Efficacy of novel attention-based gated recurrent units transformer for depression detection using electroencephalogram signals.

IF 4.7 3区医学 Q1 MEDICAL INFORMATICS

Health Information Science and Systems Pub Date : 2022-12-29 eCollection Date: 2023-12-01 DOI:10.1007/s13755-022-00205-8

Neha Prerna Tigga, Shruti Garg

{"title":"Efficacy of novel attention-based gated recurrent units transformer for depression detection using electroencephalogram signals.","authors":"Neha Prerna Tigga, Shruti Garg","doi":"10.1007/s13755-022-00205-8","DOIUrl":null,"url":null,"abstract":"Purpose: Depression is a global challenge causing psychological and intellectual problems that require efficient diagnosis. Electroencephalogram (EEG) signals represent the functional state of the human brain and can help build an accurate and viable technique for the early prediction and treatment of depression.Methods: An attention-based gated recurrent units transformer (AttGRUT) time-series model is proposed to efficiently identify EEG perturbations in depressive patients. Statistical, spectral and wavelet features were first extracted from the 60-channel EEG signal data. Then, two feature selection techniques, recursive feature elimination and the Boruta algorithm, both with Shapley additive explanations, were utilised for selecting essential features.Results: The proposed model outperformed the two baseline and two hybrid time-series models-long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural network-LSTM (CNN-LSTM), and CNN-GRU-achieving an accuracy of up to 98.67%. Feature selection considerably increased the performance across all time-series models.Conclusion: Based on the obtained results, novel feature selection greatly affected the results of the baseline and hybrid time-series models. The proposed AttGRUT can be implemented and tested in other domains by using different modalities for prediction.Supplementary information: The online version contains supplementary material available at 10.1007/s13755-022-00205-8.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"1"},"PeriodicalIF":4.7000,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800680/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Information Science and Systems","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s13755-022-00205-8","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/12/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}

引用次数: 0

Abstract

Purpose: Depression is a global challenge causing psychological and intellectual problems that require efficient diagnosis. Electroencephalogram (EEG) signals represent the functional state of the human brain and can help build an accurate and viable technique for the early prediction and treatment of depression.

Methods: An attention-based gated recurrent units transformer (AttGRUT) time-series model is proposed to efficiently identify EEG perturbations in depressive patients. Statistical, spectral and wavelet features were first extracted from the 60-channel EEG signal data. Then, two feature selection techniques, recursive feature elimination and the Boruta algorithm, both with Shapley additive explanations, were utilised for selecting essential features.

Results: The proposed model outperformed the two baseline and two hybrid time-series models-long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural network-LSTM (CNN-LSTM), and CNN-GRU-achieving an accuracy of up to 98.67%. Feature selection considerably increased the performance across all time-series models.

Conclusion: Based on the obtained results, novel feature selection greatly affected the results of the baseline and hybrid time-series models. The proposed AttGRUT can be implemented and tested in other domains by using different modalities for prediction.

Supplementary information: The online version contains supplementary material available at 10.1007/s13755-022-00205-8.

查看原文本刊更多论文

新型基于注意力的门控复发单元变换器用于使用脑电图信号检测抑郁症的疗效。

目的：抑郁症是一个全球性的挑战，导致心理和智力问题，需要有效的诊断。脑电图（EEG）信号代表了人类大脑的功能状态，可以帮助建立一种准确可行的技术来早期预测和治疗抑郁症。方法：提出了一种基于注意力的门控递归单元变换器（AttGRUT）时间序列模型来有效识别抑郁症患者的脑电图扰动。首先从60通道脑电信号数据中提取统计、频谱和小波特征。然后，使用两种特征选择技术，递归特征消除和Boruta算法，都具有Shapley加法解释，来选择基本特征。结果：所提出的模型优于两个基线和两个混合时间序列模型——长短期记忆（LSTM）、门控递归单元（GRU）、卷积神经网络LSTM（CNN-LSTM）和CNN-GRU，准确率高达98.67%。特征选择显著提高了所有时间序列模型的性能。结论：基于所获得的结果，新的特征选择极大地影响了基线和混合时间序列模型的结果。所提出的AttGRUT可以通过使用不同的预测模式在其他领域中实现和测试。补充信息：在线版本包含补充材料，可访问10.1007/s13755-022-00205-8。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Health Information Science and Systems MEDICAL INFORMATICS-

CiteScore

11.30

自引率

5.00%

发文量

期刊介绍： Health Information Science and Systems is a multidisciplinary journal that integrates artificial intelligence/computer science/information technology with health science and services, embracing information science research coupled with topics related to the modeling, design, development, integration and management of health information systems, smart health, artificial intelligence in medicine, and computer aided diagnosis, medical expert systems. The scope includes: i.) smart health, artificial Intelligence in medicine, computer aided diagnosis, medical image processing, medical expert systems ii.) medical big data, medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, optimize the use of information in the health domain, iii.) data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues, iv.) development of new architectures and applications for health information systems.