2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG)最新文献_第2页

Prototype of a medical device for measuring intravaginal forces 一种用于测量阴道内力的医疗装置的原型

2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG) Pub Date : 2023-06-22 DOI: 10.1109/ENBENG58165.2023.10175325

F. Pinheiro, M. E. Silva, A. Fernandes

引用次数: 0

Towards a Smart-Vest for Forward Posture Monitoring: Improving Usability with E-Textiles 面向向前姿态监测的智能背心:提高电子纺织品的可用性

2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG) Pub Date : 2023-06-22 DOI: 10.1109/ENBENG58165.2023.10175331

João T. Martins, Sara M. Cerqueira, Alexandre Silva, A. Catarino, Ana Rocha, C. Santos

引用次数: 0

Comparison of Analytical Methods Of Serum Untargeted Metabolomics 血清非靶向代谢组学分析方法的比较

2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG) Pub Date : 2023-06-22 DOI: 10.1109/ENBENG58165.2023.10175339

Tiago A H Fonseca, M. Oliveira, Rúben Araújo, Luís Bento, Cristiana P Von Rekowski, G. Justino, C. Calado

{"title":"Comparison of Analytical Methods Of Serum Untargeted Metabolomics","authors":"Tiago A H Fonseca, M. Oliveira, Rúben Araújo, Luís Bento, Cristiana P Von Rekowski, G. Justino, C. Calado","doi":"10.1109/ENBENG58165.2023.10175339","DOIUrl":"https://doi.org/10.1109/ENBENG58165.2023.10175339","url":null,"abstract":"Metabolomics has emerged as a powerful tool in the discovery of new biomarkers for medical diagnosis and prognosis. However, there are numerous challenges, such as the methods used to characterize the system metabolome. In the present work, the comparison of two analytical platforms to acquire the serum metabolome of critically ill patients was conducted. The untargeted serum metabolome analysis by ultraperformance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) enabled to identify a set of metabolites statistically different between deceased and discharged patients. This set of metabolites also enabled to develop a very good predictive model, based on linear discriminant analysis (LDA) with a sensitivity and specificity of 80% and 100%, respectively. Fourier Transform Infrared (FTIR) spectroscopy was also applied in a high-throughput, simple and rapid mode to analyze the serum metabolome. Despite this technique not enabling the identification of metabolites, it allowed to identify molecular fingerprints associated to each patient group, while leading to a good predictive model, based on principal component analysis-LDA, with a sensitivity and specificity of 100% and 90%, respectively. Therefore, both analytical techniques presented complementary characteristics, that should be further explored for metabolome characterization and application as for biomarkers discovery for medical diagnosis and prognosis.","PeriodicalId":125330,"journal":{"name":"2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114141501","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

Reliability analysis of an Electronic Portal Imaging Device (EPID) of a linear accelerator 直线加速器电子传送门成像装置(EPID)可靠性分析

2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG) Pub Date : 2023-06-22 DOI: 10.1109/ENBENG58165.2023.10175354

Clésia Gouveia, J. Sobral

引用次数: 0

Measurement of paracetamol concentration using a fiber laser system 用光纤激光系统测量扑热息痛浓度

2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG) Pub Date : 2023-06-22 DOI: 10.1109/ENBENG58165.2023.10175344

Liliana Soares, R. Pérez-Herrera, S. Novais, António Ferreira, O. Frazão, Susana O. Silva

引用次数: 0

Epileptic seizure prediction using EEG peripheral channels 利用脑电图外周通道预测癫痫发作

2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG) Pub Date : 2023-06-22 DOI: 10.1109/ENBENG58165.2023.10175347

Carolina Salvador, Virginie Felizardo, Henriques Zacarias, Leonice Souza-Pereira, Mehran Pourvahab, Nuno Pombo, N. Garcia

引用次数: 1

Deep Learning for Segmentation of the Left Ventricle in Echocardiography 超声心动图中左心室分割的深度学习

2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG) Pub Date : 2023-06-22 DOI: 10.1109/ENBENG58165.2023.10175322

Sofia Ferraz, Miguel Coimbra, J. Pedrosa

{"title":"Deep Learning for Segmentation of the Left Ventricle in Echocardiography","authors":"Sofia Ferraz, Miguel Coimbra, J. Pedrosa","doi":"10.1109/ENBENG58165.2023.10175322","DOIUrl":"https://doi.org/10.1109/ENBENG58165.2023.10175322","url":null,"abstract":"Two-dimensional echocardiography is the most widely used non-invasive imaging modality due to its fast acquisition time, low cost, and high temporal resolution. Accurate segmentation of the left ventricle in echocardiography is vital for ensuring the accuracy of subsequent diagnosis. Currently, numerous efforts have been made to automatize this task and various public datasets have been released in recent decades to further develop present research. However, medical datasets acquired at different institutions have inherent bias caused by various confounding factors, such as operation policies, machine protocols, treatment preference, etc. As a result, models trained on one dataset, regardless of volume, cannot be confidently utilized for the others. In this study, we investigated model robustness to dataset bias using two publicly available echocardiographic datasets. This work validates the efficacy of a supervised deep learning model for left ventricle segmentation and ejection fraction prediction, outside the dataset on which it was trained. The exposure of this model to unseen, but related samples without additional training maintained a good performance. However, a performance decrease from the original results can be observed, while the impact of quality is also noteworthy with lower quality data leading to decreased performance.","PeriodicalId":125330,"journal":{"name":"2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114651022","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

Electrocardiographic Signal Quality Assessment Without Morphology Analysis 无形态学分析的心电图信号质量评估

2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG) Pub Date : 2023-06-22 DOI: 10.1109/ENBENG58165.2023.10175317

David Velez, A. Lourenco, João Costa

引用次数: 0

Towards Industrially Feasible Invisible Electrocardiography (ECG) in Sanitary Facilities 迈向工业可行的卫生设施隐形心电图(ECG)

2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG) Pub Date : 2023-06-22 DOI: 10.1109/ENBENG58165.2023.10175356

Aline dos Santos Silva, M. V. Correia, Andreia Costa, H. P. da Silva

{"title":"Towards Industrially Feasible Invisible Electrocardiography (ECG) in Sanitary Facilities","authors":"Aline dos Santos Silva, M. V. Correia, Andreia Costa, H. P. da Silva","doi":"10.1109/ENBENG58165.2023.10175356","DOIUrl":"https://doi.org/10.1109/ENBENG58165.2023.10175356","url":null,"abstract":"Previous work from our team, has proposed a novel approach to invisible electrocardiography (ECG) in sanitary facilities using polymeric electrodes, leading to the creation of a proof-of-concept system integrated in a toilet seat. However, for this approach to be industrially feasible, further optimization is needed, in particular in what concerns electrode materials compatible with injection moulding processes. In this paper we explore the use of different types of conductive materials as electrodes, aiming at industrial-scale production of a toilet seat capable of recording ECG data, without the need for body-worn devices. In addition, the effect of cleaning agents applied to the materials over time. Our approach has been evaluated comparatively with a gold standard device, for a population of 15 healthy subjects. While some of the materials did not allow adequate signal acquisition in all users, one electrically conductive compound showed the best results as per heart rate and ECG waveform morphology analysis. For the best performing compound we were able to acquire signals in 100% of the sessions, with an average heart rate deviation between the reference and experimental systems of −3.67±5.05 beats per minute (BPM). In terms of ECG waveform morphology, the best cases showed a Pearson correlation coefficient of 0.99.","PeriodicalId":125330,"journal":{"name":"2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133548405","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

ECG based quantification and modeling of physiological reactions to emotional stimuli 基于ECG的情绪刺激生理反应的量化和建模

2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG) Pub Date : 2023-06-22 DOI: 10.1109/ENBENG58165.2023.10175355

Beatriz Henriques, Susana Brás, S. Gouveia

{"title":"ECG based quantification and modeling of physiological reactions to emotional stimuli","authors":"Beatriz Henriques, Susana Brás, S. Gouveia","doi":"10.1109/ENBENG58165.2023.10175355","DOIUrl":"https://doi.org/10.1109/ENBENG58165.2023.10175355","url":null,"abstract":"Emotion recognition systems are designed to help in the identification of human emotions, being associated with learning and decision-making on daily tasks as well as treatment and diagnosis in mental health contexts. The research in this area explores different aspects ranging from the information conveyed in different physiological signals to different methods aiming feature selection and emotion classification. This work implements a dedicated experimental protocol to acquire physiological data, such as the electrocardiogram (ECG), while the participants watched videos associated with emotional stimulation to provoke reactions of fear, happiness, and neutral. Data analysis was based on ECG features, being clear that the intended stimuli effectively provoked variation in the heart rhythm and in other ECG features. In addition, each emotional stimulus presents different degrees of reactions clearly distinguished by a clustering procedure. A machine learning model developed based on Support Vector Machine achieved accuracy above 87.01% (training) and 38.40% (test). The emotion state identification was performed over the goals of this study, indicating the potential ability of electrophysiological signal processing for automatic emotion stratification, after emotional video stimulation.","PeriodicalId":125330,"journal":{"name":"2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129907076","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