Rotational data augmentation for electroencephalographic data

Krell, Mario Michael; Kim, Su Kyoung

doi:10.1109/embc.2017.8036864

Cited by 52 publications

(48 citation statements)

References 13 publications

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“…The augmentation of multi-channel EEG data by means of spatial rotation has been proposed in Krell and Kim ( 2017 ), but this cannot be applied to single channel data. We are currently also lacking physically informed and quantitatively well understood models for other possible effects, e.g., noise sources.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG

Bresch

Großekathöfer

Garcia-Molina

2018

Front. Comput. Neurosci.

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Objective: We investigate the design of deep recurrent neural networks for detecting sleep stages from single channel EEG signals recorded at home by non-expert users. We report the effect of data set size, architecture choices, regularization, and personalization on the classification performance.Methods: We evaluated 58 different architectures and training configurations using three-fold cross validation.Results: A network consisting of convolutional (CONV) layers and long short term memory (LSTM) layers can achieve an agreement with a human annotator of Cohen's Kappa of ~0.73 using a training data set of 19 subjects. Regularization and personalization do not lead to a performance gain.Conclusion: The optimal neural network architecture achieves a performance that is very close to the previously reported human inter-expert agreement of Kappa 0.75.Significance: We give the first detailed account of CONV/LSTM network design process for EEG sleep staging in single channel home based setting.

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Section: Experiments and Resultsmentioning

confidence: 99%

Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG

Bresch

Großekathöfer

Garcia-Molina

2018

Front. Comput. Neurosci.

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“…There was a large imbalance between the numbers of positive and negative samples in this experiment, which could have led to poor results; thus, we introduced data augmentation technology in training sets to resolve the problem. Data augmentation is a popular method for dealing with insufficient sample sizes and sample imbalances in data mining [ 27 , 29 ]. For dataset_1, the unified image size was initially 500 × 300; then, the ECG images were augmented using different cropping methods (left top ( Figure 1 C,D), right bottom ( Figure 1 E,F), and center ( Figure 1 G,H), etc.…”

Section: Methodsmentioning

confidence: 99%

“…It converts initial data into a form that is suitable for computation. Existing ECG preprocessing techniques mainly involve wavelet transforms-to reduce noise, eliminate baseline drift, and data segmentation [27], which is a complex procedure. Studies have been published in which the original ECGs did not undergo a significant amount of preprocessing; instead, after performing random cropping operations, they were directly inputted into two-dimensional CNNs (2D-CNNs) in the form of grayscale images for training; this resulted in an average accuracy and sensitivity of 0.991 and 0.979, respectively [28].…”

Section: Electrocardiogram Preprocessingmentioning

confidence: 99%

IGRNet: A Deep Learning Model for Non-Invasive, Real-Time Diagnosis of Prediabetes through Electrocardiograms

Wang

Zhao

et al. 2020

Sensors

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The clinical symptoms of prediabetes are mild and easy to overlook, but prediabetes may develop into diabetes if early intervention is not performed. In this study, a deep learning model—referred to as IGRNet—is developed to effectively detect and diagnose prediabetes in a non-invasive, real-time manner using a 12-lead electrocardiogram (ECG) lasting 5 s. After searching for an appropriate activation function, we compared two mainstream deep neural networks (AlexNet and GoogLeNet) and three traditional machine learning algorithms to verify the superiority of our method. The diagnostic accuracy of IGRNet is 0.781, and the area under the receiver operating characteristic curve (AUC) is 0.777 after testing on the independent test set including mixed group. Furthermore, the accuracy and AUC are 0.856 and 0.825, respectively, in the normal-weight-range test set. The experimental results indicate that IGRNet diagnoses prediabetes with high accuracy using ECGs, outperforming existing other machine learning methods; this suggests its potential for application in clinical practice as a non-invasive, prediabetes diagnosis technology.

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“…To address such issue, data augmentation method have been applied to generate EEG data recently. Some researchers have generated EEG data by applying a geometric transformation to the original data [36]- [37]. Other researchers have focused on using deep generative models to generate artificial EEG data [2], [38]- [40].…”

Section: Related Workmentioning

confidence: 99%

Ground Truth Dataset for EEG-Based Emotion Recognition With Visual Indication

et al. 2020

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Along with extensive and successive applications, emotion recognition based on electroencephalogram has attracted more and more researchers. However, how to acquire sufficient and high-quality real emotion electroencephalogram to train emotion recognition model has always been a bottleneck issue in the electroencephalogram-based emotion recognition research field. Because the subject's emotion is easy to be affected by many factors and subjects do not always evoke emotion well, it's very hard to determine whether the real emotion electroencephalogram appears or not in the experiments. On the contrary to electroencephalogram, facial expression obtained without any intentional control is easy to be recognized by computers, and is also one of the primary and reliable cues for understanding emotions. Inspired by such a common sense, we proposed an approach to building up ground truth electroencephalogram dataset with visual indication. Firstly, the relationship between facial expression and electroencephalogram is analyzed in details from the viewpoints of biophysics and correlation. Secondly, based on the analysis result that when the subject's facial expression is evoked, it should be accompanied by the emotional electroencephalogram corresponding to it, the method of building up the ground truth electroencephalogram dataset with visual indication is put forward along with the computer automatic implementation. Thirdly, we have built up a ground truth electroencephalogram dataset which covers 3 kinds of emotions (i.e. joy, sadness, and neutral) of the subjects who are undergraduate and graduate students from Minzu University of China. Lastly, the validation of the established dataset is tested by the comparative experiments between the long short term memory emotion recognition models trained with two different datasets respectively. One dataset contains both truth and false electroencephalograms and another one only contains the truth data.

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Rotational data augmentation for electroencephalographic data

Abstract: This shows that our processing efficient approach generates meaningful data and encourages to look for further new methods for EEG data augmentation.

Cited by 52 publications

References 13 publications

Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG

Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG

IGRNet: A Deep Learning Model for Non-Invasive, Real-Time Diagnosis of Prediabetes through Electrocardiograms

Ground Truth Dataset for EEG-Based Emotion Recognition With Visual Indication

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