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Spatially Regularized SVM for the Detection of Brain Areas Associated with Stroke Outcome [chapter]

Rémi Cuingnet, Charlotte Rosso, Stéphane Lehéricy, Didier Dormont, Habib Benali, Yves Samson, Olivier Colliot
2010 Lecture Notes in Computer Science  
We then applied it to 72 stroke patients to detect brain areas associated with motor outcome at 90 days, based on diffusion-weighted images acquired at the acute stage (one day delay).  ...  First, we propose to spatially regularize the SVM using a graph encoding the voxels' proximity. Two examples of regularization graphs are provided.  ...  The proposed approach was applied to the detection of brain areas associated with stroke outcome based on DWI acquired at the acute stages.  ... 
doi:10.1007/978-3-642-15705-9_39 fatcat:eant7pkgyjef3nzmknpudg3uqu

Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome

Rémi Cuingnet, Charlotte Rosso, Marie Chupin, Stéphane Lehéricy, Didier Dormont, Habib Benali, Yves Samson, Olivier Colliot
2011 Medical Image Analysis  
It was then applied to 72 stroke patients to detect brain areas associated with motor outcome at 90 days, based on diffusionweighted images acquired at the acute stage (median delay one day).  ...  In this paper, we propose a new method to detect differences at the group level in brain images based on spatially regularized support vector machines (SVM).  ...  Acknowledgments This work was partially supported by the ''Programme Hospitalier de Recherche Clinique EVAL-USINV'' (No. AOM 03 008). This work was partially supported by ANR (Project HM-TC, No.  ... 
doi:10.1016/j.media.2011.05.007 pmid:21752695 fatcat:5wjjk6q7sbgzjm6ayw6qrcrw5u

Classification of rhythmic cortical activity elicited by whole-body balance perturbations suggests the cortical representation of directionspecific changes in postural stability

Teodoro Solis-Escalante, Digna De Kam, Vivian Weerdesteyn
2020 IEEE transactions on neural systems and rehabilitation engineering  
) for the classification of four different sway directions (one vs. the rest scheme).  ...  Using common spatial patterns for feature extraction and linear discriminant analysis or support vector machines for classification, we achieved classification accuracies above random level (p<0.05; cross-validated  ...  ACKNOWLEDGMENT The authors would like to express their gratitude to Joris van der Cruijsen for assistance with data collection during creation of the database.  ... 
doi:10.1109/tnsre.2020.3028966 pmid:33021931 fatcat:gjirphrnundr3nvfji3dfwly2u

Machine Learning in Acute Ischemic Stroke Neuroimaging

Haris Kamal, Victor Lopez, Sunil A. Sheth
2018 Frontiers in Neurology  
The evaluation and treatment of Acute Ischemic Stroke (AIS) have experienced a significant advancement over the past few years, increasingly requiring the use of neuroimaging for decision-making.  ...  Machine Learning (ML) through pattern recognition algorithms is currently becoming an essential aid for the diagnosis, treatment, and prediction of complications and patient outcomes in a number of neurological  ...  The outcome of acute ischemic stroke patients is dependent on therapy, and risks for complications should be considered when deciding for stroke therapy. Yu et al.  ... 
doi:10.3389/fneur.2018.00945 pmid:30467491 pmcid:PMC6236025 fatcat:w345o2rjg5dpvjemhglqcthhdi

Early Identification of Potentially Salvageable Tissue with MRI-Based Predictive Algorithms after Experimental Ischemic Stroke

Mark JRJ Bouts, Ivo ACW Tiebosch, Annette van der Toorn, Max A Viergever, Ona Wu, Rick M Dijkhuizen
2013 Journal of Cerebral Blood Flow and Metabolism  
Our study shows that assessment of the heterogeneity of infarction probability with MRI-based algorithms enables estimation of the extent of potentially salvageable tissue after acute ischemic stroke.  ...  Individualized stroke treatment decisions can be improved by accurate identification of the extent of salvageable tissue.  ...  , for detection of hemodynamic disturbances, 5 provide sensitive and specific means for acute stroke diagnosis.  ... 
doi:10.1038/jcbfm.2013.51 pmid:23571283 pmcid:PMC3705436 fatcat:aj3xx45cprenllzjiktbyuzylu

Review of Machine Learning Algorithms for Brain Stroke Diagnosis and Prognosis by EEG Analysis [article]

Mohammad-Parsa Hosseini, Cecilia Hemingway, Jerard Madamba, Alexander McKee, Natalie Ploof, Jennifer Schuman, Elliot Voss
2020 arXiv   pre-print
The various machine learning techniques and algorithms that are addressed and combined with BCI technology show that the use of BCIs for stroke treatment is a promising and rapidly expanding field.  ...  Currently, strokes are the leading cause of adult disability in the United States.  ...  "Electroencephalography (EEG) for detecting acute ischemic stroke."  ... 
arXiv:2008.08118v1 fatcat:ipt2pr5xdfe7lhn7llo7migycu

Computational Analysis: A Bridge to Translational Stroke Treatment [chapter]

Nirmalya Ghosh, Yu Sun, Christine Turenius, Bir Bhanu, Andre Obenaus, Stephen Ashwal
2012 Translational Stroke Research  
We conclude with our assessment of probable future research directions in the fi eld of computational noninvasive stroke analysis such as automated detection of the ischemic core and penumbra, monitoring  ...  Objective rapid quantifi cation of injury using computational methods can improve the assessment of the degree of stroke injury, aid in the selection of patients for early or specifi c treatments, and  ...  to an atlas) and may lose information during spatial registration of the injured brain to the atlas brain.  ... 
doi:10.1007/978-1-4419-9530-8_42 fatcat:did4nx25jzgkjjt4onmamkytjm

Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images

U. Raghavendra, The-Hanh Pham, Anjan Gudigar, V. Vidhya, B. Nageswara Rao, Sukanta Sabut, Joel Koh En Wei, Edward J. Ciaccio, U. Rajendra Acharya
2021 Complex & Intelligent Systems  
The affected part of brain will not function properly after this attack. Hence, early detection is important for more efficacious treatment.  ...  There are two main types of brain stroke, ischemic and hemorrhagic. Ischemic brain stroke is caused by a lack of blood flow, and the haemorrhagic form is due to internal bleeding.  ...  Intracerebral haemorrhage (ICH) is one of the devastating stroke subtypes with poor outcome and high mortality rate within the first year.  ... 
doi:10.1007/s40747-020-00257-x fatcat:ztjnd45syvcp3gpnsdy5ukzdmy

MR Images, Brain Lesions, and Deep Learning

Darwin Castillo, Vasudevan Lakshminarayanan, María José Rodríguez-Álvarez
2021 Applied Sciences  
In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of white matter hyperintensities (WMHs) of brain magnetic  ...  For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app11041675 fatcat:ivwi2tp52ngstdoelbsjglhem4

Intention Detection of Gait Adaptation in Natural Settings [article]

Ines Domingos, Guang-Zhong Yang, Fani Deligianni
2021 arXiv   pre-print
We demonstrate that the method can not only successfully detect adaptation steps but also detect efficiently whether the subject adjust their pace to higher or lower speed.  ...  It allows monitoring of subjects in more realistic environment without the requirement of specialized equipment such as treadmill and foot pressure sensors.  ...  The feedback is provided by output of rehabilitation devices, for example, the movement of a prosthetic limb, activated with brain activity.  ... 
arXiv:1906.10747v2 fatcat:pe3xzjt7djfvrkr4nkwrbmqejq

Mining Acute Stroke Patients' Data Using Supervised Machine Learning [chapter]

Ritu Kundu, Toktam Mahmoodi
2017 Lecture Notes in Computer Science  
In this paper, we analyse the registry of brain stroke patients collected over fifteen years in south London hospitals, known as South London Stroke Register.  ...  Given that time is very crucial for stroke patients, main motivation of this research work is identifying the most effective treatment immediately for a new patient, and potentially increase the probability  ...  The authors would like to thank Miss Siobhan Crichton from the department of Primary Care & Public Health Sciences at King's College for providing support on the dataset, to the authors.  ... 
doi:10.1007/978-3-319-72453-9_30 fatcat:claqzd23efdv5d5xknlc5d7fre

Default mode network anatomy and function is linked to pediatric concussion recovery [article]

Kartik K Iyer, Andrew Zalesky, Karen M Barlow, Luca Cocchi
2019 bioRxiv   pre-print
The combination of structural and functional brain indices associated to individual variations in the default mode network accurately predicted clinical outcomes at follow-up (area under the curve = 0.86  ...  Results: Higher scores on a composite index of sleep disturbance, including fatigue, were associated with converging decreases in grey matter volume and local functional connectivity in two key nodes of  ...  For example, in stroke 15 and TBI 11, 16, 17 , the anatomy and function of the DMN are compromised and alterations within this network have been linked to poor outcomes.  ... 
doi:10.1101/795740 fatcat:dtvdavlh5zahzdsyffscibz7zu

Default mode network anatomy and function is linked to pediatric concussion recovery

Kartik K. Iyer, Andrew Zalesky, Karen M. Barlow, Luca Cocchi
2019 Annals of Clinical and Translational Neurology  
brain indices can predict individual recovery from PPCS.  ...  To determine whether anatomical and functional brain features relate to key persistent post-concussion symptoms (PPCS) in children recovering from mild traumatic brain injuries (mTBI), and whether such  ...  Conflict of Interest The authors have no competing interest or conflict of interest to declare.  ... 
doi:10.1002/acn3.50951 pmid:31755665 pmcid:PMC6917315 fatcat:txlbk2p5nnhn7dlt6xnxfvlik4

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Haroon Khan, Noman Naseer, Anis Yazidi, Per Kristian Eide, Hafiz Wajahat Hassan, Peyman Mirtaheri
2021 Frontiers in Human Neuroscience  
Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait  ...  Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low  ...  Rea et al. (2014) The study aimed to assess the measurement and classification of hemodynamic signals associated with lower limb motor movements for chronic stroke and its usage for future fNIRS-BCI rehabilitation  ... 
doi:10.3389/fnhum.2020.613254 pmid:33568979 pmcid:PMC7868344 fatcat:syxy7hu74fdj7e3azv7etcglya

Characterizing the Structural Pattern of Heavy Smokers Using Multivoxel Pattern Analysis

Yufeng Ye, Jian Zhang, Bingsheng Huang, Xun Cai, Panying Wang, Ping Zeng, Songxiong Wu, Jinting Ma, Han Huang, Heng Liu, Guo Dan, Guangyao Wu
2021 Frontiers in Psychiatry  
Such findings might provide insights for understanding the mechanism of chronic smoking and the creation of effective cessation treatment.  ...  In this study, a multivoxel pattern analysis using a searchlight algorithm and support vector machine was applied on structural magnetic resonance imaging to identify the spatial pattern of gray matter  ...  ACKNOWLEDGMENTS The authors want to thank the Zhongnan Hospital of Wuhan University and the participants who took part in the study and data collection.  ... 
doi:10.3389/fpsyt.2020.607003 pmid:33613332 pmcid:PMC7890259 fatcat:lg47w65uybcgbgpc5ky7j67xny
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