Efficient use of clinical EEG data for deep learning in epilepsy
نویسندگان
چکیده
• Augmenting datasets improves the performance of neural networks for interictal epileptiform discharge detection. Time shifting and different montages can reduce need annotated data. Deep learning may cause a fundamental shift in clinical EEG analysis. Automating detection Interictal Epileptiform Discharges (IEDs) electroencephalogram (EEG) recordings time spent on visual analysis diagnosis epilepsy. has shown potential this purpose, but scarceness expert data creates bottleneck process. We used EEGs from 50 patients with focal epilepsy, 49 generalized epilepsy (IEDs were visually labeled by experts) 67 controls. The was filtered, downsampled cut into two second epochs. increased number input samples containing IEDs through temporal using montages. A VGG C convolutional network trained to detect IEDs. Using dataset more samples, we reduced false positive rate 2.11 0.73 detections per minute at intersection sensitivity specificity. Sensitivity 63% 96% 99% model became less sensitive position IED epoch montage. Temporal use deep Dataset augmentation annotation, facilitating training networks, potentially leading
منابع مشابه
Data-efficient Deep Reinforcement Learning
Grasping an object and precisely stacking it on another is a difficult task for traditional robotic control or hand-engineered approaches. Here we examine the problem in simulation and provide techniques aimed at solving it via deep reinforcement learning. We introduce two straightforward extensions to the Deep Deterministic Policy Gradient algorithm (DDPG), which make it significantly more dat...
متن کاملEfficient Method Based on Combination of Deep Learning Models for Sentiment Analysis of Text
People's opinions about a specific concept are considered as one of the most important textual data that are available on the web. However, finding and monitoring web pages containing these comments and extracting valuable information from them is very difficult. In this regard, developing automatic sentiment analysis systems that can extract opinions and express their intellectual process has ...
متن کاملAn Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach
Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. Several automatic techniques have been proposed using traditional approaches to assist neurologists in detecting binary epile...
متن کاملDeep Feature Learning for EEG Recordings
We introduce and compare several strategies for learning discriminative features from electroencephalography (EEG) recordings using deep learning techniques. EEG data are generally only available in small quantities, they are highdimensional with a poor signal-to-noise ratio, and there is considerable variability between individual subjects and recording sessions. Our proposed techniques specif...
متن کاملData-efficient Deep Reinforcement Learning for Dexterous Manipulation
Deep learning and reinforcement learning methods have recently been used to solve a variety of problems in continuous control domains. An obvious application of these techniques is dexterous manipulation tasks in robotics which are difficult to solve using traditional control theory or hand-engineered approaches. One example of such a task is to grasp an object and precisely stack it on another...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Clinical Neurophysiology
سال: 2021
ISSN: ['1872-6224', '0168-5597']
DOI: https://doi.org/10.1016/j.clinph.2021.01.035