An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals

Sharma, Manish; Achuth, P V; Deb, Dipankar; Puthankattil, Subha D.; Acharya, U. Rajendra

doi:10.1016/j.cogsys.2018.07.010

Cited by 118 publications

(60 citation statements)

References 64 publications

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“…Hence, directly observing the nonlinear and nonstationary EEG raw data in time domain is a very tedious task [4]. EEG sources localization system consists of identifying the spatial variations of underlying mental processes using the inverse solution techniques.…”

Section: Discussionmentioning

confidence: 99%

Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique

Kaur¹,

Singh²,

Sahni³

2019

Eur Neurol

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Background: Electroencephalography (EEG) may be used as an objective diagnosis tool for diagnosing various disorders. Recently, source localization from EEG is being used in the analysis of real-time brain monitoring applications. However, inverse problem reduces the accuracy in EEG signal processing systems. Objectives: This paper presents a new method of EEG source localization using variational mode decomposition (VMD) and standardized the low resolution brain electromagnetic tomography (sLORETA) inverse model. The focus is to compare the effectiveness of the proposed approach for EEG signals of depression patients. Method: As the first stage, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying VMD. Then, closely related functions are analyzed using the inverse modelling-based source localization procedures such as sLORETA. Simulations have been carried out on real EEG databases for depression to demonstrate the effectiveness of the proposed techniques. Results: The performance of the algorithm has been assessed using localization error (LE), mean square error and signal to noise ratio output corresponding to simulated EEG dipole sources and real EEG signals for depression. In order to study the spatial resolution for cortical potential distribution, the main focus has been on studying the effects of noise sources and estimating LE of inverse solutions. More accurate and robust localization results show that this methodology is very promising for EEG source localization of depression signals. Conclusion: It can be said that proposed algorithm efficiently suppresses the influence of noise in the EEG inverse problem using simulated EEG activity and EEG database for depression. Such a system may offer an effective solution for clinicians as a crucial stage of EEG pre-processing in automated depression detection systems and may prevent delay in diagnosis.

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Section: Discussionmentioning

confidence: 99%

Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique

Kaur¹,

Singh²,

Sahni³

2019

Eur Neurol

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“…Wavelet transform (WT) can be used to meet the above requirement. WT represents the signal in time and frequency domain and can change the frequency resolution and time interval by dilation and shift of wavelet function [31,32].…”

Section: Wavelet Transformmentioning

confidence: 99%

Stacked Auto-Encoder Based CNC Tool Diagnosis Using Discrete Wavelet Transform Feature Extraction

Kim

Lee

Jeon³

et al. 2020

Processes

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Machining processes are critical and widely used components in the manufacturing industry because they help to precisely make products and reduce production time. To keep the previous advantages, a machine tool should be installed at the designated place and condition of the machine tool should be maintained appropriately to working environment. In various maintenance methods for keeping the condition of machine tool, condition-based maintenance can be robust to unpredicted accidents and reduce maintenance costs. Tool monitoring and diagnosis are some of the most important components of the condition based maintenance. This paper proposes stacked auto-encoder based CNC machine tool diagnosis using discrete wavelet transform feature extraction to diagnose a machine tool. The diagnosis model, which only uses cutting force data, cannot sufficiently reflects tool condition. Hence, we modeled diagnosis model using features extracted from a cutting force, a current signal, and coefficients of the discrete wavelet transform. The experimental results showed that the model which uses feature data has better performance than the model that uses only cutting force data. The feature based models are lower false negative rate (FNR) and false positive rate. Moreover, squared prediction error using normalized residual vector also reduced FNR because normalization reduces weight bias.

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“…Similarly, conditions such as unattended post-traumatic stress disorder (PTSD) with depression may induce suicidal behaviours [13,14]. CAD evaluation of EEG signals has been performed using machine learning (ML) to identify EEG patterns for predictions involving PTSD or depression [15][16][17][18]. Furthermore, using CNNs, deep learning has been applied to classify depression-related EEG signals [16].…”

Section: Introductionmentioning

confidence: 99%

“…CAD techniques using ML approaches for stress assessment have been reported [16,[19][20][21]. Most of these systems are either offline or time-consuming methods.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Stress Assessment Using Sliding Window Based Convolutional Neural Network

Naqvi

Ali

Yahya

et al. 2020

Sensors

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Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Conventional stress assessment methods are highly subjective and tedious and tend to lack accuracy. Machine-learning (ML)-based computer-aided diagnosis systems can be used to assess the mental state with reasonable accuracy, but they require offline processing and feature extraction, rendering them unsuitable for real-time applications. This paper presents a real-time mental stress assessment approach based on convolutional neural networks (CNNs). The CNN-based approach afforded real-time mental stress assessment with an accuracy as high as 96%, the sensitivity of 95%, and specificity of 97%. The proposed approach is compared with state-of-the-art ML techniques in terms of accuracy, time utilisation, and quality of features.

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An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals

Cited by 118 publications

References 64 publications

Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique

Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique

Stacked Auto-Encoder Based CNC Tool Diagnosis Using Discrete Wavelet Transform Feature Extraction

Real-Time Stress Assessment Using Sliding Window Based Convolutional Neural Network

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