2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)最新文献

{"title":"Real-time drip infusion monitoring through a computer vision system","authors":"N. Giaquinto, M. Scarpetta, M. Ragolia, Pietro Pappalardi","doi":"10.1109/MeMeA49120.2020.9137359","DOIUrl":"https://doi.org/10.1109/MeMeA49120.2020.9137359","url":null,"abstract":"Intravenous (IV) infusion is one of the most common therapies in hospitalized patients. Monitoring the flow rate of the fluid that is being administered to the patient is therefore very important for his safety, considering that both over-infusion and under-infusion can cause serious health problems. In this document, a novel method for monitoring the flow rate in IV infusions is presented, that is based on deep learning computer vision techniques. Basically, the drip chamber is filmed with a camera and object detection is used to count drops. The proposed method is therefore less invasive than other ones developed for this purpose. Experimental results show that it can produce an accurate real-time estimate of the instantaneous flow rate of the drip. For these reasons, the proposed method can be effectively adopted to implement monitoring and control systems for health facilities.","PeriodicalId":152478,"journal":{"name":"2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":" 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133420000","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}

{"title":"Personalized Computational Human Phantoms via a Hybrid Model-based Deep Learning Method","authors":"H. Khodajou-Chokami, Adeleh Bitarafan, D. Dylov, M. Baghshah, S. A. Hosseini","doi":"10.1109/MeMeA49120.2020.9137114","DOIUrl":"https://doi.org/10.1109/MeMeA49120.2020.9137114","url":null,"abstract":"Computed tomography (CT) simulators are versatile tools for scanning protocol evaluation, optimization of geometrical design parameters, assessment of image reconstruction algorithms, and evaluation of the impact of future innovations attempting to improve the performance of CT scanners. Computational human phantoms (CHPs) play a key role in simulators for the radiation dosimetry and assessment of image quality tasks in the medical x-ray systems. Since the construction of patient-specific CHPs can be both difficult and time-consuming, nominal standard/reference CHPs have been established, yielding significant discrepancies in the special design and optimization demands of patient dose and imaging protocols for most medical applications. Therefore, the aim of this work was to develop a personalized Monte-Carlo (MC) CT simulator equipped with a fast and well-structured tool-kit called DeepSegNet for automatic generation of patient-specific CHPs based on MRI images, working under two principal algorithms. To this end, we first developed a 3D convolutional neural network (3DCNN) for the automated segmentation of 3D MRI images to detect anatomical organs/tissues. Then, a 3D voxel merging (3DVM) algorithm constructing CHPs and making fast MC calculations were developed. The proposed 3DCNN benefits from the main merit of residual networks by designing a 15-layer model. Next, the 3DVM algorithm utilizes the segmented data acquired from the former step, to create realistic and optimized CHPs by material mapping and voxel size manipulating. The performance of our 3DCNN model on 20 patients as test cases was 84.54% and 74.52% in terms of average accuracy and Dice-Coefficient, respectively, outperforming SegNet, as a comparable method by 2%. Finally, we developed an MC CT simulator by implementing a set of our generated CHPs. The efficiency of our 3DVM algorithm in constructing CHPs was assessed in terms of MC execution time and the number of merged voxels representing occupied storage memory and compared to the existing lattice method. Besides, the accuracy of our 3DVM investigated through the estimation of patient dose maps and image reconstruction. Results demonstrated a significant reduction of about 96% in the number of voxels and a 15% reduction in MC execution time for x-ray photon transportation while keeping the same accuracy. Therefore, this software package has a strong potential in the optimization of therapeutic and radiological imaging procedures.","PeriodicalId":152478,"journal":{"name":"2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"924 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123053190","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}

{"title":"Upper limbs cranking for post-stroke rehabilitation: a pilot study on healthy subjects","authors":"E. Digo, S. Pastorelli, T. Vieira, A. Botter, L. Gastaldi","doi":"10.1109/MeMeA49120.2020.9137282","DOIUrl":"https://doi.org/10.1109/MeMeA49120.2020.9137282","url":null,"abstract":"Since one of the major consequences of stroke is hemiparesis, the rehabilitation of upper limbs is necessary to improve the quality of life. Arm cranking gesture represents an alternative rehabilitation tool, especially if accompanied by a biofeedback involving and motivating patients. The aim of this pilot study was twofold: (1) to evaluate the effect of a visual and virtual biofeedback on arm cranking gesture and (2) to estimate the duration of pull and push phases of the crank cycle. Nine healthy and young subjects were involved in the test and were asked to perform the arm cranking gesture in different conditions. A stereophotogrammetric system was adopted to create a virtual, visual and real time biofeedback of cadence, to measure the real cadence of participants and to estimate push and pull phases durations. Results showed that the biofeedback helped subjects to follow an externally imposed cadence. Furthermore, the pull phase resulted to be slightly longer than the push one, although the angular amplitude of the two phases suggested they were the same.","PeriodicalId":152478,"journal":{"name":"2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121376432","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}

{"title":"Novel kinematics indexes for the upper limb reaching movements evaluation with robotic exoskeleton","authors":"L. Iuppariello, G. Faiella, P. Macias, M. Cesarelli, M. Nespoli, F. Clemente","doi":"10.1109/MeMeA49120.2020.9137188","DOIUrl":"https://doi.org/10.1109/MeMeA49120.2020.9137188","url":null,"abstract":"The study of the Reaching movements, since their significant importance for independence in daily living activities, is increasingly proposed in rehabilitation programs of several disorders of upper limbs. Robotic rehabilitation programs represent a valid approach to this issue, as they are well-matched to realize an rigorous, task-oriented motor training as part of an combined rehabilitation program, counting also non-robotic approaches. However, there is a lack of information showing progresses on parameters related to activities of daily living (ADL), and some experimental studies indicates that to date robotic training fails to transfer the obtained improvements on the functional level. This may be since most of the existing robotic devices are based on the repetition of stereotypical movements rather than of applying more appropriated principles of motor learning. This work proposes a new quantitative kinematic index to assess the kinematic quality of upper arm reaching movements based from the mathematical minimum jerk theory. The results showed as the indexes are close to the ideal ones in according with the mathematica model, making them suitable in a clinical contest for the valuation of the ADL performance during the rehabilitation of upper limb reaching movements from several diseases.","PeriodicalId":152478,"journal":{"name":"2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129116657","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}

{"title":"Dealing with probabilistic problems in health care: What about the role of knowledge in daily life?","authors":"M. Agus, M. Peró-Cebollero, J. Guàrdia-Olmos, M. P. Penna","doi":"10.1109/MeMeA49120.2020.9137266","DOIUrl":"https://doi.org/10.1109/MeMeA49120.2020.9137266","url":null,"abstract":"This study investigates the role of knowledge about probability on probabilistic reasoning applied in a health scenario. A total of 594 undergraduates attempted to solve a probabilistic problem regarding healthiness; they were distinguished on the basis of their previous curricula and assessed in relation to a set of individual dimensions (confidence in the correctness of their response, statistical anxiety, numerical and visuo-spatial abilities) and a contextual feature (time pressure). No differences in the occurrence of correct/incorrect answers were discovered in participants with/without knowledge about probability. The participants without knowledge showed a confidence in the correctness of their response higher than that of their colleagues with previous knowledge. Network analysis was applied to investigate all dimensions under inquiry. Findings provide evidence regarding the positive connections between knowledge and correctness and between confidence and correctness. Regarding the practical and applicative issues, outcomes support the idea that it could be of great utility to devise programs to promote well-being and public health by the development of ad hoc educational strategies devoted to support confidence in one's ability to solve problems and to enhance uncertainty tolerance.","PeriodicalId":152478,"journal":{"name":"2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129387107","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}

{"title":"Adaptive IMU-based Calibration of the Center of Joints for Movement Analysis: One Case Study","authors":"S. G. D. Villa, Ana Jiménez Martín, Juan Jesús García Domínguez","doi":"10.1109/MeMeA49120.2020.9137135","DOIUrl":"https://doi.org/10.1109/MeMeA49120.2020.9137135","url":null,"abstract":"This work proposes a novel real-time estimator of the center of joints using an inertial measurement unit (IMU), able to adapt to variations in the distance to this center during the joint motion. Our proposal, called ArVE, to the best of our knowledge, is the first real-time estimator of the IMU-joint center distance vector based on IMUs. Previous works are off-line and require a complete measure batch to be solved. We use an extended Kalman filter (EKF) to obtain an IMU-joint vector at each time, instead of the already proposed least-squares approximation, which require a previous collection of measurements and thus the distance vector to be constant. Also, the EKF framework avoids the previous stage of signal noise reduction and directly uses the IMU measurements. We carry out two synthetic experiments in order to evaluate the performance of ArVE in fixed joints when the IMU-joint vector remains constant and when this vector changes over time. We compare the performance of ArVE with a method from the State of the Art. ArVE shows an average error of 1.5 mm and 6.0 mm in the fixed and changing IMU-joint vector cases, respectively. Finally, we test ArVE together with the State of the Art versus an optical system in a real scenario obtaining the IMU-hip vector in one case study. Compared with the optical system, our proposal shows promising results in the test.","PeriodicalId":152478,"journal":{"name":"2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115586307","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}

{"title":"New Measurement Method in Drug Sensing by Direct Total-Charge Detection in Voltammetry","authors":"S. Aiassa, J. G. Martínez, D. Demarchi, S. Carrara","doi":"10.1109/MeMeA49120.2020.9137197","DOIUrl":"https://doi.org/10.1109/MeMeA49120.2020.9137197","url":null,"abstract":"Electrochemical biosensors are promoting point-of-care and wearable instrumentation due to their high versatility in measuring human metabolites. There is a considerable number of biological compounds that can be detected and measured through voltammetry based techniques. Voltmmetry some times requires peak identification and quantification that are non-trivial to be efficiently implemented by automatic instrumentation. To overcome the complexity of automatic peak estimation, we propose here an instrumentation circuit for edge-computing in pharmacology relying on an entirely novel measurement method via TotalCharge Detection in Cyclic voltammetry (TCDC). Namely, our TCDC method innovatively applies the coulometry measurement to the well-established voltammetry procedure. The proposed instrumentation accumulates the total charge exchanged in the faradaic process, exploiting a Nagaraj integrator as charge suppressor to fit the application-specific constraints. The work shows accurate simulations of the TCDC circuit on a set of experimental measures, acquired on paracetamol as benchmark drug. The proposed measurement technique and the developed circuit are compared to the peak detection method usually adopted in literature. The results demonstrate that the proposed system is a perfect trade-off between the doubled limit-of-detection and a tenfold reduction in measurement errors. At the same time, we eliminate any need for data oversampling and processing, promoting the TCDC as an efficient new measurement method for point-of-care and wearable monitoring of biological compounds.","PeriodicalId":152478,"journal":{"name":"2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127561012","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}

{"title":"A Hybrid Deep Model for Automatic Arrhythmia Classification based on LSTM Recurrent Networks","authors":"Adeleh Bitarafan, Afra Amini, M. Baghshah, H. Khodajou-Chokami","doi":"10.1109/MeMeA49120.2020.9137328","DOIUrl":"https://doi.org/10.1109/MeMeA49120.2020.9137328","url":null,"abstract":"Electrocardiogram (ECG) recording of electrical heart activities has a vital diagnostic role in heart diseases. We propose to tackle the problem of arrhythmia detection from ECG signals totally by a deep model that does not need any hand-designed feature or heuristic segmentation (e.g., ad-hoc R-peak detection). In this work, we first segment ECG signals by detecting R-peaks automatically via a convolutional network, including dilated convolutions and residual connections. Next, all beats are aligned around their R-peaks as the most informative section of the heartbeat in detecting arrhythmia. After that, a deep learning model, including both dilated convolution layers and a Long-Short Term Memory (LSTM) layer, is utilized to recognize arrhythmia. Indeed, the segments centered around R-peaks acquired from the previous step are fed into this network to distinguish various arrhythmias. The LSTM part of the proposed network enables modeling the relation among different heartbeats in a sequence. Experiments on the MIT-BIH databases and Creighton university ventricular tachyarrhythmia show the superiority of our proposed method on arrhythmia detection in comparison with the recent methods proposed for this problem. The performance of the proposed model on test samples is 98.93%, 99.78%, and 99.58% respectively in terms of overall accuracy, sensitivity, and specificity for tackling the problem of 4-class arrhythmia classification. Thus, it outperforms other recent methods with a large margin in terms of accuracy and specificity.","PeriodicalId":152478,"journal":{"name":"2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127783936","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}

{"title":"A novel measurement technique for the assessment of best positive end-expiratory pressure in newborn patient","authors":"P. Marchionni, A. Galli, V. Carnielli, L. Scalise","doi":"10.1109/MeMeA49120.2020.9137151","DOIUrl":"https://doi.org/10.1109/MeMeA49120.2020.9137151","url":null,"abstract":"More than 10 million of infants born prematurely each year in the word. In case of a preterm delivery, there is a dramatic physiological transition from fetal to neonatal life often associated to some infant insufficiencies and/or pathologies. Respiratory diseases, such as distress syndrome and bronchopulmonary dysplasia, are common reasons for admission to a neonatal unit. In order to replace spontaneous breathing and to restore a physiologic gas exchanges, mechanical ventilation (MV) is very often required also in order to evaluate the degree of the insufficiency and/or of the diseases. To control the MV parameters, and the positive end-respiratory pressure, pulse oximetry is the leading instrumentation allowing the measurement of the saturation of the oxygen (SpO2) molecules linked to the hemoglobin in the infant blood. The aim of this paper is to present a novel approach for the assessment of the best positive end-expiratory pressure (PEEP) values using pulse oximetry. Tests have been conducted on a small cohort of 5 infants. Subjects have been monitored using a Computer Aided Work for a period of 4 hours; SpO2, HR (heart rate), FiO2 (fraction of inhale oxygen) and PIP (peak inspiratory pressure) have been simultaneously acquired. Results show that, in average, max SpO2 values are obtained with a PEEP value of 8 mmH2O. Even if the number of subjects used in this study is limited to derive final conclusions, the prospective are extremely interesting in terms of optimal patient treatments.","PeriodicalId":152478,"journal":{"name":"2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124958043","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}

{"title":"Detection of Neonatal Patient Motion Using a Pressure-Sensitive Mat","authors":"Samreen Aziz, Yasmina Souley Dosso, Shermeen Nizami, K. Greenwood, J. Harrold, J. Green","doi":"10.1109/MeMeA49120.2020.9137147","DOIUrl":"https://doi.org/10.1109/MeMeA49120.2020.9137147","url":null,"abstract":"Patient movements can cause motion artifacts on physiological signals and can result in false alarms in a continuous patient care environment. This paper explores the use of data from a pressure sensitive mat (PSM), placed below neonates in the neonatal intensive care unit (NICU), to detect patient movement. The centre of pressure (COP) is tracked over time using a sliding window. Windows exhibiting large deviations in the COP are indicative of patient motion. Local averaging and window boundary suppression leads to improved movement detection accuracy. Using data from five patients, optimal parameter values were determined using a grid search method. After averaging the best performing parameters, a window size of six seconds was found to be optimal across patients, resulting in an area under the ROC curve of 0.909. Detection accuracy is maintained when evaluated on a patient not used to optimize algorithm parameters, with an accuracy of 93.4%. It is hoped that the movement detection algorithm developed in this work will be useful for gating motion-artifact related false alarms from neonatal patient monitors.","PeriodicalId":152478,"journal":{"name":"2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124306149","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}