2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)最新文献

ResHNet: Spectrograms Based Efficient Heart Sounds Classification Using Stacked Residual Networks ResHNet:利用堆叠残差网络，基于频谱图的高效心音分类

2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) Pub Date : 2019-09-12 DOI: 10.1109/BHI.2019.8834578

A. Balamurugan, S. Teo, Jianxi Yang, Zhongbo Peng, Xulei Yang, Zeng Zeng

{"title":"ResHNet: Spectrograms Based Efficient Heart Sounds Classification Using Stacked Residual Networks","authors":"A. Balamurugan, S. Teo, Jianxi Yang, Zhongbo Peng, Xulei Yang, Zeng Zeng","doi":"10.1109/BHI.2019.8834578","DOIUrl":"https://doi.org/10.1109/BHI.2019.8834578","url":null,"abstract":"Cardiovascular disease (CVD) is one of the major contributors of global mortality rate as it accounts for almost 31% of the worldwide deaths. As per World Health Organization (WHO), CVD continues to be the number one cause of death in the world. In some parts of the world, access to expert doctors and diagnosis are difficult. Thus an efficient and quick diagnosis of heart disease method is needed especially for low-income and middle-income countries where Magnetic Resonance Imaging (MRI) and Ultrasound becomes a constraint in terms of the resources to save human life. With the tremendous technology advancement in the medical field, deep learning has gained more attention to automate most of the initial diagnosis of diseases. This fosters continuous research in adopting deep learning methods for automatic classification of heart sounds to identify any abnormalities. In this work, we aim to investigate the efficiency of introducing residual modules in heart sounds classification using a deep neural network. This approach involves the following steps: (i) Generation of Spectrograms for every 1D audio signal using Spectrogram generator module (ii) Training of residual network based classifier for identifying normal and abnormal heart sounds based on the Spectrograms. The standard dataset given for 2016 PhysioNet/CinC Challenge has been used here for validating our residual network. This method achieved around 97% accuracy on the independent hidden test set performing best without incorporating any segmentation or additional Mel-Frequency Cepstrum Coefficients (MFCC) features of the audio signals and just the learned features from the image-based representations. Various baseline results of other deep learning based approaches have also been considered for evaluating the robustness of this framework.","PeriodicalId":281971,"journal":{"name":"2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123296229","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: 5

Non-Invasive In-Home Sleep Stage Classification Using a Ballistocardiography Bed Sensor 使用ballo - cardiography床传感器的无创家庭睡眠阶段分类

2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) Pub Date : 2019-05-19 DOI: 10.1109/BHI.2019.8834535

Ruhan Yi, Moein Enayati, J. Keller, M. Popescu, M. Skubic

{"title":"Non-Invasive In-Home Sleep Stage Classification Using a Ballistocardiography Bed Sensor","authors":"Ruhan Yi, Moein Enayati, J. Keller, M. Popescu, M. Skubic","doi":"10.1109/BHI.2019.8834535","DOIUrl":"https://doi.org/10.1109/BHI.2019.8834535","url":null,"abstract":"Longitudinal monitoring of sleep related parameters can be used for early detection of diseases and also as an indication to physicians for effective adjustment of medication and dosage treatments for people at risk. The correlation between sleep disorders and health conditions such as Alzheimer's and Parkinson's diseases has already been reported in the literature. In this paper, we propose the use of a hydraulic bed sensor for sleep stage classification. Our main motivation of using the bed sensor is to provide a non-invasive, in-home monitoring system, which tracks the changes in health conditions of the subjects over time. Regular polysomnography data from a Sleep Lab have been used as the ground truth, with the focus on three sleep stages, namely, awake, rapid eye movement (REM) and non-REM sleep (NREM). A total of 74 features including heart rate variability (HRV) features, respiratory rate variability (RV) features, and linear frequency cepstral coefficients (LFCC) were extracted from the bed sensor data. Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) classification methods were applied to these features. Our results show accuracy as good as 85% with 0.74 kappa, in the detection of these three sleep stages. These results show promise in the ability of the bed sensor to monitor and track sleep quality and sleep related disorders noninvasively.","PeriodicalId":281971,"journal":{"name":"2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126418706","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: 14

Towards EEG Generation Using GANs for BCI Applications 脑机接口中使用gan生成脑电图的研究

2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) Pub Date : 2019-05-19 DOI: 10.1109/BHI.2019.8834503

Fatemeh Fahimi, Zhuo Zhang, Wooi-Boon Goh, K. Ang, Cuntai Guan

引用次数: 29

Leveraging transfer learning techniques for classifying infant vocalizations 利用迁移学习技术对婴儿发声进行分类

2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) Pub Date : 2019-05-19 DOI: 10.1109/BHI.2019.8834666

Aditya Gujral, Kexin Feng, Gulshan Mandhyan, Nfn Snehil, Theodora Chaspari

{"title":"Leveraging transfer learning techniques for classifying infant vocalizations","authors":"Aditya Gujral, Kexin Feng, Gulshan Mandhyan, Nfn Snehil, Theodora Chaspari","doi":"10.1109/BHI.2019.8834666","DOIUrl":"https://doi.org/10.1109/BHI.2019.8834666","url":null,"abstract":"Infant vocalizations serve various communicative functions and are related to several developmental factors. Different types of vocalizations depict distinct spectro-temporal patterns, which can be recovered and learned using emerging end-to-end machine learning systems. A common problem in such systems is the limited availability of labelled data preventing reliable training. Transfer learning can be used to mitigate this problem by taking advantage of additional data resources relevant to the problem of interest. We propose a transfer learning framework which relies on neural network fine-tuning, and explore various types of architectures, such as a convolutional neural network (CNN) and long-term-short-memory (LSTM) recurrent neural networks with and without an attention mechanism. Our target data come from the Cry Recognition In Early Development (CRIED), while the source data come from three publicly available resources: the Oxford Vocal (OxVoc) Sounds database, the Google AudioSet, and the Freesound repository. Our results indicate that the neural network architectures trained with the proposed transfer learning approach outperform the corresponding networks solely trained on the target data, as well as neural networks pre-trained on large-scale image datasets and adapted to the target data (e.g., VGG16). These suggest the effectiveness of adaptation techniques combined with appropriate publicly available datasets for mitigating the limited availability of labelled data in human-related applications.","PeriodicalId":281971,"journal":{"name":"2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117193967","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: 6

Utilizing Consumer-grade Wearable Sensors for Unobtrusive Rehabilitation Outcome Prediction 利用消费级可穿戴传感器进行不显眼的康复结果预测

2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) Pub Date : 2019-05-19 DOI: 10.1109/BHI.2019.8834512

Jason Conci, Gina Sprint, D. Cook, D. Weeks

{"title":"Utilizing Consumer-grade Wearable Sensors for Unobtrusive Rehabilitation Outcome Prediction","authors":"Jason Conci, Gina Sprint, D. Cook, D. Weeks","doi":"10.1109/BHI.2019.8834512","DOIUrl":"https://doi.org/10.1109/BHI.2019.8834512","url":null,"abstract":"Rehabilitation outcome prediction can be useful for clinicians providing therapy services to patients undergoing inpatient medical rehabilitation. Machine learning models trained with medical record information available at admission can predict rehabilitation outcomes fairly well. In our previous work, we found rehabilitation outcome prediction accuracy can be improved by also including inertial sensor-based features that objectively quantify patient movement abilities during therapy tasks. In this paper, we extend our prior work by unobtrusively and continuously collecting minute-by-minute movement data from 15 patients throughout their stay of inpatient rehabilitation using inexpensive, consumer-grade fitness trackers, specifically the Fitbit Charge with heart rate. From the Fitbit time series data, we extract features related to physical activity, heart rate, and sleep quality. We use these features as inputs to machine learning models to predict the discharge Functional Independence Measure (FIM) rehabilitation outcome. We also utilize patient similarity techniques to improve prediction accuracy. Results indicate prediction accuracy with the consumer-grade sensor data is close to the same accuracy as prior work using research-grade inertial sensor data. Using consumer-grade fitness devices to obtain highly accurate FIM predictions can help clinicians plan therapy activities during the inpatient stay, as well as assist with discharge to an appropriate setting.","PeriodicalId":281971,"journal":{"name":"2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115738894","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

Outside the Hospital Walls: Associations of Value Based Care Metrics and Community Health Factors 医院外:基于价值的护理指标与社区健康因素的关联

2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) Pub Date : 2019-05-19 DOI: 10.1109/BHI.2019.8834487

C. Markley, K. Feldman, N. Chawla

{"title":"Outside the Hospital Walls: Associations of Value Based Care Metrics and Community Health Factors","authors":"C. Markley, K. Feldman, N. Chawla","doi":"10.1109/BHI.2019.8834487","DOIUrl":"https://doi.org/10.1109/BHI.2019.8834487","url":null,"abstract":"As the healthcare industry shifts from traditional fee-for-service payment to value-based care models, the need to accurately quantify and compare the performance of institutions has become an integral component of both policy and research. To date, several notable metrics have been introduced, including the Centers for Medicare and Medicaids Hospital Value Based Purchasing (HVBP) program. However, despite widespread adoption, these standards suffer from a fundamental oversight. Where the factors utilized to characterize performance reflect only intrinsic facets of an institutions care, capturing elements of mortality rates, patient satisfaction, outcomes, and spending. Yet, this approach is directly at odds with our current understanding of health and wellness, as it is well known that social, economic, and community factors are deeply intertwined with healthcare outcomes. To this end, with institutions spread across diverse geographic regions, our manuscript demonstrates that HVBP performance metrics do not exist in isolation. Rather, they possess strong associations to the community factors in which the institution resides. Aggregating a broad set of factors from disparate data sources, this work moves through the informatics pipeline. Identifying performance scoring profiles though clustering and employing robust linear models to uncover novel relationships and advance the discussion around the need for value-based care quality metrics.","PeriodicalId":281971,"journal":{"name":"2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)","volume":"223 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122502007","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

Designing an ontology for Head and Neck Cancer research 头颈部肿瘤研究本体设计

2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) Pub Date : 2019-05-19 DOI: 10.1109/BHI.2019.8834473

Liss Hernández, Laura Lopez-Perez, Ana M. Ugena, M. Arredondo, G. Fico

引用次数: 2

Automated Identification of Persistent Time-Domain Features in Seismocardiogram Signals 地震心电信号持续时域特征的自动识别

2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) Pub Date : 2019-05-19 DOI: 10.1109/BHI.2019.8834555

Jonathan S. Zia, Jacob P. Kimball, M. Shandhi, O. Inan

引用次数: 8

Reliable lymph node metastasis prediction in head & neck cancer through automated multi-objective model 应用自动化多目标模型预测头颈癌淋巴结转移的可靠性

2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) Pub Date : 2019-05-19 DOI: 10.1109/BHI.2019.8834658

Zhiguo Zhou, M. Dohopolski, Liyuan Chen, Xi Chen, Steve B. Jiang, D. Sher, Jing Wang

{"title":"Reliable lymph node metastasis prediction in head & neck cancer through automated multi-objective model","authors":"Zhiguo Zhou, M. Dohopolski, Liyuan Chen, Xi Chen, Steve B. Jiang, D. Sher, Jing Wang","doi":"10.1109/BHI.2019.8834658","DOIUrl":"https://doi.org/10.1109/BHI.2019.8834658","url":null,"abstract":"Lymph node metastasis (LNM) plays an important role for accurately diagnosing and treating the patients with head & neck cancer. Positron emission tomography (PET) and computed tomography (CT) are two primary imaging modalities used for identifying LNM status. However, the uncertainty of LNM may exist especially for reactive or small nodes. Furthermore, identifying the LNM on PET or CT is greatly dependent on the physician's experience. Therefore, developing a reliable and automatic model is essential for accurately identifying LNM. Multi-objective models have shown promising predictive results by considering different objectives such as sensitivity and specificity. However, most multi-objective models need to choose an optimal model manually. In this work, we proposed an automated multi-objective learning model (AutoMO) for predicting LNM reliably. Instead of picking one optimal model, all the Pareto-optimal models with the calculated relative weights are used in AutoMO. Then the evidential reasoning (ER) approach is used for fusing the output probability for obtaining more reliable results than traditional fusion method. We built three models for PET, CT and PET&CT and the results showed that PET&CT outperformed two single modality based models. The comparative study demonstrated that AutoMO obtained better performance than current available multi-objective and deep learning methods, and more reliable results can be acquired when using ER fusion.","PeriodicalId":281971,"journal":{"name":"2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130008082","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: 8

Lung Nodule Classification Using Combined Deep and Spectral 3D Shape Features 结合深度和光谱三维形状特征的肺结节分类

2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) Pub Date : 2019-05-19 DOI: 10.1109/BHI.2019.8834537

Fereshteh S. Bashiri, Jonathan C. Badger, R. D'Souza, Zeyun Yu, P. Peissig

引用次数: 1