404 research outputs found
Ensemble approach for detection of depression using EEG features
Depression is a public health issue which severely affects one's well being
and cause negative social and economic effect for society. To rise awareness of
these problems, this publication aims to determine if long lasting effects of
depression can be determined from electoencephalographic (EEG) signals. The
article contains accuracy comparison for SVM, LDA, NB, kNN and D3 binary
classifiers which were trained using linear (relative band powers, APV, SASI)
and non-linear (HFD, LZC, DFA) EEG features. The age and gender matched dataset
consisted of 10 healthy subjects and 10 subjects with depression diagnosis at
some point in their lifetime. Several of the proposed feature selection and
classifier combinations reached accuracy of 90% where all models where
evaluated using 10-fold cross validation and averaged over 100 repetitions with
random sample permutations.Comment: 8 pages, 2 figure
EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review
Mental disorders represent critical public health challenges as they are
leading contributors to the global burden of disease and intensely influence
social and financial welfare of individuals. The present comprehensive review
concentrate on the two mental disorders: Major depressive Disorder (MDD) and
Bipolar Disorder (BD) with noteworthy publications during the last ten years.
There is a big need nowadays for phenotypic characterization of psychiatric
disorders with biomarkers. Electroencephalography (EEG) signals could offer a
rich signature for MDD and BD and then they could improve understanding of
pathophysiological mechanisms underling these mental disorders. In this review,
we focus on the literature works adopting neural networks fed by EEG signals.
Among those studies using EEG and neural networks, we have discussed a variety
of EEG based protocols, biomarkers and public datasets for depression and
bipolar disorder detection. We conclude with a discussion and valuable
recommendations that will help to improve the reliability of developed models
and for more accurate and more deterministic computational intelligence based
systems in psychiatry. This review will prove to be a structured and valuable
initial point for the researchers working on depression and bipolar disorders
recognition by using EEG signals.Comment: 29 pages,2 figures and 18 Table
Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder
The present methods of diagnosing depression are entirely dependent on self-report
ratings or clinical interviews. Those traditional methods are subjective, where the individual may
or may not be answering genuinely to questions. In this paper, the data has been collected using
self-report ratings and also using electronic smartwatches. This study aims to develop a weighted
average ensemble machine learning model to predict major depressive disorder (MDD) with superior
accuracy. The data has been pre-processed and the essential features have been selected using a
correlation-based feature selection method. With the selected features, machine learning approaches
such as Logistic Regression, Random Forest, and the proposedWeighted Average Ensemble Model are
applied. Further, for assessing the performance of the proposed model, the Area under the Receiver
Optimization Characteristic Curves has been used. The results demonstrate that the proposed
Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and
the Random Forest approaches
Intelligent Advanced User Interfaces for Monitoring Mental Health Wellbeing
It has become pressing to develop objective and automatic measurements integrated in intelligent diagnostic tools for detecting and monitoring depressive states and enabling an increased precision of diagnoses and clinical decision-makings. The challenge is to exploit behavioral and physiological biomarkers and develop Artificial Intelligent (AI) models able to extract information from a complex combination of signals considered key symptoms. The proposed AI models should be able to help clinicians to rapidly formulate accurate diagnoses and suggest personalized intervention plans ranging from coaching activities (exploiting for example serious games), support networks (via chats, or social networks), and alerts to caregivers, doctors, and care control centers, reducing the considerable burden on national health care institutions in terms of medical, and social costs associated to depression cares
The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods in psychiatry detection applications, specifically depression disorder: A Brief Review
The COVID-19 pandemic has forced many people to limit their social
activities, which has resulted in a rise in mental illnesses, particularly
depression. To diagnose these illnesses with accuracy and speed, and prevent
severe outcomes such as suicide, the use of machine learning has become
increasingly important. Additionally, to provide precise and understandable
diagnoses for better treatment, AI scientists and researchers must develop
interpretable AI-based solutions. This article provides an overview of relevant
articles in the field of machine learning and interpretable AI, which helps to
understand the advantages and disadvantages of using AI in psychiatry disorder
detection applications.Comment: 12 page
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