2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)最新文献_第3页

What PLS can still do for Imaging Genetics in Alzheimer's disease PLS对阿尔茨海默病的成像遗传学还有什么作用

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926813

F. Cruciani, A. Altmann, Marco Lorenzi, G. Menegaz, I. Galazzo

{"title":"What PLS can still do for Imaging Genetics in Alzheimer's disease","authors":"F. Cruciani, A. Altmann, Marco Lorenzi, G. Menegaz, I. Galazzo","doi":"10.1109/BHI56158.2022.9926813","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926813","url":null,"abstract":"In this work we exploited Partial Least Squares (PLS) model for analyzing the genetic underpinning of grey matter atrophy in Alzheimer's Disease (AD). To this end, 42 features derived from T1-weighted Magnetic Resonance Imaging, including cortical thicknesses and subcortical volumes were considered to describe the imaging phenotype, while the genotype information consisted of 14 recently proposed AD related Polygenic Risk Scores (PRS), calculated by including Single Nucleotide Polymorphism passing different significance thresholds. The PLS model was applied on a large study cohort obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database including both healthy individuals and AD patients, and validated on an independent ADNI Mild Cognitive Impairment (MCI) cohort, including Early (EMCI) and Late MCI (LMCI). The experimental results confirm the existence of a joint dynamics between brain atrophy and genotype data in AD, while providing important generalization results when tested on a clinically heterogeneous cohort. In particular, less AD specific PRS scores were negatively correlated with cortical thicknesses, while highly AD specific PRSs showed a peculiar correlation pattern among specific subcortical volumes and cortical thicknesses. While the first outcome is in line with the well known neurodegeneration process in AD, the second could be revealing of different AD subtypes.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"14 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":"131899808","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

Investigating Graph-based Features for Speech Emotion Recognition 基于图的语音情感识别特征研究

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926795

A. Pentari, George P. Kafentzis, M. Tsiknakis

{"title":"Investigating Graph-based Features for Speech Emotion Recognition","authors":"A. Pentari, George P. Kafentzis, M. Tsiknakis","doi":"10.1109/BHI56158.2022.9926795","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926795","url":null,"abstract":"During the last decades, automatic speech emotion recognition (SER) has gained an increased interest by the research community. Specifically, SER aims to recognize the emotional state of a speaker directly from a speech recording. The most prominent approaches in the literature include feature extraction of speech signals in time and/or frequency domain that are successively applied as input into a classification scheme. In this paper, we propose to exploit graph theory and structures as alternative forms of speech representations. We suggest applying the so-called Visibility Graph (VG) theory to represent speech data using an adjacency matrix and extract well-known graph-based features from the latter. Finally, these features are fed into a Support Vector Machine (SVM) classifier in a leave-one-speaker-out, multi-class fashion. Our proposed feature set is compared with a well-known acoustic feature set named the Geneva Minimalistic Acoustic Parameter Set (GeMAPS). We test both approaches on two publicly available speech datasets: SAVEE and EMOVO. The experimental results show that the proposed graph-based features provide better results, namely a classification accuracy of 70% and 98%, respectively, yielding an increase by 29.2% and 60.6%, respectively, when compared to GeMAPS.","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":"133368678","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: 1

Parallel Multi-scale convolution based prototypical network for few-shot ECG beats classification 基于并行多尺度卷积的心电短拍分类原型网络

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926948

Zicong Li, Henggui Zhang

引用次数: 0

Efficient Metric Learning with Graph Transformer for Accurate Colorectal Cancer Staging 基于图形转换器的高效度量学习用于结直肠癌的准确分期

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926858

Zongxiang Pei, Daoqiang Zhang, Wei Shao

{"title":"Efficient Metric Learning with Graph Transformer for Accurate Colorectal Cancer Staging","authors":"Zongxiang Pei, Daoqiang Zhang, Wei Shao","doi":"10.1109/BHI56158.2022.9926858","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926858","url":null,"abstract":"Colorectal cancer (CRC) is the third leading cause of cancer death in men and the third leading cause of cancer death in women in United States. So far, the histopathological image remains the golden standard in staging CRC, and accurate staging CRC is important for timely therapy and possible delay of the disease. Existing studies often utilized the pre-trained deep models to extract features from histopathological images, which neglected to take the supervised metric information into consideration. In addition, most of the existing methods did not take advantages of the correlations among different samples for the downstream classification tasks. To address the aforementioned problems, in this paper, we propose an efficient Metric learning with Graph Transformer (MGT), which adopts efficient metric learning to help extract distinguished image features followed by applying graph transformer for CRC staging. The main advantage of the proposed graph transformer is that it can fully exploit the correlations among different patients, which results in better tumor staging performance. To evaluate the effectiveness of the proposed method, we conduct several experiments for CRC staging on public available dataset TCGA-CRC in The Cancer Genome Atlas (TCGA). The experimental results show that our method can consistently achieve superior classification performance than the comparing methods.","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":"127878593","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: 1

Enhancing Continuous Glucose Monitoring-based Eating Detection with Wearable Biomarkers 利用可穿戴生物标志物增强基于连续血糖监测的饮食检测

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926964

Sorush Omidvar, Ali R. Roghanizad, Lucy Chikwetu, Garrett I. Ash, J. Dunn, B. Mortazavi

{"title":"Enhancing Continuous Glucose Monitoring-based Eating Detection with Wearable Biomarkers","authors":"Sorush Omidvar, Ali R. Roghanizad, Lucy Chikwetu, Garrett I. Ash, J. Dunn, B. Mortazavi","doi":"10.1109/BHI56158.2022.9926964","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926964","url":null,"abstract":"Proper diet monitoring is a cornerstone of preventing and treating Type 2 Diabetes. However, this usually relies on burdensome manual meal logging. Continuous glucose monitors (CGMs), which have recently gained popularity as a tool to help Type 2 Diabetics with their treatments, may allow for a burden-free, sensor-based approach to logging periods of eating through monitoring the glucose dynamics and attempting to identify periods of post-prandial glucose response. However, CGMs-alone may not be sufficient in properly detecting periods; periods such as those present in gastric emptying may result in false positives for eating detection, given the sharp rise in glucose response. This work seeks to augment CGM-captured signals with that of other wearable biomarkers, captured from smartwatches, to aid in the detection of eating periods. These signals have been shown to detect eating motions. We explore a hierarchical model approach to augmenting CGM-based eating detection with additional sensing modalities. We test our model data collected from healthy participants eating in free-living conditions. We find that CGM-based eating detection can be improved by retrospectively reviewing wearable sensing data for confirmation, improving our model performance of eating detection, as measured by the area under the receiver operating characteristic curve, by 0.15 (from 0.64 to 0.79), and similarly across additional performance metrics.","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":"129277607","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

An implementation of an AI-assisted sonification algorithm for neonatal EEG seizure detection on an edge device 在边缘设备上实现用于新生儿脑电图发作检测的人工智能辅助超声算法

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926876

F. O'Sullivan, Sergi Gómez-Quintana, A. Temko, E. Popovici

引用次数: 1

Fine-grained Cross-Layer Attention Framework for Wound Stage Classification 伤口分期分类的细粒度跨层注意框架

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926798

Keval Nagda, M. Briden, Narges Norouzi

引用次数: 0

Domain over size: Clinical ELECTRA surpasses general BERT for bleeding site classification in the free text of electronic health records 领域超过大小:在电子健康记录的自由文本中，临床ELECTRA在出血部位分类方面优于一般BERT

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926955

J. Pedersen, M. Laursen, C. Soguero-Ruíz, T. Savarimuthu, R. Hansen, P. Vinholt

{"title":"Domain over size: Clinical ELECTRA surpasses general BERT for bleeding site classification in the free text of electronic health records","authors":"J. Pedersen, M. Laursen, C. Soguero-Ruíz, T. Savarimuthu, R. Hansen, P. Vinholt","doi":"10.1109/BHI56158.2022.9926955","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926955","url":null,"abstract":"Bleeding can be a life-threatening condition which occurs for 3.2% of medical patients. Information about previous bleeding and bleeding site is used to predict the risk of future bleeding and guide anticoagulant treatment. However, obtaining this information is a time-consuming task as it is contained in the free text of electronic health records. Previous research has mainly been focused on extracting bleeding events but does not classify the bleeding site which is important for assessing the severity of the bleeding. This study creates the first dataset for developing and evaluating machine learning models for classification of bleeding site. The dataset consists of sentences annotated by medical doctors as belonging to one of ten bleeding sites. The sentences were annotated in 149,523 electronic health record notes from 1,533 patients of Odense University Hospital, Denmark, between 2015 and 2020. We compare different deep learning models on classifying bleeding site and find that a ∼13M parameter ELECTRA model pretrained on clinical text achieves higher accuracy ($0.905 pm 0.002$) than a ∼110M parameter general BERT model ($0.884 pm 0.001$) on a balanced test set of 1,500 sentences. We furthermore test different methods for dealing with unbalanced data without finding any significant differences between methods.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"197 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":"116168569","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: 3

Comparison of Missing Data Imputation Methods using the Framingham Heart study dataset 使用Framingham心脏研究数据集的缺失数据输入方法的比较

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926882

Konstantinos Psychogyios, Loukas Ilias, D. Askounis

{"title":"Comparison of Missing Data Imputation Methods using the Framingham Heart study dataset","authors":"Konstantinos Psychogyios, Loukas Ilias, D. Askounis","doi":"10.1109/BHI56158.2022.9926882","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926882","url":null,"abstract":"Cardiovascular disease (CVD) is a class of diseases that involve the heart or blood vessels and according to World Health Organization is the leading cause of death worldwide. EHR data regarding this case, as well as medical cases in general, contain missing values very frequently. The percentage of missingness may vary and is linked with instrument errors, manual data entry procedures, etc. Even though the missing rate is usually significant, in many cases the missing value imputation part is handled poorly either with case-deletion or with simple statistical approaches such as mode and median imputation. These methods are known to introduce significant bias, since they do not account for the relationships between the dataset's variables. Within the medical framework, many datasets consist of lab tests or patient medical tests, where these relationships are present and strong. To address these limitations, in this paper we test and modify state-of-the-art missing value imputation methods based on Generative Adversarial Networks (GANs) and Autoencoders. The evaluation is accomplished for both the tasks of data imputation and post-imputation prediction. Regarding the imputation task, we achieve improvements of 0.20, 7.00% in normalised Root Mean Squared Error (RMSE) and Area Under the Receiver Operating Characteristic Curve (AUROC) respectively. In terms of the post-imputation prediction task, our models outperform the standard approaches by 2.50% in F1-score.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"20 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":"126718726","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: 3

An EEG-based Method for Drowsiness Level Estimation 一种基于脑电图的睡意水平估计方法

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2022-09-27 DOI: 10.1109/BHI56158.2022.9926820

David O'Callaghan, Cian Ryan, Ashkan Parsi, Joe Lemley

{"title":"An EEG-based Method for Drowsiness Level Estimation","authors":"David O'Callaghan, Cian Ryan, Ashkan Parsi, Joe Lemley","doi":"10.1109/BHI56158.2022.9926820","DOIUrl":"https://doi.org/10.1109/BHI56158.2022.9926820","url":null,"abstract":"Obtaining accurate estimates of a driver's level of drowsiness to help develop non-invasive methods for drowsiness detection is a challenging and open research problem. Many approaches to drowsiness or sleepiness estimation are supervised machine learning ones that require accurate labels for their sensor data to train a model. In this work, a novel method is presented to annotate time-series data with a driver's estimated level of drowsiness using characteristics from the electroencephalogram (EEG). The proposed scoring algorithm assigns a value between one and ten to segments of EEG data corresponding to a driver's predicted response on the Karolinska Sleepiness Scale (KSS). The parameters of the scoring algorithm are tuned using a metaheuristic optimization algorithm called Late-Acceptance Hill-Climbing and a loss function that utilizes the driver's own KSS ratings. Promising qualitative results have been presented for the proposed method to estimate a person's level of drowsiness on a more granular timescale than traditional survey methods like KSS. Furthermore, the approach could be extended beyond drowsiness estimation to any task involving the need to make use of EEG data between event markers or annotations. In addition, the data acquisition process that was employed in this work is described along with the database created.","PeriodicalId":347210,"journal":{"name":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"5 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":"123729543","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: 1