Major depressive disorder assessment via enhanced k-nearest neighbor method and EEG signals

Saeedi, Maryam; Saeedi, Abdolkarim; Maghsoudi, Arash

doi:10.1007/s13246-020-00897-w

“…Therefore, it is difficult to partition the available EEG dataset of small sample size into a training dataset used for training classifiers and an independent test dataset of sufficient size. Due to this limitation, previous studies have only estimated the efficacy of their method in MDD detection by performing a k-fold CV on the entire EEG dataset, where k = 10 [10][11][12][13][14][15][16]18,19,22,25] or k = number of EEG samples [9,17,23] (i.e., leave-one-out (LOO)). Directly performing a 10-fold CV on the group of participants (i.e., partitioning the entire group into 10 folds, with no EEGs of the same participants appearing in different folds at the same time) is nearly impossible due to participant size limitations.…”

Section: Cross Validation Used In Previous Work: More Detailed Review and Analysismentioning

confidence: 99%

Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset

Wu

¹

,

Huang²,

Huang³

et al. 2021

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Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi’s fractal dimension, and Katz’s fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.

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“…Saeedi M et al [41] aimed to the prediction by a three-step procedure; feature extraction, feature selection, classification. For pre-processing operation, Discrete Wavelet Transform (DWT) is employed to decompose EEG signals into detail and approximate coefficients by which thresholds that matched artifacts omitted.…”

Section: Deep Learning Methods For Depression Detection Using Eeg Signalsmentioning

confidence: 99%

Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review

Safayari¹,

Bolhasani²

2021

Preprint

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Depression is considered by WHO as the main contributor to global disability and it poses dangerous threats to approximately all aspects of human life, in particular public and private health. This mental disorder is usually characterized by considerable changes in feelings, routines, or thoughts. With respect to the fact that early diagnosis of this illness would be of critical importance ineffective treatment, some development has occurred in the purpose of depression detection. EEG signals reflect the working status of the human brain by which are considered the most proper tools for a depression diagnosis. Deep learning algorithms have the capacity of pattern discovery and extracting features from the raw data which is fed into them. Owing to this significant characteristic of deep learning, recently, these methods have intensely utilized in the diverse field of researches, specifically medicine and healthcare. Thereby, in this article, we aimed to review all papers concentrated on using deep learning to detect or predict depressive subjects with the help of EEG signals as input data. Regarding the adopted search method, we finally evaluated 22 articles between 2016 and 2021. This article which is organized according to the systematic literature review (SLR) method, provides complete summaries of all exploited studies and compares the noticeable aspects of them. Moreover, some statistical analysis performs to gain a depth perception of the general ideas of the latest researches in this area. A pattern of a five-step procedure was also established by which almost all reviewed articles fulfilled the goal of depression detection. Finally, open issues and challenges in this way of depression diagnosis or prediction and suggested works as the future directions discussed.

show abstract

“…Saeedi M et al [42] aimed to the prediction by a three-step procedure; feature extraction, feature selection, classification. For pre-processing operation, Discrete Wavelet Transform (DWT) employed to decompose EEG signals into detailed and approximate coefficients by which thresholds that matched artifacts omitted.…”

Section: Deep Learning Methods For Depression Detection Using Eeg Signalsmentioning

confidence: 99%

“…Combined models of convolution neural network structure and LSTM blocks have obtained better results than other ones because these representations enjoyed the advantages of memorization capability of LSTM architecture and built-in feature extracting property of CNN constructions. Referring to reviewed studies, it can conclude that using some external methods and combining them with a deep learning structure will also give a significant accuracy, as this technique has been adopted by [42,51]. As it can perceive from the depiction, most studies have selected convolutional layer(s) to derive features as an end-to-end technique.…”

Section: Aq1: What Deep Learning Algorithms Have Been Used To Detect or Predict Depression?mentioning

confidence: 99%

Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review

Safayari¹,

Bolhasani²

2021

Preprint

View full text Add to dashboard Cite

Depression is considered by WHO as the main contributor to global disability and it poses dangerous threats to approximately all aspects of human life, in particular public and private health. This mental disorder is usually characterized by considerable changes in feelings, routines, or thoughts. With respect to the fact that early diagnosis of this illness would be of critical importance ineffective treatment, some development has occurred in the purpose of depression detection. EEG signals reflect the working status of the human brain by which are considered the most proper tools for a depression diagnosis. Deep learning algorithms have the capacity of pattern discovery and extracting features from the raw data which is fed into them. Owing to this significant characteristic of deep learning, recently, these methods have intensely utilized in the diverse field of researches, specifically medicine and healthcare. Thereby, in this article, we aimed to review all papers concentrated on using deep learning to detect or predict depressive subjects with the help of EEG signals as input data. Regarding the adopted search method, we finally evaluated 22 articles between 2016 and 2021. This article which is organized according to the systematic literature review (SLR) method, provides complete summaries of all exploited studies and compares the noticeable aspects of them. Moreover, some statistical analysis performs to gain a depth perception of the general ideas of the latest researches in this area. A pattern of a five-step procedure was also established by which almost all reviewed articles fulfilled the goal of depression detection. Finally, open issues and challenges in this way of depression diagnosis or prediction and suggested works as the future directions discussed.

show abstract

Major depressive disorder assessment via enhanced k-nearest neighbor method and EEG signals

Cited by 53 publications

References 34 publications

Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset

Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset

Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review

Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review

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