Aslib Journal of Information Management

Machine learning and deep learning-based advanced classification techniques for the detection of major depressive disorder

Abhinandan Chatterjee 1
Pradip Kumar Bala 1
Shruti Gedam 2
Sanchita Paul 2
Nishant Goyal 3
Publication typeJournal Article
Publication date2023-07-11
scimago Q1
SJR0.625
CiteScore5.3
Impact factor2.4
ISSN20503806, 20503814
Library and Information Sciences
Information Systems
Abstract
Purpose

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

Design/methodology/approach

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

Findings

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

Originality/value

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

Moussa M.M., Alzaabi Y., Khandoker A.H.
IEEE Access scimago Q1 wos Q2 Open Access
2022-11-03 citations by CoLab: 12 Abstract  
Obstructive Sleep Apnea Syndrome (OSAS) and Major Depressive Disorder (MDD) are common conditions associated with poor quality of life. In this work, we aim to classify OSAS and depression in patients with OSAS using machine learning techniques. We have extracted features from electrocardiograms (ECG), electroencephalograms (EEG), and breathing signals from polysomnography (PSG) at specific 5-minute intervals, where the participants’ statuses are known, meaning we do not need breathing signals. These statuses include sleep stage, whether or not they have depression, or an apneic event has occurred. The PSGs were recorded from a total of 118 subjects with a 75/25 split for training and testing and the resultant features were used in sleep staging and classifying OSAS and depression in OSAS patients. Sleep staging was best done with random forest without feature selection, yielding an accuracy of 70.52 % and F1-Score of 69.99 %. The best classification performance of OSAS happened during deep sleep without feature selection and SVM, which yielded an accuracy of 98.36 % and F1-Score of 98.82 %. All sleep stages with Chi2 ANN yielded an accuracy of 72.95 % and F1-Score of 73.43 % for classification of depression in OSAS patients. Results show promise in detecting OSAS and depression in OSAS patients, and the Bland-Altman plot shows that posterior probability provides comparable means of detecting OSAS to the apnea-hypopnea index (AHI). Besides detection of OSAS in depressed patients, this work serves to classify depression and give insights into relevant sleep stages to both of those conditions, allowing better planning for polysomnography.
Aleem S., Huda N.U., Amin R., Khalid S., Alshamrani S.S., Alshehri A.
Electronics (Switzerland) scimago Q2 wos Q2 Open Access
2022-03-31 citations by CoLab: 69 PDF Abstract  
Over the years, stress, anxiety, and modern-day fast-paced lifestyles have had immense psychological effects on people’s minds worldwide. The global technological development in healthcare digitizes the scopious data, enabling the map of the various forms of human biology more accurately than traditional measuring techniques. Machine learning (ML) has been accredited as an efficient approach for analyzing the massive amount of data in the healthcare domain. ML methodologies are being utilized in mental health to predict the probabilities of mental disorders and, therefore, execute potential treatment outcomes. This review paper enlists different machine learning algorithms used to detect and diagnose depression. The ML-based depression detection algorithms are categorized into three classes, classification, deep learning, and ensemble. A general model for depression diagnosis involving data extraction, pre-processing, training ML classifier, detection classification, and performance evaluation is presented. Moreover, it presents an overview to identify the objectives and limitations of different research studies presented in the domain of depression detection. Furthermore, it discussed future research possibilities in the field of depression diagnosis.
Zhan C., Zheng Y., Zhang H., Wen Q.
IEEE Internet of Things Journal scimago Q1 wos Q1
2021-11-01 citations by CoLab: 54 Abstract  
The rapid geographic spread of COVID-19, to which various factors may have contributed, has caused a global health crisis. Recently, the analysis and forecast of the COVID-19 pandemic have attracted worldwide attention. In this work, a large COVID-19 data set consisting of COVID-19 pandemic, COVID-19 testing capacity, economic level, demographic information, and geographic location data in 184 countries and 1241 areas from December 18, 2019, to September 30, 2020, were developed from public reports released by national health authorities and bureau of statistics. We proposed a machine learning model for COVID-19 prediction based on the broad learning system (BLS). Here, we leveraged random forest (RF) to screen out the key features. Then, we combine the bagging strategy and BLS to develop a random-forest-bagging BLS (RF-Bagging-BLS) approach to forecast the trend of the COVID-19 pandemic. In addition, we compared the forecasting results with linear regression (LR) model, $K$ -nearest neighbors (KNN), decision tree (DT), adaptive boosting (Ada), RF, gradient boosting DT (GBDT), support vector regression (SVR), extra trees (ETs) regressor, CatBoost (CAT), LightGBM (LGB), XGBoost (XGB), and BLS.The RF-Bagging BLS model showed better forecasting performance in terms of relative mean-square error (RMSE), coefficient of determination ( $R^{2}$ ), adjusted coefficient of determination ( $R_{\mathrm{ adj}}^{2}$ ), median absolute error (MAD), and mean absolute percentage error (MAPE) than other models. Hence, the proposed model demonstrates superior predictive power over other benchmark models.
Izci E., Ozdemir M.A., Akan A., Ozcoban M.A., Arikan M.K.
2021-06-09 citations by CoLab: 1 Abstract  
Major depressive disorder (MDD) is a common mood disorder encountered worldwide. Early diagnosis has great importance to prevent the negative effects on the person. The aim of this study is to develop an objective method to differentiate MDD patients from healthy controls. Electroencephalography (EEG) signals taken from 16 MDD patients and 16 healthy subjects are analyzed according to the regions of the brain, and time-domain, frequency-domain, and nonlinear features were extracted. The feature sets are classified using five different classification algorithms. As a result of the study, a classification accuracy of 89.5% was yielded using the Bagging classifier with 7 features calculated from the central EEG channels.
Sharma G., Parashar A., Joshi A.M.
2021-04-01 citations by CoLab: 126 Abstract  
• For depression detection EEG signal proves as best biomarker. • CNN-LSTM hybrid neural networks present high performance. • Large EEG dataset processing is better with deep neural network. • Windowing technique shows less computation and time complexity. Depression is a psychological disorder characterized by the continuous occurrence of bad mood state. It is critical to understand that this disorder is severely affecting people of multiple age groups across the world. This illness is now considered as a global issue and its early diagnosis will be effective in saving the lives of many people. This mental disorder can be diagnosed with the help of Electroencephalogram (EEG) signals as an analysis of these signals can indicate the prevailing mental state of the patients. This paper elaborates on the advantages of a fully automated Depression Detection System, as manual analysis of the EEG signal is very time consuming, tedious and it requires a lot of experience. This research paper presents a novel EEG based computer-aided (CAD) Hybrid Neural Network that can be identified as DepHNN (Depression Hybrid Neural Network) for depression screening. The proposed method uses Convolutional Neural Network (CNN) for temporal learning, windowing and long-short term memory (LSTM) architectures for the sequence learning process. In this model, EEG signals have been obtained from 21 drug-free, symptomatic depressed, and 24 normal patients using neuroscan. The model has less time and minimized computation complexity as it uses the windowing technique. It has attained an accuracy of 99.10% with mean absolute error (MAE) of 0.2040. The results show that the developed hybrid CNN-LSTM model is accurate, less complex, and useful in detecting depression using EEG signals.
Hosseini M., Hosseini A., Ahi K.
2021-01-01 citations by CoLab: 258 Abstract  
Electroencephalography (EEG) has been a staple method for identifying certain health conditions in patients since its discovery. Due to the many different types of classifiers available to use, the analysis methods are also equally numerous. In this review, we will be examining specifically machine learning methods that have been developed for EEG analysis with bioengineering applications. We reviewed literature from 1988 to 2018 to capture previous and current classification methods for EEG in multiple applications. From this information, we are able to determine the overall effectiveness of each machine learning method as well as the key characteristics. We have found that all the primary methods used in machine learning have been applied in some form in EEG classification. This ranges from Naive-Bayes to Decision Tree/Random Forest, to Support Vector Machine (SVM). Supervised learning methods are on average of higher accuracy than their unsupervised counterparts. This includes SVM and KNN. While each of the methods individually is limited in their accuracy in their respective applications, there is hope that the combination of methods when implemented properly has a higher overall classification accuracy. This paper provides a comprehensive overview of Machine Learning applications used in EEG analysis. It also gives an overview of each of the methods and general applications that each is best suited to.
Espinola C.W., Gomes J.C., Pereira J.M., dos Santos W.P.
2020-10-12 citations by CoLab: 29 Abstract  
Diagnosis and treatment in psychiatry are still highly dependent on reports from patients and on clinician judgment. This fact makes them prone to memory and subjectivity biases. As for other medical fields, where objective biomarkers are available, there has been an increasing interest in the development of such tools in psychiatry. To this end, vocal acoustic parameters have been recently studied as possible objective biomarkers, instead of otherwise invasive and costly methods. Patients suffering from different mental disorders, such as major depressive disorder (MDD), may present with alterations of speech. These can be described as uninteresting, monotonous, and spiritless speech and low voice. Thirty-three individuals (11 males) over 18 years old were selected, 22 of which being previously diagnosed with MDD and 11 healthy controls. Their speech was recorded in naturalistic settings, during a routine medical evaluation for psychiatric patients, and in different environments for healthy controls. Voices from third parties were removed. The recordings were submitted to a vocal feature extraction algorithm, and to different machine learning classification techniques. The results showed that random tree models with 100 trees provided the greatest classification performances. It achieved mean accuracy of 87.5575% ± 1.9490, mean kappa index, sensitivity, and specificity of 0.7508 ± 0.0319, 0.9149 ± 0.0204, and 0.8354 ± 0.0254, respectively, for the detection of MDD. The use of machine learning classifiers with vocal acoustic features appears to be very promising for the detection of major depressive disorder in this exploratory study, but further experiments with a larger sample will be necessary to validate our findings.
Yoon H., Klasky H.B., Gounley J.P., Alawad M., Gao S., Durbin E.B., Wu X., Stroup A., Doherty J., Coyle L., Penberthy L., Blair Christian J., Tourassi G.D.
2020-10-01 citations by CoLab: 13 Abstract  
In machine learning, it is evident that the classification of the task performance increases if bootstrap aggregation (bagging) is applied. However, the bagging of deep neural networks takes tremendous amounts of computational resources and training time. The research question that we aimed to answer in this research is whether we could achieve higher task performance scores and accelerate the training by dividing a problem into sub-problems. : The data used in this study consist of free text from electronic cancer pathology reports. We applied bagging and partitioned data training using Multi-Task Convolutional Neural Network (MT-CNN) and Multi-Task Hierarchical Convolutional Attention Network (MT-HCAN) classifiers. We split a big problem into 20 sub-problems, resampled the training cases 2,000 times, and trained the deep learning model for each bootstrap sample and each sub-problem—thus, generating up to 40,000 models. We performed the training of many models concurrently in a high-performance computing environment at Oak Ridge National Laboratory (ORNL). We demonstrated that aggregation of the models improves task performance compared with the single-model approach, which is consistent with other research studies; and we demonstrated that the two proposed partitioned bagging methods achieved higher classification accuracy scores on four tasks. Notably, the improvements were significant for the extraction of cancer histology data, which had more than 500 class labels in the task; these results show that data partition may alleviate the complexity of the task. On the contrary, the methods did not achieve superior scores for the tasks of site and subsite classification. Intrinsically, since data partitioning was based on the primary cancer site, the accuracy depended on the determination of the partitions, which needs further investigation and improvement. Results in this research demonstrate that 1. The data partitioning and bagging strategy achieved higher performance scores. 2. We achieved faster training leveraged by the high-performance Summit supercomputer at ORNL. • We demonstrated that bagging is an effective way of boosting information extraction performance. • We designed, developed and evaluated two data partitioning approaches. • The proposed approaches alleviate the complexity of classification tasks. • Our results demonstrated significant performance boost in macro-F1 scores. • We performed training deep learning models in parallel on Summit supercomputer.
Saeedi A., Saeedi M., Maghsoudi A., Shalbaf A.
Cognitive Neurodynamics scimago Q2 wos Q2
2020-07-26 citations by CoLab: 110 Abstract  
Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully understood. So, early discovery of MDD patients helps to cure or limit the adverse effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as MDD due to having high temporal resolution information, and being a noninvasive, inexpensive and portable method. This paper has proposed an EEG-based deep learning framework that automatically discriminates MDD patients from healthy controls. First, the relationships among EEG channels in the form of effective brain connectivity analysis are extracted by Generalized Partial Directed Coherence (GPDC) and Direct directed transfer function (dDTF) methods. A novel combination of sixteen connectivity methods (GPDC and dDTF in eight frequency bands) was used to construct an image for each individual. Finally, the constructed images of EEG signals are applied to the five different deep learning architectures. The first and second algorithms were based on one and two-dimensional convolutional neural network (1DCNN–2DCNN). The third method is based on long short-term memory (LSTM) model, while the fourth and fifth algorithms utilized a combination of CNN with LSTM model namely, 1DCNN-LSTM and 2DCNN-LSTM. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals. The efficiency of the proposed algorithms is evaluated on resting state EEG data obtained from 30 healthy subjects and 34 MDD patients. The experiments show that the 1DCNN-LSTM applied on constructed image of effective connectivity achieves best results with accuracy of 99.24% due to specific architecture which captures the presence of spatial and temporal relations in the brain connectivity. The proposed method as a diagnostic tool is able to help clinicians for diagnosing the MDD patients for early diagnosis and treatment.
Mahato S., Goyal N., Ram D., Paul S.
Journal of Medical Systems scimago Q1 wos Q2
2020-05-21 citations by CoLab: 57 Abstract  
Depression is a psychiatric problem which affects the growth of a person, like how a person thinks, feels and behaves. The major reason behind wrong diagnosis of depression is absence of any laboratory test for detection as well as severity scaling of depression. Any degradation in the working of the brain can be identified through change in the electroencephalogram (EEG) signal. Thus detection as well as severity scaling of depression is done in this study using EEG signal. In this study, features are extracted from the temporal region of the brain using six (FT7, FT8, T7, T8, TP7, TP8) channels. The linear features used are delta, theta, alpha, beta, gamma1 and gamma2 band power and their corresponding asymmetry as well as paired asymmetry. The non-linear features used are Sample Entropy (SampEn) and Detrended Fluctuation Analysis (DFA). The classifiers used are: Bagging along with three different kernel functions (Polynomial, Gaussian and Sigmoidal) of Support Vector Machine (SVM). Feature selection technique used is ReliefF. Highest classification accuracy of 96.02% and 79.19% was achieved for detection and severity scaling of depression using SVM (Gaussian Kernel Function) and ReliefF as feature selection. From the analysis, it was found that depression affects the temporal region of the brain (temporo-parietal region).It was also found that depression affects the higher frequency band features more and it affects each hemisphere differently. It can also be analysed that out of all the kernel of SVM, Gaussian kernel is more efficient to other kernels. Of all the features, combination of all paired asymmetry and asymmetry showed high classification accuracy (accuracy of 90.26% for detection of depression and accuracy of 75.31% for severity scaling).
Shrivastava D., Sanyal S., Maji A.K., Kandar D.
2020-01-17 citations by CoLab: 41 Abstract  
Bone cancer is one of the life threatening diseases which may cause death to many individuals. There must be an accurate detection and classification system available to diagnose bone cancer at early stage. Early detection of cancer seems to be the important factor in increasing the chance of cancer patient survival. Classification of cancer is one of the most challenging tasks in clinical finding and diagnosis. This work elaborates different machine learning techniques for tumor detection and classification. Machine Learning has a vast area of research, out of which medical image processing is significant area of work. In medical diagnosis like ulcer, fracture, tumor etc image processing made the work easier in finding the exact cause and best possible solution. In this work, bone Computed Tomography (CT) dataset in digital Imaging and communication in medicine (DICOM) format are used. Machine learning techniques applied on medical images for abnormality detection. It is observed that satisfactory level of improvement has been achieved by applying Machine Learning techniques. In this work different machine learning techniques for classification are elaborated.
Pisner D.A., Schnyer D.M.
2020-01-01 citations by CoLab: 516 Abstract  
In this chapter, we explore Support Vector Machine (SVM)—a machine learning method that has become exceedingly popular for neuroimaging analysis in recent years. Because of their relative simplicity and flexibility for addressing a range of classification problems, SVMs distinctively afford balanced predictive performance, even in studies where sample sizes may be limited. In brain disorders research, SVMs are typically employed using multivoxel pattern analysis (MVPA) because their relative simplicity carries a lower risk of overfitting even using high-dimensional imaging data. More recently, SVMs have been used in the context of precision psychiatry, particularly for applications that involve predicting diagnosis and prognosis of brain diseases such as Alzheimer's disease, schizophrenia, and depression. In the last section of this chapter, we review a number of recent studies that use SVM for such applications.
Mahato S., Paul S.
Journal of Medical Systems scimago Q1 wos Q2
2019-12-13 citations by CoLab: 100 Abstract  
Depression or Major Depressive Disorder (MDD) is a mental illness which negatively affects how a person thinks, acts or feels. MDD has become a major disease affecting millions of people presently. The diagnosis of depression is questionnaire based and is not based on any objective criteria. In this paper, feature extracted from EEG signal are used for the diagnosis of depression. Alpha, alpha1, alpha2, beta, delta and theta power and theta asymmetry was used as feature. Alpha1, alpha2 along with theta asymmetry was also used as a feature. Multi-Cluster Feature Selection (MCFS) was used for feature selection when feature combination was used. The classifiers used were Support Vector Machine (SVM), Logistic Regression (LR), Naïve-Bayesian (NB) and Decision Tree (DT). Alpha2 showed higher classification accuracy than alpha1 and alpha power in all applied classifier. From t-test it was found that there was a significant difference in the theta power of left and right hemisphere of normal subjects, but there was no significant difference in depression patients. Average theta asymmetry in normal subjects is higher than MDD patients but the difference in theta asymmetry in normal subjects and MDD patients is not significant. The combination of alpha2 and theta asymmetry showed the highest classification accuracy of 88.33% in SVM.
Dhieb N., Ghazzai H., Besbes H., Massoud Y.
2019-09-01 citations by CoLab: 54 Abstract  
Vehicle safety is one of the most important research topics not only for vehicular industry but also for auto insurance companies. It is of great significance for them to avoid compensating damaged vehicles and dealing with an incredible number of correct and fraudulent claims. In fact, fraudulent claims present a huge and a costly problem for insurance companies and end up with big losses reaching over billions of Dollars yearly. These frauds also have social-economical consequences as their costs are defrayed by the policy holder through the increase of their premiums to cover the insurer loss. Several insurance companies are exploring innovative solutions not only to improve customers safety and driving experience but also to streamline fraud detection methods since traditional ones are complex, time-consuming, and usually lead to inaccurate results. In this paper, we develop an automated fraud detection approach for auto insurance companies based on extreme gradient boosting algorithm, aka XGBoost. The objective is to automatically detect fraudulent claims and classify them into different fraud types. To this end, data analysis techniques are used to clean, explore, and extract relevant features. The proposed framework aims to minimize human intervention, deliver alerts for risky claims, and reduce monetary losses in the auto insurance industry. The obtained results reveal a high performance gain achieved by XGBoost in detecting and classifying fraudulent claims compared to other machine learning algorithms.
Kamel H., Abdulah D., Al-Tuwaijari J.M.
2019-06-01 citations by CoLab: 82 Abstract  
Cancer is one of the most deadly diseases in the world and it is a subject of concern because until till date they cannot found the real treatment for this disease. If and only if this disease is detected in early stage, patients having this disease can be saved. If it is detected in latter stage then chance of survival will be very less. Because of this, early and true diagnosis is an important issue and plays a key role to cure this disease. In the work, Gaussian Naive Bayes algorithm is used for classification cancer. The algorithm is tested by applying it on two datasets in which the first is Wisconsin Breast Cancer dataset (WBCD) and the second is lung cancer dataset. The evaluation results of the proposed algorithm have achieved 98% accuracy of predicting breast cancer and 90% of predicting lung cancer.
Yuan D., Li Y.
2024-06-21 citations by CoLab: 0 Abstract  
PurposeWhen emergencies occur, the attention of the public towards emergency information on social media in a specific time period forms the emergency information popularity evolution patterns. The purpose of this study is to discover the popularity evolution patterns of social media emergency information and make early predictions.Design/methodology/approachWe collected the data related to the COVID-19 epidemic on the Sina Weibo platform and applied the K-Shape clustering algorithm to identify five distinct patterns of emergency information popularity evolution patterns. These patterns include strong twin peaks, weak twin peaks, short-lived single peak, slow-to-warm-up single peak and slow-to-decay single peak. Oriented toward early monitoring and warning, we developed a comprehensive characteristic system that incorporates publisher features, information features and early features. In the early features, data measurements are taken within a 1-h time window after the release of emergency information. Considering real-time response and analysis speed, we employed classical machine learning methods to predict the relevant patterns. Multiple classification models were trained and evaluated for this purpose.FindingsThe combined prediction results of the best prediction model and random forest (RF) demonstrate impressive performance, with precision, recall and F1-score reaching 88%. Moreover, the F1 value for each pattern prediction surpasses 87%. The results of the feature importance analysis show that the early features contribute the most to the pattern prediction, followed by the information features and publisher features. Among them, the release time in the information features exhibits the most substantial contribution to the prediction outcome.Originality/valueThis study reveals the phenomena and special patterns of growth and decline, appearance and disappearance of social media emergency information popularity from the time dimension and identifies the patterns of social media emergency information popularity evolution. Meanwhile, early prediction of related patterns is made to explore the role factors behind them. These findings contribute to the formulation of social media emergency information release strategies, online public opinion guidance and risk monitoring.

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