2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)最新文献_第7页

Comparison of PPG and BCG Features for Camera-based Blood Pressure Estimation by Ice Water Stimulation PPG与BCG特征在冰水刺激下相机测血压中的比较

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926833

Guanghang Liao, Caifeng Shan, Wenjin Wang

{"title":"Comparison of PPG and BCG Features for Camera-based Blood Pressure Estimation by Ice Water Stimulation","authors":"Guanghang Liao, Caifeng Shan, Wenjin Wang","doi":"10.1109/BHI56158.2022.9926833","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926833","url":null,"abstract":"Non-invasive Blood Pressure (BP) measurement is highly demanded for pervasive healthcare with the development of Internet of Things, sensors and mobile technology. Camera-based Photoplethysmography (camera-PPG) has been applied for non-contact BP estimation. Most camera-PPG based approaches calculate the Pulse Transmission Time between different peripheral sites like face and palm for BP calibration, which require more than one body part to be simultaneously measured and thus introduce inconvenience to real applications. In this study, we investigate the feasibility of measuring BP from a single body site using either the forehead PPG signals or neck ballistocardiographic (BCG) motion signals. Two morphological features (K-value and Augmentation Index) that have clinical meanings for BP monitoring have been compared. The study was conducted in the ice water stimulation experiment involving 16 healthy subjects. The results show that the neck can be an attractive site for BP estimation as the neck-BCG signals show more distinct features (e.g. dicrotic wave) that have stronger correlations with BP than the forehead-PPG signals, and it eliminates the privacy issue of imaging a face. Both the K-value and Augmentation Index can well track the changes of BP. The conclusions drawn from this study inspire the selection of physiological site and features for non-contact BP estimation.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"13 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":"125801728","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

RetainEXT: Enhancing Rare Event Detection and Improving Interpretability of Health Records using Temporal Neural Networks 使用时间神经网络增强罕见事件检测和改善健康记录的可解释性

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926906

Suraj Ramchand, Gavin Tsang, Duncan Cole, Xianghua Xie

{"title":"RetainEXT: Enhancing Rare Event Detection and Improving Interpretability of Health Records using Temporal Neural Networks","authors":"Suraj Ramchand, Gavin Tsang, Duncan Cole, Xianghua Xie","doi":"10.1109/BHI56158.2022.9926906","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926906","url":null,"abstract":"A recurring theme during the pandemic was the shortage of hospital beds. Despite all efforts, the healthcare system still faces 25 % of resource strain felt during the first peak of coronavirus. Digitisation of Electronic Healthcare Records (EHRs) and the pandemic have brought about many successful applications of Recurrent Neural Networks (RNNs) to predict patients' current and future states. Despite their strong per-formance, it remains a challenge for users to delve into the black box which has heavily influenced researchers to utilise more interpretable techniques such as ID-Convolutional neural networks. Others focus on using more interpretable machine learning techniques but only achieve high performance on a select subset of patients. By collaborating with medical experts and artificial intelligence scientists, our study improves on the REverse Time AttentIoN EX model, a feature and visit level attention network, for increased interpretability and usability of RNNs in predicting COVID-19-related hospitalisations. We achieved 82.40 % area under the receiver operating characteristic curve and showcased effective use of the REverse Time AttentIoN EXTension model and EHRs in understanding how individual medical codes contribute to hospitalisation risk prediction. This study provides a guideline for researchers aiming to design interpretable temporal neural networks using the power of RNNs and data mining techniques.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"22 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":"126868058","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

Multi-label Neural Model for Prediction of Myocardial Infarction Complications with Resampling and Explainability 多标签神经模型预测心肌梗死并发症的重采样及可解释性

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926915

Munib Mesinovic, Kai-Wen Yang

{"title":"Multi-label Neural Model for Prediction of Myocardial Infarction Complications with Resampling and Explainability","authors":"Munib Mesinovic, Kai-Wen Yang","doi":"10.1109/BHI56158.2022.9926915","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926915","url":null,"abstract":"With myocardial infarctions accounting for the largest percent of cardiovascular-related deaths, the need for machine learning tools in prediction and prevention has never been clearer. Specifically, in the case of in-hospital complications following acute myocardial infarction (AMI), even with decreased in-hospital mortality rate due to improved hospital care, patients who survive the acute phase of MI remain at risk for MI-associated complications or recurrent AMI such as bundle branch blocks and angina. In this paper, we propose a multi-label framework to predict the occurrence of 5 complications following admission of 1,700 patients after suffering an AMI episode. We evaluate the models using several multi-label prediction metrics as a test of robustness of our method beating numerous other alternatives and comment on the balance of cost-effectiveness of a compact deep learning model versus shallow machine learning in the multi-label context. Our neural network outperformed 13 other algorithms across all metrics, except Hamming loss. We also implement Shapley value analysis to this multi-label problem and observe interesting behaviour such as the duration of arterial hypertension and time elapsed from the beginning of the attack to the hospital being key predictive features of lethal outcome. This framework presents a novel approach in using multi-label learning, and especially compact cost-effective deep learning, simultaneous for prediction of several AMI complications which has not been explored previously.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"75 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":"134040527","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

Vision Transformer Based COVID-19 Detection Using Chest CT-scan images 基于视觉变压器的COVID-19胸部ct扫描图像检测

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926823

P. Sahoo, S. Saha, S. Mondal, Suraj Gowda

{"title":"Vision Transformer Based COVID-19 Detection Using Chest CT-scan images","authors":"P. Sahoo, S. Saha, S. Mondal, Suraj Gowda","doi":"10.1109/BHI56158.2022.9926823","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926823","url":null,"abstract":"The fast proliferation of the coronavirus around the globe has put several countries' healthcare systems in danger of collapsing. As a result, locating and separating COVID-19-positive patients is a critical task. Deep Learning approaches were used in several computer-aided automated systems that utilized chest computed tomography (CT-scan) or X-ray images to create diagnostic tools. However, current Convolutional Neural Network (CNN) based approaches cannot capture the global context because of inherent image-specific inductive bias. These techniques also require large and labeled datasets to train the algorithm, but not many labeled COVID-19 datasets exist publicly. To mitigate the problem, we have developed a self-attention-based Vision Transformer (ViT) architecture using CT-scan. The proposed ViT model achieves an accuracy of 98.39% on the popular SARS-CoV-2 datasets, outperforming the existing state-of-the-art CNN-based models by 1%. We also provide the characteristics of CT scan images of the COVID-19-affected patients and an error analysis of the model's outcome. Our findings show that the proposed ViT-based model can be an alternative option for medical professionals for effective COVID-19 screening. The implementation details of the proposed model can be accessed at https://github.com/Pranabiitp/ViT.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"10 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":"132008240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

TrustSleepNet: A Trustable Deep Multimodal Network for Sleep Stage Classification TrustSleepNet:一个可信赖的深度多模态睡眠阶段分类网络

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926875

Guanjie Huang, Fenglong Ma

{"title":"TrustSleepNet: A Trustable Deep Multimodal Network for Sleep Stage Classification","authors":"Guanjie Huang, Fenglong Ma","doi":"10.1109/BHI56158.2022.9926875","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926875","url":null,"abstract":"Correctly classifying different sleep stages is a critical and prerequisite step in diagnosing sleep-related issues. In practice, the clinical experts must manually review the polysomnography (PSG) recordings to classify sleep stages. Such a procedure is time-consuming, laborious, and potentially prone to human subjective errors. Deep learning-based methods have been successfully adopted for automatically classifying sleep stages in recent years. However, they cannot simply say “I do not know” when they are uncertain in their predictions, which may easily create significant risk in clinical applications, despite their good performance. To address this issue, we propose a deep model, named TrustSleepNet, which contains evidential learning and cross-modality attention modules. Evidential learning predicts the probability density of the classes, which can learn an uncertainty score and make the prediction trustable in real-world clinical applications. Cross-modality attention adaptively fuses multimodal PSG data by enhancing the significant ones and suppressing irrelevant ones. Experimental results demonstrate that TrustSleepNet outperforms state-of-the-art benchmark methods, and the uncertainty score makes the prediction more trustable and reliable.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"21 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":"133686924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid approach based on dynamic trajectories to predict mortality in COVID-19 patients upon steroids administration 基于动态轨迹预测类固醇治疗后COVID-19患者死亡率的混合方法

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926889

V. Pezoulas, Eygenia Mylona, C. Papaloukas, Angelos Liontos, Dimitrios Biros, Orestis I. Milionis, C. Kyriakopoulos, K. Kostikas, H. Milionis, D. Fotiadis

{"title":"A hybrid approach based on dynamic trajectories to predict mortality in COVID-19 patients upon steroids administration","authors":"V. Pezoulas, Eygenia Mylona, C. Papaloukas, Angelos Liontos, Dimitrios Biros, Orestis I. Milionis, C. Kyriakopoulos, K. Kostikas, H. Milionis, D. Fotiadis","doi":"10.1109/BHI56158.2022.9926889","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926889","url":null,"abstract":"Since the World Health Organization (WHO) has declared Artificial Intelligence (AI) as a powerful tool in the fight against COVID-19, multiple studies have been launched aiming to shed light into risk factors for ICU admission and mortality. None of the existing studies, however, have captured the dynamic trajectories of hospitalized COVID-19 patients who receive steroids nor have explored trajectory-based mortality indicators. In this work, we present a novel, hybrid approach to address this need. Latent Growth Mixture Modelling (LGMM) was used to analyze the trajectories of patients who received steroids. The patients were then grouped into clusters based on the similarity of their dynamic trajectories. State-of-the art machine learning classifiers are trained on the original dataset with and without dynamic trajectories to assess whether their inclusion can enhance the prediction of mortality. Our results highlight the importance of trajectories for predicting mortality in patients who receive steroids yielding 4% and 5% increase in the sensitivity (0.84) and specificity (0.85). The FiO2 and percentage of neutrophils at day 5, along with the percentage of lymphocytes at day 7, were identified as the main causes for mortality in patients who receive steroids, where the SatO2 levels showed significant alterations in the dynamic trajectories.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"24 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":"121072448","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

Tsakaneli Stavroula

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926949

Tsakaneli Stavroula, E. Bei, M. Zervakis

{"title":"Tsakaneli Stavroula","authors":"Tsakaneli Stavroula, E. Bei, M. Zervakis","doi":"10.1109/BHI56158.2022.9926949","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926949","url":null,"abstract":"Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease that affects approximately 2.8 million persons globally. While there is currently no cure for this neurodegenerative disease, MS has become a highly manageable disease through treatment options like disease-modifying medications, that can help to control the symptoms and slow disease progression. Among them, interferon beta (IFNβ) therapy is a first-line treatment for MS but has shown to be only partially effective. Thus, it is important to identify biomarkers that aid in early identification of IFNβ responders. In this study, based on gene expression profiles from untreated and interferon treated patients from a publicly available dataset, we performed differential expression analysis and Pigengene network association (weighted correlation network analysis (WGCNA) and Bayesian networks modeling) in order to construct a high-confidence protein-protein (PPI) interaction network. Subsequently, aiming to find the most significant clustering modules and hub genes, we applied a number of topological analysis methods (cytoHubba plugin) followed by MCODE clustering algorithm. Our approach resulted in highly connected hub genes generating a reliable 21-hubgene signature that could predict the response of interferon beta (IFNβ) therapy in patients with MS. The 21-hub-gene signature showed high classification performance (Accuracy = 91,49%, Sensitivity = 94.55%, Specificity = 87.15%) demonstrating potential clinical benefit.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"1 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":"126847401","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

Classification of Sleep Apnea via SpO2 in a Simulated Smartwatch Environment 模拟智能手表环境下SpO2对睡眠呼吸暂停的分类

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926966

Brendan Lyden, Zachary Dair, Ruairi O'Reilly

{"title":"Classification of Sleep Apnea via SpO2 in a Simulated Smartwatch Environment","authors":"Brendan Lyden, Zachary Dair, Ruairi O'Reilly","doi":"10.1109/BHI56158.2022.9926966","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926966","url":null,"abstract":"Sleep apnea is one of the most common sleep disorders. To diagnose sleep apnea, a patient must undertake a polysomnography where multiple physiological signals are recorded in a specialised sleep laboratory. Reducing the number of physiological signals necessary for a diagnosis and enabling data monitoring in a distributed fashion would assist in the detection of sleep apnea. Smartwatches are becoming more advanced, with the current generation capable of deriving blood oxygen saturation, which can indicate sleep apnea. This work evaluates the efficacy of sleep apnea classifiers in a simulated smartwatch environment. Results demonstrate that SpO2 is a performant signal for classifying sleep apnea. Naive Bayes trained with features extracted from a Long Short Term Memory Network is capable of classifying sleep apnea with an accuracy of 97.04%, outperforming state-of-the-art approaches. Classification within the simulated smartwatch environment demonstrates robustness up to a signal-to-noise ratio of 50 dB and maintains high levels of accuracy at sampling frequencies above 25 Hz. These encouraging results show substantial potential for smartwatches to provide timely, accessible sleep apnea screening and enable automated diagnostics reducing the reliance on specialist centres.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"12 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":"116584940","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: 2

Cognitive-emotional Stress and Risk Stratification of Situational Awareness in Immersive First Responder Training 沉浸式急救训练中情境意识的认知-情绪应激和风险分层

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926805

L. Paletta, M. Pszeida, M. Schneeberger, Amir Dini, Lilian Reim, W. Kallus

{"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}

引用次数: 0

Spectrogram Image-based Machine Learning Model for Carotid-to-Femoral Pulse Wave Velocity Estimation Using PPG Signal 基于频谱图图像的PPG信号颈-股脉波速度估计机器学习模型

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926941

Juan Manuel Vargas Garcia, M. Bahloul, T. Laleg‐Kirati

{"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}

引用次数: 0