Spine Segmentation","authors":"Xingxing Liu, Wenxiang Deng, Yang Liu","doi":"10.1109/MeMeA52024.2021.9478765","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478765","url":null,"abstract":"Spine segmentation is a common task for spinal imaging and spinal surgical navigation. Spine segmentation provides valuable information for the diagnosis, and the segmentation output can also serve as an input for downstream surgical navigation. Unfortunately, spine segmentation is a labor-intensive task. In this study, we applied a deep network combining feature pyramid network (FPN) and UNet to the segmentation of vertebral bodies (VBs), referring as Res50_UNet. Compared with the original UNet, Res50_UNet has the following enhancements: 1) five consecutive spine MRI slices and two coordinate maps are concatenated as the input; 2) the convolutional block from ResNet are used; 3) an FPN architecture is applied to extracting rich multi-scale features and obtaining segmentation output. Experiments were conducted on an annotated T2-weighted MRIs of the lower spine dataset. We have benchmarked Res50_UNet against UNet and other UNet based network structures. It was found that Res50_UNet needs the lowest number of epochs (~1000 epochs) to achieve steady-state performance. The accuracy (AC) of Res50_UNet is higher than 99.5% with only 1000 epochs, which is very impressive. This study demonstrated the feasibility of applying Res50_UNet in spine segmentation. The network integrates the characteristics of FPN and UNet. These results have shown the potential for Res50_UNet in spine MRI segmentation, especially when a low number of epochs is desirable.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"284 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122967735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The use of cognitive training and tDCS for the treatment of an high potential subject: a case study 运用认知训练和tDCS治疗高潜能受试者:个案研究

2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2021-06-23 DOI: 10.1109/MeMeA52024.2021.9478697

Roberta Renati, N. S. Bonfiglio, Ludovica Patrone, D. Rollo, M. P. Penna

{"title":"The use of cognitive training and tDCS for the treatment of an high potential subject: a case study","authors":"Roberta Renati, N. S. Bonfiglio, Ludovica Patrone, D. Rollo, M. P. Penna","doi":"10.1109/MeMeA52024.2021.9478697","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478697","url":null,"abstract":"Scientific literature has shown how people with ADHD, subjects with High Potential, and with High Levels of Creativity share the same behavioral and cognitive patterns, especially related to some aspects associated with executive functions, such as attentional disorders, impulsivity, and inhibitory control deficit. Several studies have shown how it is possible to improve executive functions by regulating the neuronal activity of the Prefrontal Area. Other researches have obtained equally interesting results through the use of cognitive training and video games, as well as the aim of motivating children and adolescents. This paper presents a clinical case of a high potential adolescent treated through the use of cognitive training with tDCS. The treatment consisted of the use of tDCS associated with cognitive training for 12 sessions. Cognitive battery before starting treatment and at the end of treatment, and trials on executive functions before and after each training session, where administered. The results show an improvement in the cognitive battery and the trials of executive functions, especially in the second part of the training. The results obtained in this work demonstrate how the use of training, associated with tDCS neurostimulation, represents a useful and functional treatment for people with High Potential. The protocol proposed here also lies in the possibility of being used remotely and without the presence of the operators, overcoming the limits of traditional methods.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114403788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Ensembling CNNs for dermoscopic analysis of suspicious skin lesions 集成cnn用于可疑皮肤病变的皮肤镜分析

2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2021-06-23 DOI: 10.1109/MeMeA52024.2021.9478760

Yali Nie, M. Ferro, P. Sommella, M. Carratù, S. Cacciapuoti, G. D. Leo, J. Lundgren, G. Fabbrocini

引用次数: 0

Measurement and Characterization of Hearing Protection Devices in the Presence of Impulse Sound 脉冲声下听力保护装置的测量和特性

2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2021-06-23 DOI: 10.1109/MeMeA52024.2021.9478767

Bruno Tardif, D. Lo, R. Goubran

{"title":"Measurement and Characterization of Hearing Protection Devices in the Presence of Impulse Sound","authors":"Bruno Tardif, D. Lo, R. Goubran","doi":"10.1109/MeMeA52024.2021.9478767","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478767","url":null,"abstract":"Impulsive sounds can cause severe hearing damage and even hearing loss. Sound protection devices are widely used to attenuate impulsive sounds and reduce their impact on hearing. Properly measuring and characterizing the sound attenuation is essential when choosing a specific hearing protection device. Currently, hearing protection devices are often characterized using the Impulse Peak Insertion Loss (IPIL) that measures the total attenuation across all frequency bands. IPIL does not provide any information about the spectral attenuation of the device. Human hearing is spectrally sensitive, and the risk of noise-induced hearing damage is frequency-dependent. Therefore, characterizing hearing protection devices has to be done for both the peak and the full audible frequency spectrum from 20 Hz to 20k Hz. In this paper, we propose a novel energy preserving method for estimating the 1/3 octave band insertion loss using the continuous wavelet transform. To do so, we collected gunshot audio sounds from firing a sniper rifle and evaluated the sound attenuation effect of adding a sound protection device (or sound suppressor) to the rifle. The method that we called Wavelets Octave Band Insertion Loss (WOBIL) is compared with existing methods such as the IPIL, the Impulsive Spectral Insertion Loss (ISIL) and the recently published Octave Band Impulse Peak Insertion Loss (OBIPIL).","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127569108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A preliminary study on the dynamic characterization of a MEMS microgripper for biomedical applications 生物医学用MEMS微夹持器动态特性的初步研究

2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2021-06-23 DOI: 10.1109/MeMeA52024.2021.9478703

F. Vurchio, Gabriele Bocchetta, Giorgia Fiori, A. Scorza, N. Belfiore, S. Sciuto

{"title":"A preliminary study on the dynamic characterization of a MEMS microgripper for biomedical applications","authors":"F. Vurchio, Gabriele Bocchetta, Giorgia Fiori, A. Scorza, N. Belfiore, S. Sciuto","doi":"10.1109/MeMeA52024.2021.9478703","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478703","url":null,"abstract":"This preliminary study concerns the dynamic characterization of a MEMS microgripper for biomedical applications. In particular, a prototype of microgripper, embedded with electrostatic comb-drive actuators, has been powered with a 10V sinusoidal input at different frequencies, 0.5 Hz, 1.0 Hz and 4.0 Hz. The response of the device has been recorded with a trinocular optical microscope, equipped with a digital camera and the recorded videos have been analysed with an in-house software implemented by the authors for the measurement of the comb-drive angular displacement, velocity and acceleration. The uncertainty analysis has been carried out to identify the uncertainty sources that characterize the measurements. Experimental data showed that the maximum angular displacement is (13.2 ± 0.2)•10-3 rad, (13.6 ± 0.2)•10-3 rad and (13.1 ± 0.3)•10-3 rad, the maximum angular velocity is (2.8 ± 0.2)•10-2 rad/s, (5.7 ± 0.4)•10-2 rad/s and (19.9 ± 1.5)•10-2 rad/s, and the angular acceleration is 0.178 ± 0.015 rad/s2, 0.72 ± 0.04 rad/s2 and 6.3 ± 0.7 rad/s2 for 0.5 Hz, 1.0 Hz and 4.0 Hz, respectively. The measurement results have been compared with the expected values from the theoretical model that describes the behaviour of the microgripper: the overall percentage error (PE) between the measured and the expected values at different frequencies is lower than 1%, 1% and 3% for the angular displacement, velocity and acceleration respectively.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114180832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Non-contact Blood Pressure Estimation Using a 300 GHz Continuous Wave Radar and Machine Learning Models 使用300 GHz连续波雷达和机器学习模型的非接触式血压估计

2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2021-06-23 DOI: 10.1109/MeMeA52024.2021.9478734

Marie Jung, M. Caris, S. Stanko

{"title":"Non-contact Blood Pressure Estimation Using a 300 GHz Continuous Wave Radar and Machine Learning Models","authors":"Marie Jung, M. Caris, S. Stanko","doi":"10.1109/MeMeA52024.2021.9478734","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478734","url":null,"abstract":"This work shows a novel system to measure the blood pressure (BP) values of subjects without body contact. For this purpose, a continuous wave (CW) radar consisting of a vector network analyzer (VNA), horn antennas, and frequency converters is operated at 300 GHz. By using discrete wavelet transformation and suitable signal processing, characteristics of heart sounds and certain features in the time and frequency domain are extracted from the radar signal. During that process, the heart rate of the subjects was also measured with a mean relative error (MRE) of 4.57 %. A data set of eight subjects is built up and combined with an existing database, thus creating enough instances to use machine learning (ML) models for blood pressure estimation. The models are trained, optimized and cross-validated with different subsets of the features. The ones with the best performance, support vector machine (SVM) and bagging, are also tested with the data of individual subjects, unknown to the model, which was trained with the remaining instances. Using the features in the frequency domain the best results were obtained with an MRE of 8.3 % for the diastolic BP (DBP) and 8.04 % for the systolic BP (SBP). These results suggest that this technique is of potential use for blood pressure monitoring without body contact and offer exciting possibilities for future work.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122120128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6