Data mining EEG signals in depression for their diagnostic value

Mohammadi, Mahdi; Al-Azab, Fadwa Gamal Mohammed; Raahemi, Bijan; Richards, Gregory; Jaworska, Natalia; Smith, Dylan M.; Blier, Pierre; Knott, Verner

doi:10.1186/s12911-015-0227-6

Cited by 88 publications

(34 citation statements)

References 48 publications

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“…Leardi R et al first proposed that the genetic algorithm can be a valuable tool for solving feature selection problems [33]. Mahdi Mohammadi et al used a genetic algorithm to identify the most significant features of EEG signals and find their diagnostic value for depression [34]. Dino et al combined a genetic algorithm with gene expression data to classify gene expression data in two steps [35].…”

Section: Feature Selection Methodsmentioning

confidence: 99%

An evaluation of time series summary statistics as features for clinical prediction tasks

Guo

Chen

2020

BMC Med Inform Decis Mak

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Background: Clinical prediction tasks such as patient mortality, length of hospital stay, and disease diagnosis are highly important in critical care research. The existing studies for clinical prediction mainly used simple summary statistics to summarize information from physiological time series. However, this lack of statistics leads to a lack of information. In addition, using only maximum and minimum statistics to indicate patient features fails to provide an adequate explanation. Few studies have evaluated which summary statistics best represent physiological time series. Methods: In this paper, we summarize 14 statistics describing the characteristics of physiological time series, including the central tendency, dispersion tendency, and distribution shape. Then, we evaluate the use of summary statistics of physiological time series as features for three clinical prediction tasks. To find the combinations of statistics that yield the best performances under different tasks, we use a cross-validation-based genetic algorithm to approximate the optimal statistical combination. Results: By experiments using the EHRs of 6,927 patients, we obtained prediction results based on both single statistics and commonly used combinations of statistics under three clinical prediction tasks. Based on the results of an embedded cross-validation genetic algorithm, we obtained 25 optimal sets of statistical combinations and then tested their prediction results. By comparing the performances of prediction with single statistics and commonly used combinations of statistics with quantitative analyses of the optimal statistical combinations, we found that some statistics play central roles in patient representation and different prediction tasks have certain commonalities. Conclusion: Through an in-depth analysis of the results, we found many practical reference points that can provide guidance for subsequent related research. Statistics that indicate dispersion tendency, such as min, max, and range, are more suitable for length of stay prediction tasks, and they also provide information for short-term mortality prediction. Mean and quantiles that reflect the central tendency of physiological time series are more suitable for mortality and disease prediction. Skewness and kurtosis perform poorly when used separately for prediction but can be used as supplementary statistics to improve the overall prediction effect.

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Section: Feature Selection Methodsmentioning

confidence: 99%

An evaluation of time series summary statistics as features for clinical prediction tasks

Guo

Chen

2020

BMC Med Inform Decis Mak

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“…Further, ML approaches may be more conducive to extracting response prediction data at the individual-level (after we are sufficiently confident that we input appropriate information). While there have been several ML-based studies using EEG data to separate individuals with and without MDD (37), including work from our own group (38), there have only been a handful of studies utilizing ML-based approaches of EEG data for response prediction (see Supplementary Table 1 for a summary). However, the few that exist have yielded relatively high prediction accuracies of response to SSRI treatment based on pre-treatment EEG features (39), and appear to be more accurate than prediction models based on clinician ratings (40).…”

Section: Introductionmentioning

confidence: 99%

Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data

et al. 2019

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Background: Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or early in the course of treatment that can predict antidepressant efficacy, remains a challenge.Methods: Individuals with MDD participated in a 12-week antidepressant pharmacotherapy trial. Electroencephalographic (EEG) data was collected before and 1 week post-treatment initiation in 51 patients. Response status at week 12 was established with the Montgomery-Asberg Depression Scale (MADRS), with a ≥50% decrease characterizing responders (N = 27/24 responders/non-responders). We used a machine learning (ML)-approach for predicting response status. We focused on Random Forests, though other ML methods were compared. First, we used a tree-based estimator to select a relatively small number of significant features from: (a) demographic/clinical data (age, sex, individual item/total MADRS scores at baseline, week 1, change scores); (b) scalp-level EEG power; (c) source-localized current density (via exact low-resolution electromagnetic tomography [eLORETA] software). Second, we applied kernel principal component analysis to reduce and map important features. Third, a set of ML models were constructed to classify response outcome based on mapped features. For each dataset, predictive features were extracted, followed by a model of all predictive features, and finally by a model of the most predictive features.Results: Fifty eLORETA features were predictive of response (across bands, both time-points); alpha1/theta eLORETA features showed the highest predictive value. Eighty-eight scalp EEG features were predictive of response (across bands, both time-points), with theta/alpha2 being most predictive. Clinical/demographic data consisted of 31 features, with the most important being week 1 “concentration difficulty” scores. When all features were included into one model, its predictive utility was high (88% accuracy). When the most important features were extracted in the final model, 12 predictive features emerged (78% accuracy), including baseline scalp-EEG frontopolar theta, parietal alpha2 and frontopolar alpha1.Conclusions: These findings suggest that ML models of pre- and early treatment-emergent EEG profiles and clinical features can serve as tools for predicting antidepressant response. While this must be replicated using large independent samples, it lays the groundwork for research on personalized, “biomarker”-based treatment approaches.

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“…Nevertheless, AD and MCI subjects are characterized by a huge variability and thus discriminating artifacts and patterns similarities to physiological brain activity still remain a crucial issue. In this regard, EEG signal processing integrated with computational algorithms based on machine learning methods may contribute to a deeper comprehension of the disease and simplify the work of neurologists providing an additional tool to diagnose the stage of dementia [ 20 , 30 – 33 ].…”

Section: Introductionmentioning

confidence: 99%

Combining EEG signal processing with supervised methods for Alzheimer’s patients classification

Fiscon

Weitschek

Cialini

et al. 2018

BMC Med Inform Decis Mak

120

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BackgroundAlzheimer’s Disease (AD) is a neurodegenaritive disorder characterized by a progressive dementia, for which actually no cure is known. An early detection of patients affected by AD can be obtained by analyzing their electroencephalography (EEG) signals, which show a reduction of the complexity, a perturbation of the synchrony, and a slowing down of the rhythms.MethodsIn this work, we apply a procedure that exploits feature extraction and classification techniques to EEG signals, whose aim is to distinguish patient affected by AD from the ones affected by Mild Cognitive Impairment (MCI) and healthy control (HC) samples. Specifically, we perform a time-frequency analysis by applying both the Fourier and Wavelet Transforms on 109 samples belonging to AD, MCI, and HC classes. The classification procedure is designed with the following steps: (i) preprocessing of EEG signals; (ii) feature extraction by means of the Discrete Fourier and Wavelet Transforms; and (iii) classification with tree-based supervised methods.ResultsBy applying our procedure, we are able to extract reliable human-interpretable classification models that allow to automatically assign the patients into their belonging class. In particular, by exploiting a Wavelet feature extraction we achieve 83%, 92%, and 79% of accuracy when dealing with HC vs AD, HC vs MCI, and MCI vs AD classification problems, respectively.ConclusionsFinally, by comparing the classification performances with both feature extraction methods, we find out that Wavelets analysis outperforms Fourier. Hence, we suggest it in combination with supervised methods for automatic patients classification based on their EEG signals for aiding the medical diagnosis of dementia.

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Data mining EEG signals in depression for their diagnostic value

Cited by 88 publications

References 48 publications

An evaluation of time series summary statistics as features for clinical prediction tasks

An evaluation of time series summary statistics as features for clinical prediction tasks

Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data

Combining EEG signal processing with supervised methods for Alzheimer’s patients classification

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