Ensemble approach for detection of depression using EEG features

• URL: http://arxiv.org/abs/2103.08467v1
• Date: Sun, 7 Mar 2021 19:35:03 GMT
• Title: Ensemble approach for detection of depression using EEG features
• Authors: Egils Avots, Kla\=vs Jermakovs, Maie Bachmann, Laura Paeske, Cagri Ozcinar, Gholamreza Anbarjafari
• Abstract summary: Depression is a public health issue which severely affects one's well being and cause negative social and economic effect for society. This publication aims to determine if long lasting effects of depression can be determined from electoencephalographic (EEG) signals.
• Score: 9.818924322393752
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Large-scale digital phenotyping: identifying depression and anxiety indicators in a general UK population with over 10,000 participants [2.2909783327197393]
  We conducted a cross-sectional analysis of data from 10,129 participants recruited from a UK-based general population. Participants shared wearable (Fitbit) data and self-reported questionnaires on depression (PHQ-8), anxiety (GAD-7), and mood via a study app. We observed significant associations between the severity of depression and anxiety with several factors, including mood, age, gender, BMI, sleep patterns, physical activity, and heart rate.
  arXiv  Detail & Related papers  (2024-09-24T16:05:17Z)
• Refining ADHD diagnosis with EEG: The impact of preprocessing and temporal segmentation on classification accuracy [41.94295877935867]
  This study highlights the importance of preprocessing and segmentation in improving the reliability of ADHD diagnosis through EEG. Models trained on later EEG segments achieved significantly higher accuracy, indicating the potential role of cognitive fatigue in distinguishing ADHD.
  arXiv  Detail & Related papers  (2024-07-11T09:07:22Z)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• Assessing ML Classification Algorithms and NLP Techniques for Depression Detection: An Experimental Case Study [0.6524460254566905]
  Depression has affected millions of people worldwide and has become one of the most common mental disorders. Recent research has evidenced that machine learning (ML) and Natural Language Processing (NLP) tools and techniques have significantly been used to diagnose depression. However, there are still several challenges in the assessment of depression detection approaches in which other conditions such as post-traumatic stress disorder (PTSD) are present.
  arXiv  Detail & Related papers  (2024-04-03T19:45:40Z)
• Mental Health Diagnosis in the Digital Age: Harnessing Sentiment Analysis on Social Media Platforms upon Ultra-Sparse Feature Content [3.6195994708545016]
  We propose a novel semantic feature preprocessing technique with a three-folded structure. With enhanced semantic features, we train a machine learning model to predict and classify mental disorders. Our methods, when compared to seven benchmark models, demonstrate significant performance improvements.
  arXiv  Detail & Related papers  (2023-11-09T00:15:06Z)
• The Relationship Between Speech Features Changes When You Get Depressed: Feature Correlations for Improving Speed and Performance of Depression Detection [69.88072583383085]
  This work shows that depression changes the correlation between features extracted from speech. Using such an insight can improve the training speed and performance of depression detectors based on SVMs and LSTMs.
  arXiv  Detail & Related papers  (2023-07-06T09:54:35Z)
• Handwriting and Drawing for Depression Detection: A Preliminary Study [53.11777541341063]
  Short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the online handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients.
  arXiv  Detail & Related papers  (2023-02-05T22:33:49Z)
• Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data [65.28160163774274]
  We apply a Bayesian framework to capture the relationships between depression, depression symptoms, and features derived from speech, facial expression and cognitive game data collected at thymia.
  arXiv  Detail & Related papers  (2022-11-09T14:48:13Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• Deep Multi-task Learning for Depression Detection and Prediction in Longitudinal Data [50.02223091927777]
  Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. We introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks.
  arXiv  Detail & Related papers  (2020-12-05T05:14:14Z)
• Generalized Dilated CNN Models for Depression Detection Using Inverted Vocal Tract Variables [4.050982413149992]
  Depression detection using vocal biomarkers is a highly researched area. Findings of existing studies are mostly validated on a single database which limits the generalizability of results. We propose to develop a generalized classifier for depression detection using a dilated Coniculaal Neural Network.
  arXiv  Detail & Related papers  (2020-11-13T03:12:36Z)
• A study of resting-state EEG biomarkers for depression recognition [11.202182020497402]
  Depression has become a major health burden worldwide, and effective detection depression is a great public-health challenge. This Electroencephalography (EEG)-based research is to explore the effective biomarkers for depression recognition.
  arXiv  Detail & Related papers  (2020-02-23T08:33:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.