scholarly journals Seizure prediction in genetic rat models of absence epilepsy: improved performance through multiple-site cortico-thalamic recordings combined with machine learning

eNeuro ◽  
2021 ◽  
pp. ENEURO.0160-21.2021
Author(s):  
Björn Budde ◽  
Vladimir Maksimenko ◽  
Kelvin Sarink ◽  
Thomas Seidenbecher ◽  
Gilles van Luijtelaar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Absence Epilepsy ◽  
Seizure Prediction ◽  
Multiple Site ◽  
Rat Models ◽  
Improved Performance

scholarly journals An Evolving-Dynamic Network Activity Approach to Epileptic Seizure Prediction using Machine Learning

2020 ◽  
Author(s):  
Christine Joy Liu ◽  
Jordan Sorokin ◽  
Surya Ganguli ◽  
John Huguenard
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Logistic Regression ◽  
Convolutional Neural Networks ◽  
Spectral Power ◽  
Dynamic Network ◽  
Brain Regions ◽  
Absence Epilepsy ◽  
Network Activity ◽  
Seizure Prediction

AbstractAbsence epilepsy is a neurological condition characterized by abnormally synchronous electrical activity within two mutually connected brain regions, the thalamus and cortex, that results in seizures and affects more than 6.5 million people. Epilepsy is commonly studied through the use of the electroencephalogram (EEG), a device that monitors brain waves over time. In this study, we introduced machine learning models to predict epileptic seizures in two ways, one to train logistic regression models to provide an accurate decision boundary to predict based off frequency features, and second to train convolutional neural networks to predict based off spectral power images from EEG. This pipeline employed a two model approach, using logistic regression and convolutional neural networks to predict seizures. The evaluation, performed on data from 9 mice, achieved prediction accuracies of 98%. The proposed methodology introduces a novel aspect of looking at predicting absence seizures, which are known to be short events, in addition to the comparison between a time-dependent and time-agnostic seizure prediction classifier. The overall goal of these experiments were to build a model that can accurately predict whether or not a seizure will occur.

scholarly journals In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare

2021 ◽  
Vol 4 ◽  
Author(s):  
Mustafa Y. Topaloglu ◽  
Elisabeth M. Morrell ◽  
Suraj Rajendran ◽  
Umit Topaloglu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Dice Coefficient ◽  
New Paradigm ◽  
Underrepresented Groups ◽  
Patient Protection ◽  
Shapley Values ◽  
Geographically Distributed ◽  
Computationally Expensive ◽  
Improved Performance

Artificial Intelligence and its subdomain, Machine Learning (ML), have shown the potential to make an unprecedented impact in healthcare. Federated Learning (FL) has been introduced to alleviate some of the limitations of ML, particularly the capability to train on larger datasets for improved performance, which is usually cumbersome for an inter-institutional collaboration due to existing patient protection laws and regulations. Moreover, FL may also play a crucial role in circumventing ML’s exigent bias problem by accessing underrepresented groups’ data spanning geographically distributed locations. In this paper, we have discussed three FL challenges, namely: privacy of the model exchange, ethical perspectives, and legal considerations. Lastly, we have proposed a model that could aide in assessing data contributions of a FL implementation. In light of the expediency and adaptability of using the Sørensen–Dice Coefficient over the more limited (e.g., horizontal FL) and computationally expensive Shapley Values, we sought to demonstrate a new paradigm that we hope, will become invaluable for sharing any profit and responsibilities that may accompany a FL endeavor.

scholarly journals Machine Learning and Prediction-Based Resource Management in IoT Considering Qos

2019 ◽  
Vol 8 (2) ◽  
pp. 687-694
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Short Term Memory ◽  
Real Data ◽  
Computing Methods ◽  
Energy Utilization ◽  
Real Field ◽  
Proposed Model ◽  
Improved Performance ◽  
Video Sensors

Internet of Things (IoT) is one of the fast-growing technology paradigms used in every sectors, where in the Quality of Service (QoS) is a critical component in such systems and usage perspective with respect to ProSumers (producer and consumers). Most of the recent research works on QoS in IoT have used Machine Learning (ML) techniques as one of the computing methods for improved performance and solutions. The adoption of Machine Learning and its methodologies have become a common trend and need in every technologies and domain areas, such as open source frameworks, task specific algorithms and using AI and ML techniques. In this work we propose an ML based prediction model for resource optimization in the IoT environment for QoS provisioning. The proposed methodology is implemented by using a multi-layer neural network (MNN) for Long Short Term Memory (LSTM) learning in layered IoT environment. Here the model considers the resources like bandwidth and energy as QoS parameters and provides the required QoS by efficient utilization of the resources in the IoT environment. The performance of the proposed model is evaluated in a real field implementation by considering a civil construction project, where in the real data is collected by using video sensors and mobile devices as edge nodes. Performance of the prediction model is observed that there is an improved bandwidth and energy utilization in turn providing the required QoS in the IoT environment.

scholarly journals Deep Learning for Climate Model Output Statistics

2021 ◽  
Author(s):  
Michael Steininger ◽  
Daniel Abel ◽  
Katrin Ziegler ◽  
Anna Krause ◽  
Heiko Paeth ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Deep Learning ◽  
Climate Models ◽  
Climate Model ◽  
Model Output ◽  
Climate Change Effects ◽  
Climate Model Output ◽  
Improved Performance ◽  
Model Output Statistics

<p>Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential of deep learning with convolutional neural networks (CNNs) for MOS. We propose the CNN architecture ConvMOS specifically designed for reducing errors in climate model outputs and apply it to the climate model REMO. Our results show a considerable reduction of errors and mostly improved performance compared to three commonly used MOS approaches.</p>

scholarly journals Comparative Quality Estimation for Machine Translation Observations on Machine Learning and Features

2017 ◽  
Vol 108 (1) ◽  
pp. 307-318 ◽  
Author(s):  
Eleftherios Avramidis
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Machine Translation ◽  
State Of The Art ◽  
Linear Method ◽  
The State ◽  
Quality Estimation ◽  
Art Methods ◽  
Improved Performance

AbstractA deeper analysis on Comparative Quality Estimation is presented by extending the state-of-the-art methods with adequacy and grammatical features from other Quality Estimation tasks. The previously used linear method, unable to cope with the augmented features, is replaced with a boosting classifier assisted by feature selection. The methods indicated show improved performance for 6 language pairs, when applied on the output from MT systems developed over 7 years. The improved models compete better with reference-aware metrics.Notable conclusions are reached through the examination of the contribution of the features in the models, whereas it is possible to identify common MT errors that are captured by the features. Many grammatical/fluency features have a good contribution, few adequacy features have some contribution, whereas source complexity features are of no use. The importance of many fluency and adequacy features is language-specific.

Machine learning for seizure prediction: A revamped approach

2015 ◽  
Author(s):  
A. Sai Kumar ◽  
Lavi Nigam ◽  
Deepthi Karnam ◽  
Sreerama K. Murthy ◽  
Petro Fedorovych ◽  
...  
Keyword(s):  
Machine Learning ◽  
Seizure Prediction

An Overview of EEG-based Machine Learning Methods in Seizure Prediction and Opportunities for Neurologists in this Field

Neuroscience ◽  
2021 ◽  
Author(s):  
Buajieerguli Maimaiti ◽  
Hongmei Meng ◽  
Yudan Lv ◽  
Jiqing Qiu ◽  
Zhanpeng Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Seizure Prediction ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals The Conceptual Design of a Novel Workstation for Seizure Prediction Using Machine Learning With Potential eHealth Applications

2019 ◽  
Vol 7 ◽  
pp. 1-10
Author(s):  
Antonio E. Teijeiro ◽  
Maryamsadat Shokrekhodaei ◽  
Homer Nazeran
Keyword(s):  
Machine Learning ◽  
Conceptual Design ◽  
Seizure Prediction

scholarly journals Comparison of machine learning models for seizure prediction in hospitalized patients

2019 ◽  
Vol 6 (7) ◽  
pp. 1239-1247 ◽  
Author(s):  
Aaron F. Struck ◽  
Andres A. Rodriguez‐Ruiz ◽  
Gamaledin Osman ◽  
Emily J. Gilmore ◽  
Hiba A. Haider ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospitalized Patients ◽  
Seizure Prediction ◽  
Learning Models ◽  
Machine Learning Models
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