Pub Date : 2019-12-01DOI: 10.1109/spmb47826.2019.9037857
{"title":"SPMB 2019 Table of Contents","authors":"","doi":"10.1109/spmb47826.2019.9037857","DOIUrl":"https://doi.org/10.1109/spmb47826.2019.9037857","url":null,"abstract":"","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":"117062947","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}
Pub Date : 2019-12-01DOI: 10.1109/SPMB47826.2019.9037846
N. Delmonico, V. Fauveau
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.
{"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":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":"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}
Pub Date : 2019-12-01DOI: 10.1109/SPMB47826.2019.9037864
R. Dhar, R. Sandler, K. Manwaring, H. Mansy
Elevated intracranial pressure (ICP) can lead to serious health complications. Hence, this pressure needs to be monitored in patients at risk of increased ICP. The gold standard for ICP measurements are invasive manometers and pressure transducers [1] . However, the risks, discomforts, and expenses of invasive diagnostic can be avoided if satisfactory non-invasive approaches are used. In this presentation, a noninvasive method of monitoring ICP utilizing measurements of Tympanic Membrane pulsation (TMp) is discussed. TMp signals were acquired from 5 healthy subjects at different tilt positions where ICP is expected to increase with head-down positioning. Consistent TMp waveform morphological changes were observed in each subject with the head down position, which is known to increase ICP [2] . The changes tended to reverse with hyperventilation, which is a process known to decrease ICP [3] . These results suggest that TMp waveform measurements may provide a reliable non-invasive method for monitoring ICP.
{"title":"Noninvasive Detection of Elevated Intracranial Pressure Using Tympanic Membrane Pulse","authors":"R. Dhar, R. Sandler, K. Manwaring, H. Mansy","doi":"10.1109/SPMB47826.2019.9037864","DOIUrl":"https://doi.org/10.1109/SPMB47826.2019.9037864","url":null,"abstract":"Elevated intracranial pressure (ICP) can lead to serious health complications. Hence, this pressure needs to be monitored in patients at risk of increased ICP. The gold standard for ICP measurements are invasive manometers and pressure transducers [1] . However, the risks, discomforts, and expenses of invasive diagnostic can be avoided if satisfactory non-invasive approaches are used. In this presentation, a noninvasive method of monitoring ICP utilizing measurements of Tympanic Membrane pulsation (TMp) is discussed. TMp signals were acquired from 5 healthy subjects at different tilt positions where ICP is expected to increase with head-down positioning. Consistent TMp waveform morphological changes were observed in each subject with the head down position, which is known to increase ICP [2] . The changes tended to reverse with hyperventilation, which is a process known to decrease ICP [3] . These results suggest that TMp waveform measurements may provide a reliable non-invasive method for monitoring ICP.","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":"128958395","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}
Pub Date : 2019-12-01DOI: 10.1109/SPMB47826.2019.9037840
S. Jean-Paul, T. Elseify, I. Obeid, Joseph Picone
The Neuronix high-performance computing cluster allows us to conduct extensive machine learning experiments on big data [1] . This heterogeneous cluster uses innovative scheduling technology, Slurm [2] , that manages a network of CPUs and graphics processing units (GPUs). The GPU farm consists of a variety of processors ranging from low-end consumer grade devices such as the Nvidia GTX 970 to higher-end devices such as the GeForce RTX 2080. These GPUs are essential to our research since they allow extremely compute-intensive deep learning tasks to be executed on massive data resources such as the TUH EEG Corpus [2] . We use TensorFlow [3] as the core machine learning library for our deep learning systems, and routinely employ multiple GPUs to accelerate the training process.
{"title":"Issues in the Reproducibility of Deep Learning Results","authors":"S. Jean-Paul, T. Elseify, I. Obeid, Joseph Picone","doi":"10.1109/SPMB47826.2019.9037840","DOIUrl":"https://doi.org/10.1109/SPMB47826.2019.9037840","url":null,"abstract":"The Neuronix high-performance computing cluster allows us to conduct extensive machine learning experiments on big data [1] . This heterogeneous cluster uses innovative scheduling technology, Slurm [2] , that manages a network of CPUs and graphics processing units (GPUs). The GPU farm consists of a variety of processors ranging from low-end consumer grade devices such as the Nvidia GTX 970 to higher-end devices such as the GeForce RTX 2080. These GPUs are essential to our research since they allow extremely compute-intensive deep learning tasks to be executed on massive data resources such as the TUH EEG Corpus [2] . We use TensorFlow [3] as the core machine learning library for our deep learning systems, and routinely employ multiple GPUs to accelerate the training process.","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":"123631906","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}
Pub Date : 2019-12-01DOI: 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
Core body temperature measurement using an ingestible pill has been proven effective for field-based ambulatory applications. The ingestible pill overcomes many impracticalities related with traditional methods of assessing core body temperature, however, it suffers from two key issues: random gaps due to missing data and outliers due to electromagnetic intereference. In this paper, we propose a principled convex optimization based framework for preprocessing the raw core body temperature signal. The proposed framework assumes that the raw core body temperature signal consists of two components: a smooth low-frequency component and a transient component with sparse outliers. We derive a computationally efficient algorithm using the majorization-minimization procedure and show its performance on simulated data. Finally, we demonstrate utility of the proposed method for estimating the circadian rhythm of core body temperature in cognitively normal elderly.
{"title":"Nonlinear Smoothing of Data with Random Gaps and Outliers (DRAGO) Improves Estimation of Circadian Rhythm","authors":"A. Parekh, I. Ayappa, R. Osorio, I. Selesnick, A. Baroni, M. Miller, B. Cavedoni, H. Sanders, A. Varga, E. Blessing, D. Rapoport","doi":"10.1109/SPMB47826.2019.9037837","DOIUrl":"https://doi.org/10.1109/SPMB47826.2019.9037837","url":null,"abstract":"Core body temperature measurement using an ingestible pill has been proven effective for field-based ambulatory applications. The ingestible pill overcomes many impracticalities related with traditional methods of assessing core body temperature, however, it suffers from two key issues: random gaps due to missing data and outliers due to electromagnetic intereference. In this paper, we propose a principled convex optimization based framework for preprocessing the raw core body temperature signal. The proposed framework assumes that the raw core body temperature signal consists of two components: a smooth low-frequency component and a transient component with sparse outliers. We derive a computationally efficient algorithm using the majorization-minimization procedure and show its performance on simulated data. Finally, we demonstrate utility of the proposed method for estimating the circadian rhythm of core body temperature in cognitively normal elderly.","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":"130452815","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}
Pub Date : 2019-12-01DOI: 10.1109/SPMB47826.2019.9037862
S. Makrogiannis, A. Okorie, T. Biswas, L. Ferrucci
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.
{"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":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":"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}
Pub Date : 2019-12-01DOI: 10.1109/spmb47826.2019.9037830
{"title":"SPMB 2019 Organizing Committee","authors":"","doi":"10.1109/spmb47826.2019.9037830","DOIUrl":"https://doi.org/10.1109/spmb47826.2019.9037830","url":null,"abstract":"","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":"128690603","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}
Pub Date : 2019-12-01DOI: 10.1109/SPMB47826.2019.9037841
Z. Kane, E. Stecco, A. Napoli, C. Tucker, I. Obeid
This work introduces a closed loop virtual reality platform for rehabilitating members of the armed forces after concussion or lower extremity musculoskeletal injury. Subjects perform a virtual variable-speed foot patrol designed to bring the subject’s heartrate up to an operator-designated value. Relevant biometric measurements are timestamped and recorded for post-hoc analysis, including heart (ECG), brain (EEG), and movement kinematics of the hands, feet, hips, and head. The long-term goal is to use these data to guide return-to-duty decision making and to support efficient rehabilitation protocols. The platform is physically compact for ease of deployment and has been designed in a modular fashion to allow easy integration of new sensors in future designs.
{"title":"The Instrumented Multitask Assessment System (IMAS)","authors":"Z. Kane, E. Stecco, A. Napoli, C. Tucker, I. Obeid","doi":"10.1109/SPMB47826.2019.9037841","DOIUrl":"https://doi.org/10.1109/SPMB47826.2019.9037841","url":null,"abstract":"This work introduces a closed loop virtual reality platform for rehabilitating members of the armed forces after concussion or lower extremity musculoskeletal injury. Subjects perform a virtual variable-speed foot patrol designed to bring the subject’s heartrate up to an operator-designated value. Relevant biometric measurements are timestamped and recorded for post-hoc analysis, including heart (ECG), brain (EEG), and movement kinematics of the hands, feet, hips, and head. The long-term goal is to use these data to guide return-to-duty decision making and to support efficient rehabilitation protocols. The platform is physically compact for ease of deployment and has been designed in a modular fashion to allow easy integration of new sensors in future designs.","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":"128879664","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}
Pub Date : 2019-12-01DOI: 10.1109/SPMB47826.2019.9037829
Bryant M. Baldwin, G. Angus-Barker, S. Joseph, M. Figarola, M. Cohen, C. Malozzi, J. Kar
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.
本研究调查了机械收缩参数的测量，如应变、扭转和左心室射血分数(LVEF)，是否表明暴露于蒽环类和曲妥珠单抗类化疗药物(CA)的患者可能发生左心室(LV)重构。一个同样重要的目标是使用单扫描心脏应变分析工具来研究这种收缩性，该工具包括MRI扫描的受激回声位移编码(DENSE)序列和径向点插值方法(RPIM)。研究人员收集了11例暴露于CA制剂、正在接受乳腺癌抗肿瘤药物治疗和/或正在接受心脏并发症治疗的患者的数据。观察者间应变测量结果的Bland-Altman分析表明，纵向应变为0.01±0.06°，扭转为0.10±1.92°。在患者群体中，左室增大表明他们的舒张直径在健康受试者中有显著差异。患者与健康者的纵向应变分别为0.15±0.03 vs 0.21±0.04 (p=0.02)和0.17±0.02 vs 0.22±0.03 (p=0.01)。在患者和健康受试者的中脑室和基底亚区，扭转的结果相似。统计分析的结果显示，这些患者发生左室重构和纤维化的可能性，否则LVEF测量无法显示。
{"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":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":"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}
Pub Date : 2019-12-01DOI: 10.1109/spmb47826.2019.9037839
{"title":"[SPMB 2019 Title Page]","authors":"","doi":"10.1109/spmb47826.2019.9037839","DOIUrl":"https://doi.org/10.1109/spmb47826.2019.9037839","url":null,"abstract":"","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":"134236751","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}
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