Neonatal seizure detection using convolutional neural networks

O'Shea, Alison; Lightbody, Gordon; Boylan, Geraldine B.; Temko, Andriy

doi:10.1109/mlsp.2017.8168193

Cited by 39 publications

(35 citation statements)

References 24 publications

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“…Multiple studies also used overlapping windows as a way to augment their data, although many did not explicitly frame this as data augmentation. In [185,123], overlapping windows were explicitly used as a data augmentation technique. In [83], different shift lengths between overlapping windows (from 10 ms to 60 ms out of a 2-s window) were compared, showing that by generating more training samples with smaller shifts, performance improved significantly.…”

Section: Data Augmentationmentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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Context. Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question.Objective. In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, braincomputer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations.Methods. Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. * The first two authors contributed equally to this work.Significance. To help the community progress and share work more effectively, we provide a list of recommendations for future studies. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly.

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Section: Data Augmentationmentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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“…This paper covers the various layers used in CNN model, their explanation and usage to form a structure for image analysis. This paper clarifies the various misnomers about the complexity of the CNN models [5].…”

Section: An Introduction To Convolutional Neural Networkmentioning

confidence: 69%

“…CNN was initially used to resolve image driven pattern recognition, started with simple architectures, to solve simple problems [5]. As compare to other forms of ANN, CNN primarily focuses on tapping of knowledge of specified input.…”

Section: An Introduction To Convolutional Neural Networkmentioning

confidence: 99%

Analytical Study of Image Classsification Using Deep Learning

yousuf

2018

ijarcs

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Abstract:The machine learning technology has received increased attention in recent years in several vision tasks such as image classification, image detection, and image recognition. In particular, recent advances of machine learning techniques bring encouragement to image classification with convolutional neural networks. CNN has been established as a powerful class of models for image recognition problems and even in some cases they outperform humans. The main purpose of the work presented in this paper is the rise and development of machine learning, deep learning, CNN and to give an overview on using machine learning for image classification. At the end the comparison of CNN with traditional method is discussed.

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“…Moreover, neurons in a layer are arranged in three dimensions: width, height, and depth. CNNs are primarily designed to encode spatial information available in images and make the network more suited to image focused tasks [27]. Regular neural networks struggle from computational complexity and overfitting with an increase in the size of the input.…”

Section: Feature Extractionmentioning

confidence: 99%

A Convolutional Neural Network Approach for Assisting Avalanche Search and Rescue Operations with UAV Imagery

et al. 2017

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Abstract:Following an avalanche, one of the factors that affect victims' chance of survival is the speed with which they are located and dug out. Rescue teams use techniques like trained rescue dogs and electronic transceivers to locate victims. However, the resources and time required to deploy rescue teams are major bottlenecks that decrease a victim's chance of survival. Advances in the field of Unmanned Aerial Vehicles (UAVs) have enabled the use of flying robots equipped with sensors like optical cameras to assess the damage caused by natural or manmade disasters and locate victims in the debris. In this paper, we propose assisting avalanche search and rescue (SAR) operations with UAVs fitted with vision cameras. The sequence of images of the avalanche debris captured by the UAV is processed with a pre-trained Convolutional Neural Network (CNN) to extract discriminative features. A trained linear Support Vector Machine (SVM) is integrated at the top of the CNN to detect objects of interest. Moreover, we introduce a pre-processing method to increase the detection rate and a post-processing method based on a Hidden Markov Model to improve the prediction performance of the classifier. Experimental results conducted on two different datasets at different levels of resolution show that the detection performance increases with an increase in resolution, while the computation time increases. Additionally, they also suggest that a significant decrease in processing time can be achieved thanks to the pre-processing step.

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Neonatal seizure detection using convolutional neural networks

Cited by 39 publications

References 24 publications

Deep learning-based electroencephalography analysis: a systematic review

Deep learning-based electroencephalography analysis: a systematic review

Analytical Study of Image Classsification Using Deep Learning

A Convolutional Neural Network Approach for Assisting Avalanche Search and Rescue Operations with UAV Imagery

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