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

• URL: http://arxiv.org/abs/2008.08118v1
• Date: Thu, 6 Aug 2020 19:50:29 GMT
• Title: Review of Machine Learning Algorithms for Brain Stroke Diagnosis and Prognosis by EEG Analysis
• Authors: Mohammad-Parsa Hosseini, Cecilia Hemingway, Jerard Madamba, Alexander McKee, Natalie Ploof, Jennifer Schuman, and Elliot Voss
• Abstract summary: Strokes are the leading cause of adult disability in the United States. Brain-Computer Interfaces (BCIs) help the patient either restore neurologic pathways or effectively communicate with an electronic prosthetic. The various machine learning techniques and algorithms that are addressed and combined with BCIs technology show that the use of BCIs for stroke treatment is a promising and rapidly expanding field.
• Score: 50.591267188664666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Currently, strokes are the leading cause of adult disability in the United States. Traditional treatment and rehabilitation options such as physical therapy and tissue plasminogen activator are limited in their effectiveness and ability to restore mobility and function to the patient. As a result, there exists an opportunity to greatly improve the treatment for strokes. Machine learning, specifically techniques that utilize Brain-Computer Interfaces (BCIs) to help the patient either restore neurologic pathways or effectively communicate with an electronic prosthetic, show promising results when applied to both stroke diagnosis and rehabilitation. In this review, sources that design and implement BCIs for treatment of stroke patients are evaluated and categorized based on their successful applications for stroke diagnosis or stroke rehabilitation. 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.

Related papers

• Toward an Integrated Decision Making Framework for Optimized Stroke Diagnosis with DSA and Treatment under Uncertainty [0.0]
  Current diagnostic methods, including Digital Subtraction Angiography (DSA), face limitations due to high costs and its invasive nature. We propose a novel approach using a Partially Observable Markov Decision Process (POMDP) framework. Our model integrates advanced diagnostic tools and treatment approaches with a decision-making algorithm that accounts for the inherent uncertainties in stroke diagnosis.
  arXiv  Detail & Related papers  (2024-07-24T03:01:55Z)
• Computer aided diagnosis system for Alzheimers disease using principal component analysis and machine learning based approaches [0.0]
  Alzheimers disease (AD) is a severe neurological brain disorder. It is not curable, but earlier detection can help improve symptoms in a great deal. The machine learning based approaches are popular and well motivated models for medical image processing tasks.
  arXiv  Detail & Related papers  (2024-04-15T15:49:11Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)
• Automated Fidelity Assessment for Strategy Training in Inpatient Rehabilitation using Natural Language Processing [53.096237570992294]
  Strategy training is a rehabilitation approach that teaches skills to reduce disability among those with cognitive impairments following a stroke. Standardized fidelity assessment is used to measure adherence to treatment principles. We developed a rule-based NLP algorithm, a long-short term memory (LSTM) model, and a bidirectional encoder representation from transformers (BERT) model for this task.
  arXiv  Detail & Related papers  (2022-09-14T15:33:30Z)
• A review of machine learning approaches, challenges and prospects for computational tumor pathology [1.2036642553849346]
  Tumor computational pathology challenges data integration, hardware processing, network sharing bandwidth and machine learning technology. This review investigates preprocessing methods in computational pathology from a pathological and technical perspective. The challenges and prospects of machine learning in computational pathology applications are discussed.
  arXiv  Detail & Related papers  (2022-05-31T14:56:01Z)
• Enabling AI and Robotic Coaches for Physical Rehabilitation Therapy: Iterative Design and Evaluation with Therapists and Post-Stroke Survivors [66.07833535962762]
  Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction. Previous work explored the potential of automatically monitoring exercises for AI and robotic coaches, but deployment remains a challenge. We present our efforts on eliciting the detailed design specifications on how AI and robotic coaches could interact with and guide patient's exercises.
  arXiv  Detail & Related papers  (2021-06-15T22:06:39Z)
• Applications of Epileptic Seizures Detection in Neuroimaging Modalities Using Deep Learning Techniques: Methods, Challenges, and Future Works [12.393282115173387]
  Epileptic seizures are a type of neurological disorder that affect many people worldwide. Specialist physicians and neurologists take advantage of structural and functional neuroimaging modalities to diagnose various types of epileptic seizures. One method to accelerate the accurate and fast diagnosis of epileptic seizures is to employ computer aided diagnosis systems (CADS) based on artificial intelligence (AI) and functional and structural neuroimaging modalities.
  arXiv  Detail & Related papers  (2021-05-29T12:00:39Z)
• Learning Binary Semantic Embedding for Histology Image Classification and Retrieval [56.34863511025423]
  We propose a novel method for Learning Binary Semantic Embedding (LBSE) Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images. Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
  arXiv  Detail & Related papers  (2020-10-07T08:36:44Z)
• Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review [14.639115166647871]
  Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. Deep learning (DL) methods for diagnosis of ASD have been focused on neuroimaging-based approaches. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated.
  arXiv  Detail & Related papers  (2020-07-02T17:49:19Z)
• A Review of Computational Approaches for Evaluation of Rehabilitation Exercises [58.720142291102135]
  This paper reviews computational approaches for evaluating patient performance in rehabilitation programs using motion capture systems. The reviewed computational methods for exercise evaluation are grouped into three main categories: discrete movement score, rule-based, and template-based approaches.
  arXiv  Detail & Related papers  (2020-02-29T22:18:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.