Anomaly Detection on Electroencephalography with Self-supervised Learning

Xu, Junjie; Zheng, Yaojia; Mao, Yifan; Wang, Ruixuan; Zheng, Wei‐Shi

doi:10.1109/bibm49941.2020.9313163

Cited by 15 publications

(15 citation statements)

References 13 publications

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“…For CutPaste, each normal EEG in each training set is considered as a gray image of size K×L pixels, and the suggested hyper-parameters from the original study [14] were adopted for model training. For ScaleNet, the method was re-implemented with suggested hyper-parameters [26]. As Table 2 shows, on all three datasets, our method (last row) outperforms all the baselines by a large margin.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The area under ROC curve (AUC) and its average and standard deviation over five runs (Setting I) or multiple rounds of validation (Setting II), the equal error rate (EER), and F1-score (at EER) were reported. Effectiveness evaluation: Our method was compared with well-known anomaly detection methods including the one-class SVM (OC-SVM) [20], the statistical kernel density estimation (KDE), and the autoencoder (AE) [7], the recently proposed methods Multi-Scale Convolutional Recurrent Encoder-Decoder (MS-CRED) [28] and Unsupervised Anomaly Detection (USAD) [3] for multivariate time series, and the recently proposed SSL methods for anomaly detection, including ScaleNet [26] and CutPaste [14]. Note that ScaleNet [26] uses frequencies of normal EEGs at multiple scales to help detect abnormal EEGs, without considering any characteristics in abnormal EEGs.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In contrast, developing an anomaly detector based on only normal EEGs has the potential to detect any possible unhealthy status (i.e., anomaly) in new EEG data. While multiple anomaly detection strategies have been developed for both natural and medical image analyses [22,24], including statistical approaches [13,18], discriminative approaches [4,25] reconstruction approaches [19,27] and self-supervised learning approaches [5,15], very limited studies have been investigated on anomaly detection based on normal EEGs only [26]. Furthermore, all these strategies build anomaly detectors without considering any property of anomaly due to absence of abnormal data during model development.…”

Section: Introductionmentioning

confidence: 99%

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Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography

Zheng¹,

Liu²,

Mo³

et al. 2022

Preprint

Self Cite

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Accurate automated analysis of electroencephalography (EEG) would largely help clinicians effectively monitor and diagnose patients with various brain diseases. Compared to supervised learning with labelled disease EEG data which can train a model to analyze specific diseases but would fail to monitor previously unseen statuses, anomaly detection based on only normal EEGs can detect any potential anomaly in new EEGs. Different from existing anomaly detection strategies which do not consider any property of unavailable abnormal data during model development, a task-oriented self-supervised learning approach is proposed here which makes use of available normal EEGs and expert knowledge about abnormal EEGs to train a more effective feature extractor for the subsequent development of anomaly detector. In addition, a specific two-branch convolutional neural network with larger kernels is designed as the feature extractor such that it can more easily extract both largerscale and small-scale features which often appear in unavailable abnormal EEGs. The effectively designed and trained feature extractor has shown to be able to extract better feature representations from EEGs for development of anomaly detector based on normal data and future anomaly detection for new EEGs, as demonstrated on three EEG datasets. The code is available at https://github.com/ironing/EEG-AD.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography

Zheng¹,

Liu²,

Mo³

et al. 2022

Preprint

Self Cite

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“…Zhang et al [191] proposed a general bio-signal framework referred to as time-frequency consistency (TF-C) by contrasting the samples in the time domain and the frequency domain, evaluating it in diagnosing the arrhythmia using ECG, epilepsy using EEG, and muscular diseases using EMG data. The latest work from Tang et al [202] proposed a self-supervised graph neural network to diagnose seizures on EEG, demonstrating that the self-supervised pre-training has consistently improved. It represents the spatial-temporal dependencies in EEGs using GNN and the self-supervised pretraining strategy to improve performance.…”

Section: Disease Diagnosismentioning

confidence: 99%

Pre-training in Medical Data: A Survey

et al. 2023

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Medical data refers to health-related information associated with regular patient care or as part of a clinical trial program. There are many categories of such data, such as clinical imaging data, bio-signal data, electronic health records (EHR), and multi-modality medical data. With the development of deep neural networks in the last decade, the emerging pre-training paradigm has become dominant in that it has significantly improved machine learning methods’ performance in a data-limited scenario. In recent years, studies of pre-training in the medical domain have achieved significant progress. To summarize these technology advancements, this work provides a comprehensive survey of recent advances for pre-training on several major types of medical data. In this survey, we summarize a large number of related publications and the existing benchmarking in the medical domain. Especially, the survey briefly describes how some pre-training methods are applied to or developed for medical data. From a data-driven perspective, we examine the extensive use of pre-training in many medical scenarios. Moreover, based on the summary of recent pre-training studies, we identify several challenges in this field to provide insights for future studies.

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“…Self-supervised learning algorithms are commonly used in medical research for detecting irregularities in patients' records. They are successfully employed for detecting epileptic seizures [81], pulmonary diseases [82], Parkinson disease [83], and retinal diseases [84]. In addition, they are applied to different modalities of medical data, including Computed Tomography (CT) scans [85], 3D volumetric CT data [41], X-ray scans [86], optical coherence tomography (OCT) [87], Spectral Domain -optical coherence tomography images (SD-OCT) [88], and MRI images [89], [90].…”

Section: Application Domainsmentioning

confidence: 99%

Self-Supervised Anomaly Detection: A Survey and Outlook

Hojjati¹,

Ho²,

Armanfard³

2022

Preprint

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Over the past few years, anomaly detection, a subfield of machine learning that is mainly concerned with the detection of rare events, witnessed an immense improvement following the unprecedented growth of deep learning models. Recently, the emergence of self-supervised learning has sparked the development of new anomaly detection algorithms that surpassed state-of-the-art accuracy by a significant margin. This paper aims to review the current approaches in self-supervised anomaly detection. We present technical details of the common approaches and discuss their strengths and drawbacks. We also compare the performance of these models against each other and other state-of-the-art anomaly detection models. Finally, we discuss a variety of new directions for improving the existing algorithms. 1

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Anomaly Detection on Electroencephalography with Self-supervised Learning

Cited by 15 publications

References 13 publications

Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography

Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography

Pre-training in Medical Data: A Survey

Self-Supervised Anomaly Detection: A Survey and Outlook

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