2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)最新文献

SPMB 2019 Table of Contents SPMB 2019目录

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/spmb47826.2019.9037857

引用次数: 0

A novel way to manage and control chronic respiratory diseases 一种管理和控制慢性呼吸道疾病的新方法

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/SPMB47826.2019.9037846

N. Delmonico, V. Fauveau

{"title":"A novel way to manage and control chronic respiratory diseases","authors":"N. Delmonico, V. Fauveau","doi":"10.1109/SPMB47826.2019.9037846","DOIUrl":"https://doi.org/10.1109/SPMB47826.2019.9037846","url":null,"abstract":"An estimated 450 million people worldwide suffer from chronic respiratory diseases such as asthma or chronic obstructive pulmonary disease (COPD). The clinical standard of care in the diagnosis and treatment of respiratory disorders is stethoscope-based lung auscultation. Clinical signs are an integral part of the diagnosis and management of these diseases. Such use of a stethoscope, however, is limited by the episodic nature of data acquisition, as well as by the limits of human subjectivity in the recognition of symptoms. Some indications of a respiratory complication may include shortness of breath, coughing, wheezing, and labored breathing. Unfortunately, there is currently no way to objectively monitor these signs. At Strados Labs we have developed the world’s first AI-powered acoustic bio-sensor designed to bring wireless, hands-free, respiratory monitoring to clinical teams over the entire episode of care. This non-invasive clinical-grade medical device also uses proprietary machine learning algorithms to identify key changes in pulmonary sounds and breathing patterns, and to notify care teams about the respiratory health status of patients. In this way, we seek to improve care triage, reduce length of hospital stay, and avoid costly pulmonary complications. The non-invasive device captures lung sounds and chest wall motion from which it extracts key features in the time and frequency domains to identify vital respiratory symptoms. Proprietary machine learning techniques, derived from state-of-the-art speech recognition algorithms, then use the characterized data to train models that automatically label areas of interest. This process creates a closed loop system that allows the Strados device to operate autonomously and ultimately improve the management and control of chronic respiratory diseases.","PeriodicalId":143197,"journal":{"name":"2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124743149","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

Noninvasive Detection of Elevated Intracranial Pressure Using Tympanic Membrane Pulse 鼓膜脉冲无创检测颅内压升高

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/SPMB47826.2019.9037864

R. Dhar, R. Sandler, K. Manwaring, H. Mansy

引用次数: 2

Issues in the Reproducibility of Deep Learning Results 深度学习结果再现性中的问题

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/SPMB47826.2019.9037840

S. Jean-Paul, T. Elseify, I. Obeid, Joseph Picone

引用次数: 8

Nonlinear Smoothing of Data with Random Gaps and Outliers (DRAGO) Improves Estimation of Circadian Rhythm 随机间隙和异常值数据的非线性平滑(DRAGO)改进了昼夜节律的估计

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/SPMB47826.2019.9037837

A. Parekh, I. Ayappa, R. Osorio, I. Selesnick, A. Baroni, M. Miller, B. Cavedoni, H. Sanders, A. Varga, E. Blessing, D. Rapoport

引用次数: 0

Shape Modeling and Atlas-Based Segmentation for Identification of Lower Leg Tissues in pQCT 形状建模和基于图谱的pQCT下肢组织识别分割

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/SPMB47826.2019.9037862

S. Makrogiannis, A. Okorie, T. Biswas, L. Ferrucci

{"title":"Shape Modeling and Atlas-Based Segmentation for Identification of Lower Leg Tissues in pQCT","authors":"S. Makrogiannis, A. Okorie, T. Biswas, L. Ferrucci","doi":"10.1109/SPMB47826.2019.9037862","DOIUrl":"https://doi.org/10.1109/SPMB47826.2019.9037862","url":null,"abstract":"In this work, we introduce an atlas-based segmentation method for lower leg tissues at 4%, 38%, and 66% tibial length. Our goal is to model the shape of the lower leg tissue types and to identify hard and soft tissues in an automated way. In our methodology, we implemented B-spline based free form deformation (FFD), and symmetric diffeomorphic demons (SDD) deformable models for nonlinear registration, and compared their performances for atlas-based segmentation accuracy on our pQCT data. Overall, we concluded that atlas-based segmentation is a promising technique, especially in the presence of noise and other types of image degradation. We also observed that the diffeomorphic demons algorithm may produce more accurate deformation fields than FFD. On the other hand, FFD produced smoother deformations than SDD. Quantitative analysis using the Dice similarity coefficient (DSC), showed that FFD was slightly better than SDD in identification of the trabecular bone tissue in 4% tibia. At 38% tibial length, SDD produced consistently higher DSC values than FFD, while at 66% tibia, FFD produced slightly higher segmentation accuracy.","PeriodicalId":143197,"journal":{"name":"2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130940739","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

SPMB 2019 Organizing Committee SPMB 2019组委会

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/spmb47826.2019.9037830

引用次数: 0

The Instrumented Multitask Assessment System (IMAS) 仪器多任务评估系统(IMAS)

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/SPMB47826.2019.9037841

Z. Kane, E. Stecco, A. Napoli, C. Tucker, I. Obeid

引用次数: 0

Fully Automated, MRI-based Left-Ventricular Contractility Analysis in Breast Cancer Patients Following Chemotherapy 全自动、基于mri的乳腺癌患者化疗后左室收缩力分析

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/SPMB47826.2019.9037829

Bryant M. Baldwin, G. Angus-Barker, S. Joseph, M. Figarola, M. Cohen, C. Malozzi, J. Kar

{"title":"Fully Automated, MRI-based Left-Ventricular Contractility Analysis in Breast Cancer Patients Following Chemotherapy","authors":"Bryant M. Baldwin, G. Angus-Barker, S. Joseph, M. Figarola, M. Cohen, C. Malozzi, J. Kar","doi":"10.1109/SPMB47826.2019.9037829","DOIUrl":"https://doi.org/10.1109/SPMB47826.2019.9037829","url":null,"abstract":"This study investigated if measurements of mechanical contractile parameters, such as strains, torsion and left-ventricular ejection fraction (LVEF), are indicative of left-ventricular (LV) remodeling that may occur in patients who have been exposed to the anthracycline and trastuzumab type of chemotherapeutic agents (CA). An equally important goal was investigating this contractility using a single-scan cardiac strain analysis tool comprising of the Displacement Encoding with Stimulated Echoes (DENSE) sequence for MRI scans and the Radial Point Interpolation Method (RPIM). Data was acquired in 11 patients who had been exposed to CA agents and were under either a regimen of breast cancer antineoplastic drugs and/or were being treated for cardiac complications. A Bland-Altman analysis of interobserver strain measurements showed agreements of 0.01 ± 0.06 for longitudinal strain, 0.10 ± 1.92° for torsion. Enlarging of the LV in the patient population was indicated by a significant difference in their diastolic diameters in healthy subjects. Significant longitudinal strains differences were seen between patients and healthy subjects which were 0.15 ± 0.03 vs 0.21 ± 0.04 (p=0.02) and 0.17 ± 0.02 vs 0.22 ± 0.03 (p=0.01) for the mid-ventricular and apical sections. A similar result for torsion was found between patients and healthy subjects for the mid-ventricular and basal sub-regions. The results from the statistical analysis show the likelihood of LV remodeling and fibrosis in these patients that is otherwise not indicated by LVEF measurements.","PeriodicalId":143197,"journal":{"name":"2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130144837","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

[SPMB 2019 Title Page] [SPMB 2019扉页]

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2019-12-01 DOI: 10.1109/spmb47826.2019.9037839

引用次数: 0