Pub Date : 2022-09-27DOI: 10.1109/BHI56158.2022.9926805
L. Paletta, M. Pszeida, M. Schneeberger, Amir Dini, Lilian Reim, W. Kallus
First responders engage in highly stressful situations at the emergency site. Maintaining cognitive control under these circumstances is a necessary condition to perform efficient decision making for the purpose of own health and to pursue mission objectives. We are aiming at developing biosensor-based decision support for risk stratification on cognitive readiness of first responders at the mission site. In a first development stage, an exploratory pilot study was performed to test a formalized reporting schema applying equivalent stress in real, non-immersive and fully immersive training environments. Wearable psychophysiological measurement technology was applied to estimate the cognitive-emotional stress level under both training conditions. In this work we particularly focus on the potential of predicting the risk level for failures in situation awareness from digital analysis of cognitive-emotional stress. The results provide statistically significant indications for risk stratification of cognitive readiness based on situation awareness theory.
{"title":"Cognitive-emotional Stress and Risk Stratification of Situational Awareness in Immersive First Responder Training","authors":"L. Paletta, M. Pszeida, M. Schneeberger, Amir Dini, Lilian Reim, W. Kallus","doi":"10.1109/BHI56158.2022.9926805","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926805","url":null,"abstract":"First responders engage in highly stressful situations at the emergency site. Maintaining cognitive control under these circumstances is a necessary condition to perform efficient decision making for the purpose of own health and to pursue mission objectives. We are aiming at developing biosensor-based decision support for risk stratification on cognitive readiness of first responders at the mission site. In a first development stage, an exploratory pilot study was performed to test a formalized reporting schema applying equivalent stress in real, non-immersive and fully immersive training environments. Wearable psychophysiological measurement technology was applied to estimate the cognitive-emotional stress level under both training conditions. In this work we particularly focus on the potential of predicting the risk level for failures in situation awareness from digital analysis of cognitive-emotional stress. The results provide statistically significant indications for risk stratification of cognitive readiness based on situation awareness theory.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124267306","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 : 2022-09-27DOI: 10.1109/BHI56158.2022.9926824
Michail Sarafidis, G. Lambrou, G. Matsopoulos, D. Koutsouris
Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high socioeconomic impact. BCa can manifest in the form of nonaggressive and usually non-muscle invasive (NMIBC) tumors that recur and require chronic invasive surveillance, or aggressive and muscle invasive (MIBC) tumors with high associated mortality. These two subtypes exhibit distinct prognosis and require different therapeutic approaches. In the present study, we conducted an integrative bioinformatics analysis, combining transcriptomic data from various microarray experiments, in order to reveal a common signature of differentially expressed genes (DEGs) between the two subtypes. Subsequently, we constructed the protein-protein interaction (PPI) network of the DEGs and defined the hub genes based on 11 topological analysis methods. Then, the most significant hub genes were identified using LASSO logistic regression algorithm. The selected genes were finally used as features in supervised classification algorithms, namely support vector machines and random forests, for BCa subtype discrimination. The models' evaluation showed area under the curve (AUC) values up to 96% as regards separating NMIBC from MIBC tumors. Genes driving the separation between tumor subtypes may prove to be important biomarkers for BCa development and progression, and eventually candidates for therapeutic targeting.
{"title":"Integrative Bioinformatics Analysis of Transcriptomic Data Reveals Hub Genes as Diagnostic Biomarkers for Non-Muscle vs. Muscle Invasive Bladder Cancer","authors":"Michail Sarafidis, G. Lambrou, G. Matsopoulos, D. Koutsouris","doi":"10.1109/BHI56158.2022.9926824","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926824","url":null,"abstract":"Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high socioeconomic impact. BCa can manifest in the form of nonaggressive and usually non-muscle invasive (NMIBC) tumors that recur and require chronic invasive surveillance, or aggressive and muscle invasive (MIBC) tumors with high associated mortality. These two subtypes exhibit distinct prognosis and require different therapeutic approaches. In the present study, we conducted an integrative bioinformatics analysis, combining transcriptomic data from various microarray experiments, in order to reveal a common signature of differentially expressed genes (DEGs) between the two subtypes. Subsequently, we constructed the protein-protein interaction (PPI) network of the DEGs and defined the hub genes based on 11 topological analysis methods. Then, the most significant hub genes were identified using LASSO logistic regression algorithm. The selected genes were finally used as features in supervised classification algorithms, namely support vector machines and random forests, for BCa subtype discrimination. The models' evaluation showed area under the curve (AUC) values up to 96% as regards separating NMIBC from MIBC tumors. Genes driving the separation between tumor subtypes may prove to be important biomarkers for BCa development and progression, and eventually candidates for therapeutic targeting.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"401 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131916932","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 : 2022-09-27DOI: 10.1109/BHI56158.2022.9926941
Juan Manuel Vargas Garcia, M. Bahloul, T. Laleg‐Kirati
Carotid-to-femoral pulse wave velocity (cf-PWV) is a critical biomarker for evaluating arterial stiffness and cardiovascular risk. Monitoring cf-PWV is essential for cardiovascular disease diagnosis and prediction. However, the complexity during the measurement process of cf-PWV makes it prone to present errors and inaccuracies. For this reason, a learning-based non-invasive measurement of cf-PWV using peripheral signals could overcome some of the difficulties presented in the classical measurement process and improve the quality of the estimation. In this paper, a spectrogram-based machine learning model obtained from the photoplethysmogram (PPG) waveform is proposed for the estimation of the cf-PWV. For this purpose, two machine learning models have been developed using three different types of features. The first category is based on an adaptive signal processing method called Semi-Classical Signal Analysis (SCSA) that relies on the spectral problem of the Schrodinger operator; the second type proposed is energy texture-based, and the third is the statistical texture representation. Finally, the training and testing datasets were extracted from in-silico, publicly available pulse waves and hemodynamics data. The obtained results provide evidence for the feasibility and robustness of the spectrogram to transform the signals into an image and machine learning method as a tool for estimating the cf-PWV.
{"title":"Spectrogram Image-based Machine Learning Model for Carotid-to-Femoral Pulse Wave Velocity Estimation Using PPG Signal","authors":"Juan Manuel Vargas Garcia, M. Bahloul, T. Laleg‐Kirati","doi":"10.1109/BHI56158.2022.9926941","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926941","url":null,"abstract":"Carotid-to-femoral pulse wave velocity (cf-PWV) is a critical biomarker for evaluating arterial stiffness and cardiovascular risk. Monitoring cf-PWV is essential for cardiovascular disease diagnosis and prediction. However, the complexity during the measurement process of cf-PWV makes it prone to present errors and inaccuracies. For this reason, a learning-based non-invasive measurement of cf-PWV using peripheral signals could overcome some of the difficulties presented in the classical measurement process and improve the quality of the estimation. In this paper, a spectrogram-based machine learning model obtained from the photoplethysmogram (PPG) waveform is proposed for the estimation of the cf-PWV. For this purpose, two machine learning models have been developed using three different types of features. The first category is based on an adaptive signal processing method called Semi-Classical Signal Analysis (SCSA) that relies on the spectral problem of the Schrodinger operator; the second type proposed is energy texture-based, and the third is the statistical texture representation. Finally, the training and testing datasets were extracted from in-silico, publicly available pulse waves and hemodynamics data. The obtained results provide evidence for the feasibility and robustness of the spectrogram to transform the signals into an image and machine learning method as a tool for estimating the cf-PWV.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"PP 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126415248","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 : 2022-09-27DOI: 10.1109/BHI56158.2022.9926894
Siyue Song, Tianhua Chen, G. Antoniou
Currently, how to make a concrete and correct disease prediction is a popular research trend. Researchers made more efforts to develop various models to provide interpretations of medical area, however, there is still lack of human understandable explanations provided due to the non-transparency structure of some machine learning and deep learning models. According to this work, there is one combined model application we would like to adopt. After comparison experiments of classification and interpretation, it is found the combination model can address the issues from the latest interpretation models, and try to improve the trustworthiness of medical text interpretations.
{"title":"Improve the trustwortiness of medical text interpretations","authors":"Siyue Song, Tianhua Chen, G. Antoniou","doi":"10.1109/BHI56158.2022.9926894","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926894","url":null,"abstract":"Currently, how to make a concrete and correct disease prediction is a popular research trend. Researchers made more efforts to develop various models to provide interpretations of medical area, however, there is still lack of human understandable explanations provided due to the non-transparency structure of some machine learning and deep learning models. According to this work, there is one combined model application we would like to adopt. After comparison experiments of classification and interpretation, it is found the combination model can address the issues from the latest interpretation models, and try to improve the trustworthiness of medical text interpretations.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121748223","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 : 2022-09-27DOI: 10.1109/BHI56158.2022.9926777
Elvin Johnson, Shreshta Mohan, Alex Gaudio, A. Smailagic, C. Faloutsos, A. Campilho
Advances in data-driven deep learning for chest X-ray image analysis underscore the need for explainability, privacy, large datasets and significant computational resources. We frame privacy and explainability as a lossy single-image compression problem to reduce both computational and data requirements without training. For Cardiomegaly detection in chest X-ray images, we propose HeartSpot and four spatial bias priors. HeartSpot priors define how to sample pixels based on domain knowledge from medical literature and from machines. HeartSpot privatizes chest X-ray images by discarding up to 97% of pixels, such as those that reveal the shape of the thoracic cage, bones, small lesions and other sensitive features. HeartSpot priors are ante-hoc explainable and give a human-interpretable image of the preserved spatial features that clearly outlines the heart. HeartSpot offers strong compression, with up to 32x fewer pixels and 11 $x$ smaller filesize. Cardiomegaly detectors using HeartSpot are up to 9x faster to train or at least as accurate (up to +.01 AUC ROC) when compared to a baseline DenseNet121. HeartSpot is post-hoc explainable by re-using existing attribution methods without requiring access to the original non-privatized image. In summary, HeartSpot improves speed and accuracy, reduces image size, improves privacy and ensures explainability.
{"title":"HeartSpot: Privatized and Explainable Data Compression for Cardiomegaly Detection","authors":"Elvin Johnson, Shreshta Mohan, Alex Gaudio, A. Smailagic, C. Faloutsos, A. Campilho","doi":"10.1109/BHI56158.2022.9926777","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926777","url":null,"abstract":"Advances in data-driven deep learning for chest X-ray image analysis underscore the need for explainability, privacy, large datasets and significant computational resources. We frame privacy and explainability as a lossy single-image compression problem to reduce both computational and data requirements without training. For Cardiomegaly detection in chest X-ray images, we propose HeartSpot and four spatial bias priors. HeartSpot priors define how to sample pixels based on domain knowledge from medical literature and from machines. HeartSpot privatizes chest X-ray images by discarding up to 97% of pixels, such as those that reveal the shape of the thoracic cage, bones, small lesions and other sensitive features. HeartSpot priors are ante-hoc explainable and give a human-interpretable image of the preserved spatial features that clearly outlines the heart. HeartSpot offers strong compression, with up to 32x fewer pixels and 11 $x$ smaller filesize. Cardiomegaly detectors using HeartSpot are up to 9x faster to train or at least as accurate (up to +.01 AUC ROC) when compared to a baseline DenseNet121. HeartSpot is post-hoc explainable by re-using existing attribution methods without requiring access to the original non-privatized image. In summary, HeartSpot improves speed and accuracy, reduces image size, improves privacy and ensures explainability.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124874105","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 : 2022-09-27DOI: 10.1109/BHI56158.2022.9926944
Nagaraj Hegde, T. Swibas, E. Melanson, E. Sazonov
In this work we developed and validated a method to capture the activities of daily living (ADL), transitions between ADL, and the associated Energy Expenditure (EE) using a novel insole based wearable system (SmartStep). A 15-participant study was conducted in a controlled laboratory environment while participants wore the SmartStep and performed various ADL. Machine learning models were developed using 4-branched and 8-branched steady-state activities to estimate the total energy expenditure (TEE) and physical activity energy expenditure (PAEE). Additional models accounting for transitions between activities were also developed. These models were validated in an independent study with 8-participants, performed in a whole room indirect calorimeter. In the controlled study, the 8-branched models had a lower root mean square error (RMSE, 0.58 vs. 0.67 kcal/min) and lower total error (−1.5% vs. 3%). In the validation study, the 8-branched models also had a lower RMSE (0.9 kcal/min vs. 1.2 kcal/min) and lower total error (−4.5% vs 11%). Accounting for activity transitions reduced the total error in the EE estimation to −1.3%. The results suggested that SmartStep can be used to accurately monitor the EE of the wearers in their daily living. The validation study results suggested that 8-branched models more accurately predict EE than 4-branched models and that accounting for activity transitions improves the estimation of EE in daily living.
在这项工作中，我们开发并验证了一种方法来捕捉日常生活活动(ADL)， ADL之间的转换，以及相关的能量消耗(EE)使用一种新型的鞋垫可穿戴系统(SmartStep)。一项15名参与者的研究在受控的实验室环境中进行，参与者佩戴SmartStep并进行各种ADL。利用4支和8支稳态活动建立了机器学习模型，以估计总能量消耗(TEE)和身体活动能量消耗(PAEE)。另外还开发了考虑活动之间转换的其他模型。这些模型在一项有8名参与者的独立研究中得到了验证，该研究在整个房间的间接量热计中进行。在对照研究中，8支模型具有较低的均方根误差(RMSE, 0.58 vs. 0.67 kcal/min)和较低的总误差(- 1.5% vs. 3%)。在验证研究中，8支模型也具有较低的RMSE (0.9 kcal/min vs. 1.2 kcal/min)和较低的总误差(- 4.5% vs. 11%)。考虑活动转换将EE估计的总误差降低到- 1.3%。结果表明，SmartStep可以用来准确地监测佩戴者在日常生活中的情感表达。验证研究结果表明，8支模型比4支模型更准确地预测情感表达，并且考虑活动转换可以改善对日常生活中情感表达的估计。
{"title":"Development and Independent Validation of Energy Expenditure Models Using SmartStep","authors":"Nagaraj Hegde, T. Swibas, E. Melanson, E. Sazonov","doi":"10.1109/BHI56158.2022.9926944","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926944","url":null,"abstract":"In this work we developed and validated a method to capture the activities of daily living (ADL), transitions between ADL, and the associated Energy Expenditure (EE) using a novel insole based wearable system (SmartStep). A 15-participant study was conducted in a controlled laboratory environment while participants wore the SmartStep and performed various ADL. Machine learning models were developed using 4-branched and 8-branched steady-state activities to estimate the total energy expenditure (TEE) and physical activity energy expenditure (PAEE). Additional models accounting for transitions between activities were also developed. These models were validated in an independent study with 8-participants, performed in a whole room indirect calorimeter. In the controlled study, the 8-branched models had a lower root mean square error (RMSE, 0.58 vs. 0.67 kcal/min) and lower total error (−1.5% vs. 3%). In the validation study, the 8-branched models also had a lower RMSE (0.9 kcal/min vs. 1.2 kcal/min) and lower total error (−4.5% vs 11%). Accounting for activity transitions reduced the total error in the EE estimation to −1.3%. The results suggested that SmartStep can be used to accurately monitor the EE of the wearers in their daily living. The validation study results suggested that 8-branched models more accurately predict EE than 4-branched models and that accounting for activity transitions improves the estimation of EE in daily living.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116880592","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 : 2022-09-27DOI: 10.1109/BHI56158.2022.9926804
Katharina M. Jaeger, Michael Nissen, R. Richer, Simone Rahm, Adriana Titzmann, P. Fasching, Bjoern M. Eskofier, Heike Leutheuser
Preterm births account for more than 10 % of all newborns. An adverse fetal presentation is a risk factor for intrapartum and neonatal mortality. To date, no technology enables a longitudinal, ubiquitous, and unobtrusive monitoring of fetal presentation. This study presents a first approach to fetal orientation detection based on non-invasive fetal electrocardiography (NI-fECG) using the non-invasive multi-modal foetal ECG-Doppler data set for antenatal cardiology research. The data set contains 60 recordings from 39 pregnant women (21–27 weeks), including NI-fECG and ultrasound position ground truth. We evaluated both handcrafted and generic features for five different classifiers (k-Nearest-Neighbor, Decision Tree Classifier, Support Vector Classification, AdaBoost Classifier, and Multilayer Perceptron) using cross-validation on subject splits on a cleaned subset. Best results for the distinction between vertex (head down) and breech (head up) were achieved using an AdaBoost classifier with a balanced accuracy of 86.5 ± 15.0 %. With this work, we take a first step towards longitudinal fetal presentation monitoring, which contributes to a better understanding of reduced fetal movements and extends the potential applications of NI-fECG in prenatal care. In future work, we will expand our classification system to detect more detailed fetal presentations using a newly created data set.
早产占所有新生儿的10%以上。不良胎儿呈现是产时和新生儿死亡的危险因素。到目前为止，还没有一种技术能够对胎儿的表现进行纵向的、无所不在的、不显眼的监测。本研究提出了一种基于无创胎儿心电图(NI-fECG)的胎儿取向检测方法，该方法使用无创多模态胎儿心电图多普勒数据集用于产前心脏病学研究。数据集包含39名孕妇(21-27周)的60条记录，包括NI-fECG和超声位置地面真实值。我们对五种不同分类器(k-Nearest-Neighbor, Decision Tree Classifier, Support Vector Classification, AdaBoost Classifier和Multilayer Perceptron)的手工特征和通用特征进行了评估，并对清理后的子集上的主题分割进行了交叉验证。使用AdaBoost分类器区分顶点(头部向下)和后臀(头部向上)的最佳结果，平衡精度为86.5±15.0%。通过这项工作，我们向纵向胎儿呈现监测迈出了第一步，这有助于更好地了解胎儿运动减少，并扩展NI-fECG在产前护理中的潜在应用。在未来的工作中，我们将扩展我们的分类系统，使用新创建的数据集来检测更详细的胎儿表现。
{"title":"Machine Learning-based Detection of In-Utero Fetal Presentation from Non-Invasive Fetal ECG","authors":"Katharina M. Jaeger, Michael Nissen, R. Richer, Simone Rahm, Adriana Titzmann, P. Fasching, Bjoern M. Eskofier, Heike Leutheuser","doi":"10.1109/BHI56158.2022.9926804","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926804","url":null,"abstract":"Preterm births account for more than 10 % of all newborns. An adverse fetal presentation is a risk factor for intrapartum and neonatal mortality. To date, no technology enables a longitudinal, ubiquitous, and unobtrusive monitoring of fetal presentation. This study presents a first approach to fetal orientation detection based on non-invasive fetal electrocardiography (NI-fECG) using the non-invasive multi-modal foetal ECG-Doppler data set for antenatal cardiology research. The data set contains 60 recordings from 39 pregnant women (21–27 weeks), including NI-fECG and ultrasound position ground truth. We evaluated both handcrafted and generic features for five different classifiers (k-Nearest-Neighbor, Decision Tree Classifier, Support Vector Classification, AdaBoost Classifier, and Multilayer Perceptron) using cross-validation on subject splits on a cleaned subset. Best results for the distinction between vertex (head down) and breech (head up) were achieved using an AdaBoost classifier with a balanced accuracy of 86.5 ± 15.0 %. With this work, we take a first step towards longitudinal fetal presentation monitoring, which contributes to a better understanding of reduced fetal movements and extends the potential applications of NI-fECG in prenatal care. In future work, we will expand our classification system to detect more detailed fetal presentations using a newly created data set.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130321269","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 : 2022-09-27DOI: 10.1109/BHI56158.2022.9926945
J. López-Correa, Caroline König, A. Vellido
G protein-coupled receptors are a large super-family of cell membrane proteins that play an important physiological role as transmitters of extra-cellular signals. Signal transmission through the cell membrane depends on the conformational changes of the transmembrane region of the receptor and the investigation of the dynamics in these regions is therefore key. Molecular Dynamics (MD) simulations can provide information of the receptor conformational states at the atom level and machine learning (ML) methods can be useful for the analysis of these data. In this paper, Recurrent Neural Networks (RNNs) are used to evaluate whether the MD can be modeled focusing on the different regions of the receptor (intra-cellular, extra-cellular and each transmembrane regions (TM)). The best results, as measured by root-mean-square deviation (RMSD), are 0.1228 Å for TM4 of the 2rh1 (inactive state) and 0.1325 Å for TM4 of the 3p0g (active state), which are comparable to the state-of-the-art in non-dynamic 3-D predictions, showing the potential of the proposed approach.
{"title":"Molecular Dynamics forecasting of transmembrane Regions in GPRCs by Recurrent Neural Networks","authors":"J. López-Correa, Caroline König, A. Vellido","doi":"10.1109/BHI56158.2022.9926945","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926945","url":null,"abstract":"G protein-coupled receptors are a large super-family of cell membrane proteins that play an important physiological role as transmitters of extra-cellular signals. Signal transmission through the cell membrane depends on the conformational changes of the transmembrane region of the receptor and the investigation of the dynamics in these regions is therefore key. Molecular Dynamics (MD) simulations can provide information of the receptor conformational states at the atom level and machine learning (ML) methods can be useful for the analysis of these data. In this paper, Recurrent Neural Networks (RNNs) are used to evaluate whether the MD can be modeled focusing on the different regions of the receptor (intra-cellular, extra-cellular and each transmembrane regions (TM)). The best results, as measured by root-mean-square deviation (RMSD), are 0.1228 Å for TM4 of the 2rh1 (inactive state) and 0.1325 Å for TM4 of the 3p0g (active state), which are comparable to the state-of-the-art in non-dynamic 3-D predictions, showing the potential of the proposed approach.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123672065","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 : 2022-09-27DOI: 10.1109/BHI56158.2022.9926791
Daniel Lopes Soares Lima, A. Pessoa, A. C. D. Paiva, António Cunha, Geraldo Braz Júnior, J. Almeida
Cancers related to the gastrointestinal tract have a high incidence rate in the population, with a high mortality rate. Videos obtained through endoscopic capsules are essential for evaluating anomalies that can progress to cancer. However, due to their duration, which can reach 10 hours, they demand great attention from the medical specialist in their analysis. Machine learning techniques have been successfully applied in developing computer-aided diagnostic systems since the 1990s, where Convolutional Neural Networks (CNNs) have become very successful for pattern recognition in images. CNNs use convolutions to extract features from the analyzed data, operating in a fixed-size window and thus having problems capturing pixel-level relationships considering the spatial and temporal domains. Otherwise, transformers use attention mechanisms, where data is structured in a vector space that can aggregate information from adjacent data to determine meaning in a given context. This work proposes a computational method for analyzing images extracted from videos obtained by endoscopic capsules, using a transformer-based model that helps diagnose of gastrointestinal tract abnormalities. Preliminary results are promising. The classification task of 11 classes evaluated on the publicly available Kvasir-Capsule dataset yielded an average value of 99.70% of accuracy, 99.64% of precision, 99.86% of sensitivity, and 99.54% of f1-score.
{"title":"Classification of Video Capsule Endoscopy Images Using Visual Transformers","authors":"Daniel Lopes Soares Lima, A. Pessoa, A. C. D. Paiva, António Cunha, Geraldo Braz Júnior, J. Almeida","doi":"10.1109/BHI56158.2022.9926791","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926791","url":null,"abstract":"Cancers related to the gastrointestinal tract have a high incidence rate in the population, with a high mortality rate. Videos obtained through endoscopic capsules are essential for evaluating anomalies that can progress to cancer. However, due to their duration, which can reach 10 hours, they demand great attention from the medical specialist in their analysis. Machine learning techniques have been successfully applied in developing computer-aided diagnostic systems since the 1990s, where Convolutional Neural Networks (CNNs) have become very successful for pattern recognition in images. CNNs use convolutions to extract features from the analyzed data, operating in a fixed-size window and thus having problems capturing pixel-level relationships considering the spatial and temporal domains. Otherwise, transformers use attention mechanisms, where data is structured in a vector space that can aggregate information from adjacent data to determine meaning in a given context. This work proposes a computational method for analyzing images extracted from videos obtained by endoscopic capsules, using a transformer-based model that helps diagnose of gastrointestinal tract abnormalities. Preliminary results are promising. The classification task of 11 classes evaluated on the publicly available Kvasir-Capsule dataset yielded an average value of 99.70% of accuracy, 99.64% of precision, 99.86% of sensitivity, and 99.54% of f1-score.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128764777","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 : 2022-09-27DOI: 10.1109/BHI56158.2022.9926956
D. Petsani, E. Konstantinidis, Michalis Timoleon, Nicholaos Athanasopoulos, Georgios Nikolaos Tsakonas, S. Nifakos, Natalia Stathakarou, M. Doumas, P. Bamidis
Healthcare continuity and remote care are among the key components for tackling disease-related effects using technological solutions. People recovering from home need high-quality of care and timely monitoring, resembling hospital care. This study proposes the use of a new device for person - machine interaction for home monitoring. The system takes advantage of automatic interaction initiated by the device on detecting patients' symptoms and providing remote care in order to improve technology engagement features. The feasibility of the proposed system was tested in COVID-19 patients as a definitive case of stay-at-home care where the treatment depends on the current state of health and the severity of the symptoms. The study shows promising results in terms of usability. The vast majority of the answers are perceiving the system as useful (90.9%) and easy to use (95.5%) and the overall System Usability Score (SUS) of the system is 65.25. The system usage adherence was also promising for the quarantine period (on average 7.2 days) but dropped after that. However, the results from the clinical team interviews showed that there is a need for sufficient allocated time for clinicians to get acquainted with the system and for ED staff to explain the device to patients.
{"title":"Towards acceptable emerging technologies for homemonitoring and care: a feasibility study with COVID-19 patients","authors":"D. Petsani, E. Konstantinidis, Michalis Timoleon, Nicholaos Athanasopoulos, Georgios Nikolaos Tsakonas, S. Nifakos, Natalia Stathakarou, M. Doumas, P. Bamidis","doi":"10.1109/BHI56158.2022.9926956","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926956","url":null,"abstract":"Healthcare continuity and remote care are among the key components for tackling disease-related effects using technological solutions. People recovering from home need high-quality of care and timely monitoring, resembling hospital care. This study proposes the use of a new device for person - machine interaction for home monitoring. The system takes advantage of automatic interaction initiated by the device on detecting patients' symptoms and providing remote care in order to improve technology engagement features. The feasibility of the proposed system was tested in COVID-19 patients as a definitive case of stay-at-home care where the treatment depends on the current state of health and the severity of the symptoms. The study shows promising results in terms of usability. The vast majority of the answers are perceiving the system as useful (90.9%) and easy to use (95.5%) and the overall System Usability Score (SUS) of the system is 65.25. The system usage adherence was also promising for the quarantine period (on average 7.2 days) but dropped after that. However, the results from the clinical team interviews showed that there is a need for sufficient allocated time for clinicians to get acquainted with the system and for ED staff to explain the device to patients.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126442049","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}
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