1,999 research outputs found
Intelligent Biosignal Analysis Methods
This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others
RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection
The brain electrical activity presents several short events during sleep that
can be observed as distinctive micro-structures in the electroencephalogram
(EEG), such as sleep spindles and K-complexes. These events have been
associated with biological processes and neurological disorders, making them a
research topic in sleep medicine. However, manual detection limits their study
because it is time-consuming and affected by significant inter-expert
variability, motivating automatic approaches. We propose a deep learning
approach based on convolutional and recurrent neural networks for sleep EEG
event detection called Recurrent Event Detector (RED). RED uses one of two
input representations: a) the time-domain EEG signal, or b) a complex
spectrogram of the signal obtained with the Continuous Wavelet Transform (CWT).
Unlike previous approaches, a fixed time window is avoided and temporal context
is integrated to better emulate the visual criteria of experts. When evaluated
on the MASS dataset, our detectors outperform the state of the art in both
sleep spindle and K-complex detection with a mean F1-score of at least 80.9%
and 82.6%, respectively. Although the CWT-domain model obtained a similar
performance than its time-domain counterpart, the former allows in principle a
more interpretable input representation due to the use of a spectrogram. The
proposed approach is event-agnostic and can be used directly to detect other
types of sleep events.Comment: 8 pages, 5 figures. In proceedings of the 2020 International Joint
Conference on Neural Networks (IJCNN 2020
Data Augmentation for Deep-Learning-Based Electroencephalography
Background: Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)—increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc.
New method: We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected?
Results: DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively.
Comparing with existing methods: Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8% for recombination of segmentation and 36% for noise addition and from 14% for motor imagery to 56% for mental workload—29% on average.
Conclusions: DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications—if adhering to our reporting guidelines—will facilitate more detailed analysis
Data Augmentation for Deep-Learning-Based Electroencephalography
Background: Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)—increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc.
New method: We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected?
Results: DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively.
Comparing with existing methods: Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8% for recombination of segmentation and 36% for noise addition and from 14% for motor imagery to 56% for mental workload—29% on average.
Conclusions: DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications—if adhering to our reporting guidelines—will facilitate more detailed analysis
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