2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)最新文献_第3页

Drug Development Pipeline Running Low, What’s Data Got to Do with It? 药物开发管道运行缓慢，数据与之有何关系?

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/SPMB47826.2019.9037860

M. Kiani

{"title":"Drug Development Pipeline Running Low, What’s Data Got to Do with It?","authors":"M. Kiani","doi":"10.1109/SPMB47826.2019.9037860","DOIUrl":"https://doi.org/10.1109/SPMB47826.2019.9037860","url":null,"abstract":"The per capita cost of health care in the US, by far the highest in the world, is driven in part by the high cost of pharmaceuticals. The low conversion rate of promising agents into successful clinical therapeutics is an important contributor to the high cost of pharmaceuticals. For example, all of the ~150 drugs developed in the last 15 years in mouse models to treat sepsis have failed in clinical trials. Several NIH institutes and other funding agencies have recently eliminated or significantly curtailed their funding for animal-based studies. A number of in vitro models of living tissues, especially organoids and microphysiological systems, are playing an increasingly significant role in prescreening of promising therapeutics for safety, efficacy and toxicity prior to expensive animal and human trials, thus offering the promise of accelerated drug development. However, a data-based understanding of how and the degree to which these assays reproduce the biological signals of interest, as well as drug-cell interactions, is critical to their successful deployment in the field of drug discovery. It is therefore critical to decipher omic and other changes to map known response pathways/networks so that in silico models can be used to determine which components of the biological signaling in human cells is preserved in mouse cells to guide further optimization of in vitro assays. Development of appropriate analytical tools will be critical to the success of this hybrid approach to drug development.","PeriodicalId":143197,"journal":{"name":"2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127118010","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

EEG Analysis of the Music Effect on Lecture Comprehension 音乐对课堂理解效果的脑电图分析

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/SPMB47826.2019.9037848

V.A. Díaz de León-Esparza, A. Martínez-Cervantes, A. G. Vargas-Cortés, A. E. Olivares-Núñez, D. Ibarra-Zárate, L. Alonso-Valerdi

引用次数: 0

Recent Advances in the Temple University Digital Pathology Corpus 天普大学数字病理语料库的最新进展

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/SPMB47826.2019.9037859

I. Hunt, S. Husain, J. Simon, I. Obeid, J. Picone

引用次数: 1

A Comparative Study of End-To-End Discriminative Deep Learning Models for Knee Joint Kinematic Time Series Classification 端到端判别深度学习模型在膝关节运动时间序列分类中的比较研究

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/SPMB47826.2019.9037831

M. Abid, A. Mitiche, Y. Ouakrim, P. Vendittoli, A. Fuentes, N. Hagemeister, N. Mezghani

引用次数: 3

Fast Automatic Artifact Annotator for EEG Signals Using Deep Learning 基于深度学习的脑电信号快速自动伪影标注器

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/SPMB47826.2019.9037834

D. Kim, S. Keene

{"title":"Fast Automatic Artifact Annotator for EEG Signals Using Deep Learning","authors":"D. Kim, S. Keene","doi":"10.1109/SPMB47826.2019.9037834","DOIUrl":"https://doi.org/10.1109/SPMB47826.2019.9037834","url":null,"abstract":"Electroencephalogram (EEG) is a widely used non-invasive brain signal acquisition technique that measures voltage fluctuations from neuron activities of the brain. EEGs are typically used to diagnose and monitor disorders such as epilepsy, sleep disorders, and brain death and also to help the advancement of various fields of science such as cognitive science, and psychology. EEG signals usually suffer from a variety of artifacts caused by eye movements, chewing, muscle movements, and electrode pops, which disrupts the diagnosis and hinders precise representation of brain activities. This paper proposes a deep learning based model to detect the presence of the artifacts and to classify the kind of the artifact to help clinicians resolve problems regarding artifacts immediately during the signal collection process. The model is optimized to map the 1-second segments of raw EEG signals to detect 4 different kinds of artifacts and the real signal. The model achieves a 5-class classification accuracy of 67.59%, and a true positive rate of 80% with a 25.82% false alarm for binary artifact classification with time-lapse. The model is lightweight and could potentially be deployed in portable machines.","PeriodicalId":143197,"journal":{"name":"2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129364373","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

Spectrum Sharing Strategy for Radio Frequency-Based Medical Services 基于射频的医疗服务频谱共享策略

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/SPMB47826.2019.9037847

Ammar Ahmed, Shuimei Zhang, Vaishali S. Amin, Yimin D. Zhang

引用次数: 4

Total Variation Denoising for Optical Coherence Tomography 光学相干层析成像的全变分去噪

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/SPMB47826.2019.9037832

Michael Shamouilian, I. Selesnick

引用次数: 9

Machine Learning Enabled Wearable Brain Deformation Sensing System 机器学习可穿戴大脑变形传感系统

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/SPMB47826.2019.9037843

Sayemul Islam, Albert Kim

{"title":"Machine Learning Enabled Wearable Brain Deformation Sensing System","authors":"Sayemul Islam, Albert Kim","doi":"10.1109/SPMB47826.2019.9037843","DOIUrl":"https://doi.org/10.1109/SPMB47826.2019.9037843","url":null,"abstract":"Brain deformation – the primary cause of traumatic brain injury (TBI) – occurs during fall, automobile accident, brain surgery, or explosion (i.e., pressurized airflow) [1] . Mechanical impact causes strain energy that leads to tissue displacement. Researchers have attempted to characterize the brain deformation for diagnosis and prevention of concussion-related TBI [2] . It is especially important to measure microscale deformation because even a few tens of micrometer brain deformation may have direct neuropsychiatric and neuro-degenerative consequences [3] – [6] . Another effort to minimize brain deformation can be found in intracranial surgeries. The deformation is inevitable but can be minimized by designing a better apparatus and using advance stereotactic techniques [7] – [9] . As such, there are a few methods to measure brain deformation today [8] , [10] – [12] . Computational models and imaging technologies (e.g., FEM (finite element method) modeling, magnetic resonance imaging (MRI)) are such examples. However, because the brain is viscoelastic [13] , these technologies lack 1) detailed information regarding micro-scale brain deformation and 2) real-time measurement capability.","PeriodicalId":143197,"journal":{"name":"2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130379077","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

Machine Learning Approach to Measure Sleep Quality using EEG Signals 利用脑电图信号测量睡眠质量的机器学习方法

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/SPMB47826.2019.9037833

M. Ravan, Senior Member

{"title":"Machine Learning Approach to Measure Sleep Quality using EEG Signals","authors":"M. Ravan, Senior Member","doi":"10.1109/SPMB47826.2019.9037833","DOIUrl":"https://doi.org/10.1109/SPMB47826.2019.9037833","url":null,"abstract":"Sleep quality has a vital effect on good health and well-being throughout a life. Getting enough sleep at the right times can help protect mental health, physical health, quality of life, and safety. In this study, an electroencephalography (EEG)-based machine-learning approach is proposed to measure sleep quality. The advantages of our approach over standard Polysomnography (PSG) method are: 1) it measures sleep quality by recognizing three sleep categories rather than five sleep stages, thus higher accuracy can be expected; 2) three sleep categories are recognized by analyzing EEG signals only using two EEG electrodes, so the user experience is improved because he/she is attached with fewer sensors during sleep. Using quantitative features obtained from EEG signals, we developed a new automatic sleep-staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. We used polysomnographic data from PhysioBank database to train and evaluate the performance of the framework, where the sleep stages have been visually annotated. The results demonstrated that the proposed approach achieves high classification performance, which helps to measure sleep quality accurately.","PeriodicalId":143197,"journal":{"name":"2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133732938","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

Seismocardiographic Signal Variability During Regular Breathing and Breath Hold in Healthy Adults 健康成人正常呼吸和屏气时的心震信号变异性

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/SPMB47826.2019.9037852

M. K. Azad, P. Gamage, R. Sandler, N. Raval, H. Mansy

{"title":"Seismocardiographic Signal Variability During Regular Breathing and Breath Hold in Healthy Adults","authors":"M. K. Azad, P. Gamage, R. Sandler, N. Raval, H. Mansy","doi":"10.1109/SPMB47826.2019.9037852","DOIUrl":"https://doi.org/10.1109/SPMB47826.2019.9037852","url":null,"abstract":"Seismocardiographic signals (SCG) are known to correlate with mechanical cardiac activity and may be used for monitoring patients with cardiovascular disease. However, SCG variability is not well understood and may interfere with signal utility. In the current study, the SCG signals were acquired in 5 healthy subjects during regular breathing along with ECG and respiratory flow measurements. In addition, SCG waveforms were recorded during breath hold at end inspiration as well as end expiration. The SCG events were identified and segmented using ECG events. SCG waveforms during regular breathing were separated into two clusters using unsupervised machine learning. The variability was assessed for the clustered and un-clustered SCG by analyzing the Dynamic Time Warping (DTW) distances of SCG waveforms in the time domain. The inter-group variability between the normal breathing clusters and breath hold suggested that cluster 2 events were more similar to end expiration events while no clear trend was observed for cluster 1. The intra-group variability was reduced by approximately 19% for regular breathing clusters and 42% during breath hold compared to the unclustered SCG during normal breathing. The reduced variability during breath hold suggests the utility of SCG recording at breath hold since variability reduction can lead to more robust methods for longitudinal patient monitoring.","PeriodicalId":143197,"journal":{"name":"2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126482805","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