Pub Date : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478727
L. Donisi, P. Moretta, A. Coccia, F. Amitrano, A. Biancardi, G. D'Addio
Unilateral Spatial Neglect is a cognitive impairment of neuropsychological interest that is a consequence of stroke able to influence negatively the rehabilitation outcome of patients with stroke. The aim of the study is to explore the feasibility of machine learning to classify stroke patients with and without unilateral spatial neglect using clinical features. We performed the study using a machine learning approach by means the following tree-based algorithms: Decision Tree, Random Forest, Rotation Forest, AdaBoost of decision stumps and Gradient Boost tree using six clinical features both numerical and nominal: Montreal Cognitive Assessment, Functional Independence Measure scale, Barthel Index, aetiology, site of brain lesion and presence of hemiparesis at lower limbs. Tree-based Machine learning analysis achieved interesting results in terms of evaluation metrics scores; the best algorithm was Random Forest with an Accuracy, Sensitivity, Specificity, Precision and Area under the Receiver Operating Characteristic curve equal to 0.92, 0.83, 1.00, 1.00, 0.95 respectively. The study demonstrated the proposed combination of clinical features and algorithms represents a valuable approach to automatically classify stroke patients with and without Unilateral Spatial Neglect. The future investigations on enriched datasets will further confirm the potential application of this methodology as prognostic support to be chosen among those already implemented in the clinical field.
{"title":"Distinguishing Stroke patients with and without Unilateral Spatial Neglect by means of Clinical Features: a Tree-based Machine Learning Approach","authors":"L. Donisi, P. Moretta, A. Coccia, F. Amitrano, A. Biancardi, G. D'Addio","doi":"10.1109/MeMeA52024.2021.9478727","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478727","url":null,"abstract":"Unilateral Spatial Neglect is a cognitive impairment of neuropsychological interest that is a consequence of stroke able to influence negatively the rehabilitation outcome of patients with stroke. The aim of the study is to explore the feasibility of machine learning to classify stroke patients with and without unilateral spatial neglect using clinical features. We performed the study using a machine learning approach by means the following tree-based algorithms: Decision Tree, Random Forest, Rotation Forest, AdaBoost of decision stumps and Gradient Boost tree using six clinical features both numerical and nominal: Montreal Cognitive Assessment, Functional Independence Measure scale, Barthel Index, aetiology, site of brain lesion and presence of hemiparesis at lower limbs. Tree-based Machine learning analysis achieved interesting results in terms of evaluation metrics scores; the best algorithm was Random Forest with an Accuracy, Sensitivity, Specificity, Precision and Area under the Receiver Operating Characteristic curve equal to 0.92, 0.83, 1.00, 1.00, 0.95 respectively. The study demonstrated the proposed combination of clinical features and algorithms represents a valuable approach to automatically classify stroke patients with and without Unilateral Spatial Neglect. The future investigations on enriched datasets will further confirm the potential application of this methodology as prognostic support to be chosen among those already implemented in the clinical field.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"61 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":"127126537","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}
Pub Date : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478777
H. Tsai, L. Chao, Cheng-Ru Li, Kuo-Cheng Huang, Yu-Hsuan Lin, D. Shieh
Quantitative polymerase chain reaction (qPCR) has been widely employed for the positive or negative detection of bacteria or viruses, particularly SARS-CoV-2. Fluorescence signal and cycle threshold information is critical for the positive and negative detection of target test samples in qPCR systems. To determine viral concentration, the fluorescence intensity of each cycle must be recorded using a qPCR system. In general, the time points of fluorescence excitation and excitation light intensity affect fluorescence intensity. Thus, this study proposed an effective excitation method for enhancing fluorescence intensity. Several parameters, including excitation light intensity, the excitation time point, and the reaction time of the reagent at each temperature stage, were modified in assessing fluorescence performance and determining suitable parameters for fluorescence excitation in a qPCR system. Fluorescence intensity resulted in the most optimal fluorescence performance; specifically, excitation was triggered by using a 30 mA current, and the excitation light was activated when the temperature decreased to 60 °C. Total reaction time was 1 s, and the concentrated fluorescence value and suitable cycle threshold value were obtained. Overall, high efficiency, low fluorescence decay, and high light stability were observed. The present findings demonstrate that controlling the time point of excitation light can enhance the fluorescence efficiency and performance of qPCR systems, with relevant benefits in medical diagnostics and rapid viral detection, among other applications.
{"title":"Enhancing the Fluorescence and Cycle Threshold of qPCR Devices Through Excitation Time Point Adjustment","authors":"H. Tsai, L. Chao, Cheng-Ru Li, Kuo-Cheng Huang, Yu-Hsuan Lin, D. Shieh","doi":"10.1109/MeMeA52024.2021.9478777","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478777","url":null,"abstract":"Quantitative polymerase chain reaction (qPCR) has been widely employed for the positive or negative detection of bacteria or viruses, particularly SARS-CoV-2. Fluorescence signal and cycle threshold information is critical for the positive and negative detection of target test samples in qPCR systems. To determine viral concentration, the fluorescence intensity of each cycle must be recorded using a qPCR system. In general, the time points of fluorescence excitation and excitation light intensity affect fluorescence intensity. Thus, this study proposed an effective excitation method for enhancing fluorescence intensity. Several parameters, including excitation light intensity, the excitation time point, and the reaction time of the reagent at each temperature stage, were modified in assessing fluorescence performance and determining suitable parameters for fluorescence excitation in a qPCR system. Fluorescence intensity resulted in the most optimal fluorescence performance; specifically, excitation was triggered by using a 30 mA current, and the excitation light was activated when the temperature decreased to 60 °C. Total reaction time was 1 s, and the concentrated fluorescence value and suitable cycle threshold value were obtained. Overall, high efficiency, low fluorescence decay, and high light stability were observed. The present findings demonstrate that controlling the time point of excitation light can enhance the fluorescence efficiency and performance of qPCR systems, with relevant benefits in medical diagnostics and rapid viral detection, among other applications.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"1 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":"129840775","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}
Pub Date : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478708
Giuliana Emmolo, Daryl Ma, Danilo Demarchi, P. Georgiou
The paradigm of Internet of Things (IoT) has revolutionised the field of human health monitoring. Recent research works outline an ever growing interest in the development of miniaturized fully functioning devices, where optimization strategies in terms of size, power consumption and data transmission capabilities represents the main requirements as well as the biggest challenges at the design stage. In this paper we provide an analysis into a data transmission method based on digital mixing for combining multiple inputs channels into a single output. We first demonstrate that the sources of the error generated in the output stream are the frequency ratio of the input signals and their relative phase shift. With the results from the simulations, we demonstrate that the error performed on the lower frequency information in the mixed signal has a trend which is exponentially decreasing with the input frequency ratio. Additionally, we prove that the relative phase shift of the input signals may significantly impact the error towards lower input frequency ratios. Afterwards, we analyze the system power consumption, and we demonstrate that the power trend is linear with the input frequency ratio. Lastly, we discuss the error performance versus power trade-off of the system, which is helpful for the design of the input frequency levels for a specific target application.
{"title":"Multiple Input, Single Output Frequency Mixing Communication Technique for Low Power Data Transmission","authors":"Giuliana Emmolo, Daryl Ma, Danilo Demarchi, P. Georgiou","doi":"10.1109/MeMeA52024.2021.9478708","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478708","url":null,"abstract":"The paradigm of Internet of Things (IoT) has revolutionised the field of human health monitoring. Recent research works outline an ever growing interest in the development of miniaturized fully functioning devices, where optimization strategies in terms of size, power consumption and data transmission capabilities represents the main requirements as well as the biggest challenges at the design stage. In this paper we provide an analysis into a data transmission method based on digital mixing for combining multiple inputs channels into a single output. We first demonstrate that the sources of the error generated in the output stream are the frequency ratio of the input signals and their relative phase shift. With the results from the simulations, we demonstrate that the error performed on the lower frequency information in the mixed signal has a trend which is exponentially decreasing with the input frequency ratio. Additionally, we prove that the relative phase shift of the input signals may significantly impact the error towards lower input frequency ratios. Afterwards, we analyze the system power consumption, and we demonstrate that the power trend is linear with the input frequency ratio. Lastly, we discuss the error performance versus power trade-off of the system, which is helpful for the design of the input frequency levels for a specific target application.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"59 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":"126228428","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}
Pub Date : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478676
Markus Schinle, Christina Erler, Timon Schneider, Joana Plewnia, Wilhelm Stork
Following the paradigm of precision medicine, the combination of health data and Machine Learning (ML) is promising to improve the quality of healthcare services e.g. by making diagnoses and therapeutic interventions as early and precise as possible. The implementation of this approach requires sufficient amounts of data with a high quality along the data life cycle. This goal seems recently achievable through the implementation of several national digital health strategies and the hope of a growing societal acceptance of digital health applications due to the implications of the COVID-19 pandemic. But, a collection of tools and methods is missing, which supports developers to use data as driving force of the development process. Due to the iterative nature of software application development, it allows the continuous improvement through the integration of collected digital data. We refer to this as a data-driven approach and identify steps to take and tools for its implementation. Associated challenges and opportunities of this translational approach are outlined on the example of a self-developed dementia screening application. Using our methodology, we compared multiple ML algorithms based on the data of an observational study (n=55) and achieved models with sensitivity up to 89% for unhealthy participants within this use case.
{"title":"Data-driven Development of Digital Health Applications on the Example of Dementia Screening","authors":"Markus Schinle, Christina Erler, Timon Schneider, Joana Plewnia, Wilhelm Stork","doi":"10.1109/MeMeA52024.2021.9478676","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478676","url":null,"abstract":"Following the paradigm of precision medicine, the combination of health data and Machine Learning (ML) is promising to improve the quality of healthcare services e.g. by making diagnoses and therapeutic interventions as early and precise as possible. The implementation of this approach requires sufficient amounts of data with a high quality along the data life cycle. This goal seems recently achievable through the implementation of several national digital health strategies and the hope of a growing societal acceptance of digital health applications due to the implications of the COVID-19 pandemic. But, a collection of tools and methods is missing, which supports developers to use data as driving force of the development process. Due to the iterative nature of software application development, it allows the continuous improvement through the integration of collected digital data. We refer to this as a data-driven approach and identify steps to take and tools for its implementation. Associated challenges and opportunities of this translational approach are outlined on the example of a self-developed dementia screening application. Using our methodology, we compared multiple ML algorithms based on the data of an observational study (n=55) and achieved models with sensitivity up to 89% for unhealthy participants within this use case.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"38 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":"125008804","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}
Pub Date : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478723
M. Ragolia, F. Attivissimo, A. Nisio, A. Lanzolla, M. Scarpetta
Surgery navigation techniques aim to support surgeons during operations, resulting in improved accuracy and patient safety. In this context, electromagnetic tracking systems (EMTSs) are mainly used since they enable real-time tracking of small EM sensors included in surgical tools without line-of-sight restrictions. On the other hand, these systems are very sensible to magnetic field variations that can affect sensor position estimation performance. In this paper we analyze how magnetic field variations caused by the noise of transmitting coils’ excitation currents affect system performance, and we propose a technique to reduce its undesirable effect. This method includes, in the position estimation algorithm, the measurement of excitation currents, thus compensating errors in sensor signal caused by current noise.Different simulation tests were performed to assess the proposed method which is based on modeling the magnetic field produced by the field generator (FG). Finally, it is validated by using experimental data provided by a novel EMTS prototype, obtaining noise peaks reduction and an overall mean position error of 3 mm at a distance of 600 mm from the FG.
{"title":"Reducing effect of magnetic field noise on sensor position estimation in surgical EM tracking","authors":"M. Ragolia, F. Attivissimo, A. Nisio, A. Lanzolla, M. Scarpetta","doi":"10.1109/MeMeA52024.2021.9478723","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478723","url":null,"abstract":"Surgery navigation techniques aim to support surgeons during operations, resulting in improved accuracy and patient safety. In this context, electromagnetic tracking systems (EMTSs) are mainly used since they enable real-time tracking of small EM sensors included in surgical tools without line-of-sight restrictions. On the other hand, these systems are very sensible to magnetic field variations that can affect sensor position estimation performance. In this paper we analyze how magnetic field variations caused by the noise of transmitting coils’ excitation currents affect system performance, and we propose a technique to reduce its undesirable effect. This method includes, in the position estimation algorithm, the measurement of excitation currents, thus compensating errors in sensor signal caused by current noise.Different simulation tests were performed to assess the proposed method which is based on modeling the magnetic field produced by the field generator (FG). Finally, it is validated by using experimental data provided by a novel EMTS prototype, obtaining noise peaks reduction and an overall mean position error of 3 mm at a distance of 600 mm from the FG.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"26 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":"132741606","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}
Pub Date : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478765
Xingxing Liu, Wenxiang Deng, Yang Liu
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.
{"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}
Pub Date : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478695
Ashi Agarwal, Bruce Wallace, L. Ault, J. Larivière-Chartier, F. Knoefel, R. Goubran, J. Kaye, Z. Beattie, N. Thomas
With the aging of the population in Canada and elsewhere, applications of Smart Homes for well-being sensing are increasingly being considered in health care. Many of these smart home networks rely on the Zigbee wireless protocol to connect sensors used to measure various health outcomes. This paper provides preliminary results of gait estimation performed on 3 different residences over 11 months using Zigbee connected motion sensors, with a focus on understanding accuracy limitations induced by the Zigbee communication protocol. The accuracy limitations were also observed in the results from a controlled experiment done with 2 different sets of Zigbee motion sensors. This paper provides an in-depth analysis on root cause of variance in gait estimation at the same time laying out conservative variance estimations caused by different scenarios. The accuracy considerations highlighted by the paper are also applicable for all other time sensitive measures. Results of this paper necessitate further analysis of the use of Zigbee operated sensor networks in the evaluation of time sensitive measures.
{"title":"Using Zigbee Sensors for Ambient Measurement of Human Gait – Analytical Considerations","authors":"Ashi Agarwal, Bruce Wallace, L. Ault, J. Larivière-Chartier, F. Knoefel, R. Goubran, J. Kaye, Z. Beattie, N. Thomas","doi":"10.1109/MeMeA52024.2021.9478695","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478695","url":null,"abstract":"With the aging of the population in Canada and elsewhere, applications of Smart Homes for well-being sensing are increasingly being considered in health care. Many of these smart home networks rely on the Zigbee wireless protocol to connect sensors used to measure various health outcomes. This paper provides preliminary results of gait estimation performed on 3 different residences over 11 months using Zigbee connected motion sensors, with a focus on understanding accuracy limitations induced by the Zigbee communication protocol. The accuracy limitations were also observed in the results from a controlled experiment done with 2 different sets of Zigbee motion sensors. This paper provides an in-depth analysis on root cause of variance in gait estimation at the same time laying out conservative variance estimations caused by different scenarios. The accuracy considerations highlighted by the paper are also applicable for all other time sensitive measures. Results of this paper necessitate further analysis of the use of Zigbee operated sensor networks in the evaluation of time sensitive measures.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"73 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":"123476360","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}
Pub Date : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478697
Roberta Renati, N. S. Bonfiglio, Ludovica Patrone, D. Rollo, M. P. Penna
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.
{"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}
Pub Date : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478593
P. Arpaia, E. D. Benedetto, N. Donato, Luigi Duraccio, N. Moccaldi
A real-time monitoring system based on Augmented Reality (AR) and highly wearable Brain-Computer Interface (BCI) for hands-free visualization of patient’s health in Operating Room (OR) is proposed. The system is designed to allow the anesthetist to monitor hands-free and in real-time the patient’s vital signs collected from the electromedical equipment available in OR. After the analysis of the requirements in a typical Health 4.0 scenario, the conceptual design, implementation and experimental validation of the proposed system are described in detail. The effectiveness of the proposed AR-BCI-based real-time monitoring system was demonstrated through an experimental activity was carried out at the University Hospital Federico II (Naples, Italy), using operating room equipment.
{"title":"A Wearable SSVEP BCI for AR-based, Real-time Monitoring Applications","authors":"P. Arpaia, E. D. Benedetto, N. Donato, Luigi Duraccio, N. Moccaldi","doi":"10.1109/MeMeA52024.2021.9478593","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478593","url":null,"abstract":"A real-time monitoring system based on Augmented Reality (AR) and highly wearable Brain-Computer Interface (BCI) for hands-free visualization of patient’s health in Operating Room (OR) is proposed. The system is designed to allow the anesthetist to monitor hands-free and in real-time the patient’s vital signs collected from the electromedical equipment available in OR. After the analysis of the requirements in a typical Health 4.0 scenario, the conceptual design, implementation and experimental validation of the proposed system are described in detail. The effectiveness of the proposed AR-BCI-based real-time monitoring system was demonstrated through an experimental activity was carried out at the University Hospital Federico II (Naples, Italy), using operating room equipment.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"19 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":"116246667","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}
Pub Date : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478715
Hamed Tabrizchi, A. Mosavi, Z. Vámossy, A. Várkonyi-Kóczy
Since the beginning of the coronavirus disease (COVID-19) pandemic several machine learning and deep learning methods had been introduced to detect the infected patients using the X-Ray or CT scan images. Numerous sophisticated data-driven methods had been introduced to improve the performance and the accuracy of the diagnosis models. This paper proposes an improved densely connected convolutional networks (DenseNet) method based on transfer learning (TL) to enhance the model performance. The results show promising model accuracy.
{"title":"Densely Connected Convolutional Networks (DenseNet) for Diagnosing Coronavirus Disease (COVID-19) from Chest X-ray Imaging","authors":"Hamed Tabrizchi, A. Mosavi, Z. Vámossy, A. Várkonyi-Kóczy","doi":"10.1109/MeMeA52024.2021.9478715","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478715","url":null,"abstract":"Since the beginning of the coronavirus disease (COVID-19) pandemic several machine learning and deep learning methods had been introduced to detect the infected patients using the X-Ray or CT scan images. Numerous sophisticated data-driven methods had been introduced to improve the performance and the accuracy of the diagnosis models. This paper proposes an improved densely connected convolutional networks (DenseNet) method based on transfer learning (TL) to enhance the model performance. The results show promising model accuracy.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"22 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":"116403475","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：