Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM

Rasheed, Waqas; Tang, Tong Boon

doi:10.1109/tnsre.2019.2948798

Cited by 21 publications

(12 citation statements)

References 43 publications

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“…However, it has a poor ability to deal with the uncertainty of iEEG signals. The SVM is also a soft algorithm and deals well with the uncertainty of iEEG signals because it is based on the structural risk minimization principle [28].…”

Section: Introductionmentioning

confidence: 99%

HFO Detection in Epilepsy: A Stacked Denoising Autoencoder and Sample Weight Adjusting Factors-Based Method

Wu¹,

Qin

Wan

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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High-frequency oscillations (HFOs) recorded by the intracranial electroencephalography (iEEG) are the promising biomarkers of epileptogenic zones. Accurate detection of HFOs is the key to pre-operative assessment for epilepsy. Due to the subjective bias caused by manual features and the class imbalance between HFOs and false HFOs, it is difficult to obtain satisfactory detection performance by the existing methods. To solve these problems, we put forward a novel method to accurately detect HFOs based on the stacked denoising autoencoder (SDAE) and the ensemble classifier with sample weight adjusting factors. First, the adjustable threshold of Hilbert envelopes is proposed to isolate the events of interest (EoIs) from background activities. Then, the SDAE network is utilized to automatically extract features of EoIs in the time-frequency domain. Finally, the AdaBoost-based support vector machine ensemble classifier with sample weight adjusting factors is devised to separate HFOs from EoIs by using the extracted features. These adjusting factors are used to solve the class imbalance problem by adjusting sample weights when learning the base classifiers. Our HFO detection method is evaluated by using clinical iEEG data recorded from 20 patients with medically refractory epilepsy. The experimental results show that our detection method outperforms some existing methods in terms of sensitivity and false discovery rate. In addition, the HFOs detected by our method are effective for localizing seizure onset zones.

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Section: Introductionmentioning

confidence: 99%

HFO Detection in Epilepsy: A Stacked Denoising Autoencoder and Sample Weight Adjusting Factors-Based Method

Wu¹,

Qin

Wan

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…For the supersphere model, the training set is constructed from θ ∈ U[0, 2π ], ρ ∈ U [6,10], and 1000 samples are random generated, so the coordinates of θ and ρ are dimensionless. The RBF kernel is chosen and the pre-selected SVs are shown in Fig.…”

Section: A Simulation Experiments and Analysis Of Resultsmentioning

confidence: 99%

“…2) Pre-select the support vectors from D, and build the OCSVM model as the fault prediction model based on the selected sample set. For the supersphere model, calculate the variable's value range representing the relative location relationship between the samples in D and the supersphere according formula (10). Then the lower bounds of the two ranges are regarded as the respective threshold reference to judge whether the fault trend exist or not.…”

Section: Fault Prediction and Quantitative Anomaly Measurement Using Sv Pre-selection Ocsvmmentioning

confidence: 99%

“…Inertial system platforms are a kind of important precision devices used in many industrial fields, such as navigation, guidance and aerospace, which have the characteristics of difficult acquisition for state data and small sample scale, so the fault trend of the devices should be discovered as early as possible based on the limited data samples to prevent the occurrence of serious damages to the whole system. The fault state of system is actually an abnormal phenomena deviating from the normal state, so the early warning of fault may be realized by novelty recognition before the fault completely occurs [7][8][9][10]. The concrete method based on this idea is as follows: build the system's normal working state space directly using the system's normal features, and recognize the novel samples by detecting whether the deviation from the normal features is existed.…”

Section: Introductionmentioning

confidence: 99%

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A Fast Small-Sample Modeling Method for Precision Inertial Systems Fault Prediction and Quantitative Anomaly Measurement

Wang¹,

Cai²

2022

Computer Modeling in Engineering &Amp; Sciences

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Inertial system platforms are a kind of important precision devices, which have the characteristics of difficult acquisition for state data and small sample scale. Focusing on the model optimization for data-driven fault state prediction and quantitative degree measurement, a fast small-sample supersphere one-class SVM modeling method using support vectors pre-selection is systematically studied in this paper. By theorem-proving the irrelevance between the model's learning result and the non-support vectors (NSVs), the distribution characters of the support vectors are analyzed. On this basis, a modeling method with selected samples having specific geometry character from the training sets is also proposed. The method can remarkably eliminate the NSVs and improve the algorithm's efficiency. The experimental results testify that the scale of training samples and the modeling time consumption both give a sharply decrease using the support vectors pre-selection method. The experimental results on inertial devices also show good fault prediction capability and effectiveness of quantitative anomaly measurement.

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“…The non-conforming and unexpected patterns are called outliers or anomalies. Anomaly detection has been extensively used in a wide variety of applications such as cyber-intrusion detection [31], fraud detection [32], medical anomaly detection [33,34], industrial damage detection [35], hyperspectral image analysis [36], sensor networks [37], image processing [38], to cite just a few. Outlier detection is very popular in industrial applications [39][40][41][42][43] since it is critical to the efficient and secure operation of industrial equipment, integrated sensors, and the overall production process.…”

Section: Related Workmentioning

confidence: 99%

Online Learning of Oil Leak Anomalies in Wind Turbines with Block-Based Binary Reservoir

et al. 2021

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The focus of this work is to design a deeply quantized anomaly detector of oil leaks that may happen at the junction between the wind turbine high-speed shaft and the external bracket of the power generator. We propose a block-based binary shallow echo state network (BBS-ESN) architecture belonging to the reservoir computing (RC) category and, as we believe, it also extends the extreme learning machines (ELM) domain. Furthermore, BBS-ESN performs binary block-based online training using fixed and minimal computational complexity to achieve low power consumption and deployability on an off-the-shelf micro-controller (MCU). This has been achieved through binarization of the images and 1-bit quantization of the network weights and activations. 3D rendering has been used to generate a novel publicly available dataset of photo-realistic images similar to those potentially acquired by image sensors on the field while monitoring the junction, without and with oil leaks. Extensive experimentation has been conducted using a STM32H743ZI2 MCU running at 480 MHz and the results achieved show an accurate identification of anomalies, with a reduced computational cost per image and memory occupancy. Based on the obtained results, we conclude that BBS-ESN is feasible on off-the-shelf 32 bit MCUs. Moreover, the solution is also scalable in the number of image cameras to be deployed and to achieve accurate and fast oil leak detections from different viewpoints.

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Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM

Cited by 21 publications

References 43 publications

HFO Detection in Epilepsy: A Stacked Denoising Autoencoder and Sample Weight Adjusting Factors-Based Method

HFO Detection in Epilepsy: A Stacked Denoising Autoencoder and Sample Weight Adjusting Factors-Based Method

A Fast Small-Sample Modeling Method for Precision Inertial Systems Fault Prediction and Quantitative Anomaly Measurement

Online Learning of Oil Leak Anomalies in Wind Turbines with Block-Based Binary Reservoir

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