Pub Date : 2019-12-01DOI: 10.1109/SPMB47826.2019.9037856
F. Shamsi, L. Najafizadeh
This paper investigates the problem of classification of multi-class movement execution tasks from signals obtained via functional near infrared spectroscopy (fNIRS). fNIRS data is acquired from five healthy subjects while performing four types of motor execution tasks as well as a non-movement task (five classes in total). Various feature sets are extracted based on the mean of changes in the concentration of oxygenated hemoglobin ([ΔHbO]) signals computed across the [0 – 2], [1 – 3], and [2 – 4] sec intervals. A multi-class support vector machine classifier with a quadratic polynomial kernel (QSVM) is utilized to classify movement and non-movement classes (total of 5 classes) using the data from the three time intervals. Classification results revealed that the average accuracy obtained for data using [2 – 4] sec interval is higher than the other two (78.55%). In addition, a comparison between the classification results of the data obtained from only the motor cortex vs from multiple regions of the brain is done. Our results demonstrate that by using fNIRS data from different regions of the brain, the classification accuracy is improved by 10 – 12% as compared to the case when the data is used only from the motor region.
{"title":"Multi-class Classification of Motor Execution Tasks using fNIRS","authors":"F. Shamsi, L. Najafizadeh","doi":"10.1109/SPMB47826.2019.9037856","DOIUrl":"https://doi.org/10.1109/SPMB47826.2019.9037856","url":null,"abstract":"This paper investigates the problem of classification of multi-class movement execution tasks from signals obtained via functional near infrared spectroscopy (fNIRS). fNIRS data is acquired from five healthy subjects while performing four types of motor execution tasks as well as a non-movement task (five classes in total). Various feature sets are extracted based on the mean of changes in the concentration of oxygenated hemoglobin ([ΔHbO]) signals computed across the [0 – 2], [1 – 3], and [2 – 4] sec intervals. A multi-class support vector machine classifier with a quadratic polynomial kernel (QSVM) is utilized to classify movement and non-movement classes (total of 5 classes) using the data from the three time intervals. Classification results revealed that the average accuracy obtained for data using [2 – 4] sec interval is higher than the other two (78.55%). In addition, a comparison between the classification results of the data obtained from only the motor cortex vs from multiple regions of the brain is done. Our results demonstrate that by using fNIRS data from different regions of the brain, the classification accuracy is improved by 10 – 12% as compared to the case when the data is used only from the motor region.","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":"126494503","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 : 2019-09-06DOI: 10.1109/SPMB47826.2019.9037849
C. Podilchuk, Siddhartha Pachhai, Robert Warfsman, R. Mammone
Teleradiology is the transmission of radiographic images from one location to another for interpretation. Teleradiology service providers help to fill the need for sub-specialty expert consultants, vacation leaves, and overflow gaps in the onsite radiology facilities. Teleradiology has become a large and growing industry [1] . The integration standard called the Integrating of Healthcare Enterprise (IHE) [2] have been developed to address communication issues between medical imaging sites. However, the IHE standard allows different vendors to implement the standard in different ways [3] which significantly limits the ability to transmit and receive images between organizations in practice.
{"title":"On-Demand Teleradiology Using Smartphone Photographs as Proxies for DICOM Images","authors":"C. Podilchuk, Siddhartha Pachhai, Robert Warfsman, R. Mammone","doi":"10.1109/SPMB47826.2019.9037849","DOIUrl":"https://doi.org/10.1109/SPMB47826.2019.9037849","url":null,"abstract":"Teleradiology is the transmission of radiographic images from one location to another for interpretation. Teleradiology service providers help to fill the need for sub-specialty expert consultants, vacation leaves, and overflow gaps in the onsite radiology facilities. Teleradiology has become a large and growing industry [1] . The integration standard called the Integrating of Healthcare Enterprise (IHE) [2] have been developed to address communication issues between medical imaging sites. However, the IHE standard allows different vendors to implement the standard in different ways [3] which significantly limits the ability to transmit and receive images between organizations in practice.","PeriodicalId":143197,"journal":{"name":"2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124905847","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 : 2019-08-10DOI: 10.1109/SPMB47826.2019.9037845
Tongda Xu, Xiyan Cai, Yao Wang, X. Wang, Sohae Chung, E. Fieremans, J. Rath, S. Flanagan, Y. Lui
Mild Traumatic Brain Injury (mTBI) is a significant public health problem. The most troubling symptoms after mTBI are cognitive complaints. Studies show measurable differences between patients with mTBI and healthy controls with respect to tissue microstructure using diffusion MRI. However, it remains unclear which diffusion measures are the most informative with regard to cognitive functions in both the healthy state as well as after injury. In this study, we use diffusion MRI to formulate a predictive model for performance on working memory based on the most relevant MRI features. As exhaustive search is impractical, the key challenge is to identify relevant features over a large feature space with high accuracy within reasonable time-frame. To tackle this challenge, we propose a novel improvement of the best first search approach with crossover operators inspired by genetic algorithm. Compared against other heuristic feature selection algorithms, the proposed method achieves significantly more accurate predictions and yields clinically interpretable selected features (improvement of r2 in 8 of 9 cohorts and up to 0.08).
{"title":"Identification of Relevant Diffusion MRI Metrics Impacting Cognitive Functions Using a Novel Feature Selection Method","authors":"Tongda Xu, Xiyan Cai, Yao Wang, X. Wang, Sohae Chung, E. Fieremans, J. Rath, S. Flanagan, Y. Lui","doi":"10.1109/SPMB47826.2019.9037845","DOIUrl":"https://doi.org/10.1109/SPMB47826.2019.9037845","url":null,"abstract":"Mild Traumatic Brain Injury (mTBI) is a significant public health problem. The most troubling symptoms after mTBI are cognitive complaints. Studies show measurable differences between patients with mTBI and healthy controls with respect to tissue microstructure using diffusion MRI. However, it remains unclear which diffusion measures are the most informative with regard to cognitive functions in both the healthy state as well as after injury. In this study, we use diffusion MRI to formulate a predictive model for performance on working memory based on the most relevant MRI features. As exhaustive search is impractical, the key challenge is to identify relevant features over a large feature space with high accuracy within reasonable time-frame. To tackle this challenge, we propose a novel improvement of the best first search approach with crossover operators inspired by genetic algorithm. Compared against other heuristic feature selection algorithms, the proposed method achieves significantly more accurate predictions and yields clinically interpretable selected features (improvement of r2 in 8 of 9 cohorts and up to 0.08).","PeriodicalId":143197,"journal":{"name":"2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131407720","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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