• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

双相情感障碍患者在情感诱发过程中的皮肤电活动。

Electrodermal activity in bipolar patients during affective elicitation.

作者信息

Greco Alberto, Valenza Gaetano, Lanata Antonio, Rota Giuseppina, Scilingo Enzo Pasquale

出版信息

IEEE J Biomed Health Inform. 2014 Nov;18(6):1865-73. doi: 10.1109/JBHI.2014.2300940.

DOI:10.1109/JBHI.2014.2300940
PMID:25375684
Abstract

Bipolar patients are characterized by a pathological unpredictable behavior, resulting in fluctuations between states of depression and episodes of mania or hypomania. In the current clinical practice, the psychiatric diagnosis is made through clinician-administered rating scales and questionnaires, disregarding the potential contribution provided by physiological signs. The aim of this paper is to investigate how changes in the autonomic nervous system activity can be correlated with clinical mood swings. More specifically, a group of ten bipolar patients underwent an emotional elicitation protocol to investigate the autonomic nervous system dynamics, through the electrodermal activity (EDA), among different mood states. In addition, a control group of ten healthy subjects were recruited and underwent the same protocol. Physiological signals were analyzed by applying the deconvolutive method to reconstruct EDA tonic and phasic components, from which several significant features were extracted to quantify the sympathetic activation. Experimental results performed on both the healthy subjects and the bipolar patients supported the hypothesis of a relationship between autonomic dysfunctions and pathological mood states.

摘要

双相情感障碍患者的特点是行为病理性不可预测,导致抑郁状态与躁狂或轻躁狂发作之间的波动。在当前的临床实践中,精神科诊断是通过临床医生使用的评定量表和问卷进行的,而忽略了生理体征可能提供的信息。本文的目的是研究自主神经系统活动的变化如何与临床情绪波动相关。更具体地说,一组十名双相情感障碍患者接受了情绪诱发方案,以通过皮肤电活动(EDA)研究不同情绪状态下的自主神经系统动态。此外,招募了一组十名健康受试者作为对照组,并让他们接受相同的方案。通过应用反卷积方法重建EDA的紧张性和相位性成分来分析生理信号,从中提取了几个重要特征以量化交感神经激活。对健康受试者和双相情感障碍患者进行的实验结果支持了自主神经功能障碍与病理性情绪状态之间存在关联的假设。

相似文献

1
Electrodermal activity in bipolar patients during affective elicitation.双相情感障碍患者在情感诱发过程中的皮肤电活动。
IEEE J Biomed Health Inform. 2014 Nov;18(6):1865-73. doi: 10.1109/JBHI.2014.2300940.
2
On the deconvolution analysis of electrodermal activity in bipolar patients.双相情感障碍患者皮肤电活动的反卷积分析
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:6691-4. doi: 10.1109/EMBC.2012.6347529.
3
Electrodermal activity in bipolar disorder: Differences between mood episodes and clinical remission using a wearable device in a real-world clinical setting.双相障碍的皮肤电活动:在真实临床环境中使用可穿戴设备评估发作期与临床缓解期的差异。
J Affect Disord. 2024 Jan 15;345:43-50. doi: 10.1016/j.jad.2023.10.125. Epub 2023 Oct 21.
4
Electrodermal hyporeactivity as a trait marker for suicidal propensity in uni- and bipolar depression.电皮肤反应低下作为单相和双相抑郁自杀倾向的特征标志物。
J Psychiatr Res. 2013 Dec;47(12):1925-31. doi: 10.1016/j.jpsychires.2013.08.017. Epub 2013 Sep 5.
5
A functional magnetic resonance imaging study of emotional Stroop in euthymic bipolar disorder.一项关于心境正常的双相情感障碍患者情绪Stroop效应的功能磁共振成像研究。
Neuroreport. 2007 Oct 8;18(15):1583-7. doi: 10.1097/WNR.0b013e3282efa07a.
6
Mood recognition in bipolar patients through the PSYCHE platform: preliminary evaluations and perspectives.双相情感障碍患者的情绪识别:PSYCHE 平台的初步评估与展望。
Artif Intell Med. 2013 Jan;57(1):49-58. doi: 10.1016/j.artmed.2012.12.001. Epub 2013 Jan 16.
7
Electrodermal activity analysis during affective haptic elicitation.情感触觉诱发过程中的皮肤电活动分析。
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:5777-80. doi: 10.1109/EMBC.2015.7319705.
8
cvxEDA: A Convex Optimization Approach to Electrodermal Activity Processing.cvxEDA:一种用于皮肤电活动处理的凸优化方法。
IEEE Trans Biomed Eng. 2016 Apr;63(4):797-804. doi: 10.1109/TBME.2015.2474131. Epub 2015 Aug 28.
9
Impairments in "top-down" processing in bipolar disorder: a simultaneous fMRI-GSR study.双相情感障碍中“自上而下”加工的损伤:一项 fMRI-GSR 的同步研究。
Psychiatry Res. 2011 May 31;192(2):100-8. doi: 10.1016/j.pscychresns.2010.11.011. Epub 2011 Apr 14.
10
Investigating mechanical properties of a fabric-based affective haptic display through electrodermal activity analysis.通过皮肤电活动分析研究基于织物的情感触觉显示器的机械性能。
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:407-410. doi: 10.1109/EMBC.2016.7590726.

引用本文的文献

1
Randomized controlled study of a digital data driven intervention for depressive and generalized anxiety symptoms.一项针对抑郁和广泛性焦虑症状的数字数据驱动干预措施的随机对照研究。
NPJ Digit Med. 2025 Feb 19;8(1):113. doi: 10.1038/s41746-025-01511-7.
2
Identifying digital biomarkers of illness activity and treatment response in bipolar disorder with a novel wearable device (TIMEBASE): protocol for a pragmatic observational clinical study.使用新型可穿戴设备(TIMEBASE)识别双相情感障碍中疾病活动和治疗反应的数字生物标志物:一项实用观察性临床研究的方案
BJPsych Open. 2024 Aug 1;10(5):e137. doi: 10.1192/bjo.2024.716.
3
Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study.
探索心境发作中疾病活动的数字生物标志物:假设生成和模型开发研究。
JMIR Mhealth Uhealth. 2023 May 4;11:e45405. doi: 10.2196/45405.
4
Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review.用于重度抑郁发作鉴别诊断的数字表型分析:叙述性综述
JMIR Ment Health. 2023 Jan 23;10:e37225. doi: 10.2196/37225.
5
A Predictive Model for Emotion Recognition Based on Individual Characteristics and Autonomic Changes.一种基于个体特征和自主神经变化的情绪识别预测模型。
Basic Clin Neurosci. 2022 May-Jun;13(3):285-294. doi: 10.32598/bcn.2021.632.3. Epub 2022 May 1.
6
Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review.基于皮肤电活动的唤醒度分类的机器学习技术:系统综述。
Sensors (Basel). 2022 Nov 17;22(22):8886. doi: 10.3390/s22228886.
7
Machine Learning for Healthcare Wearable Devices: The Big Picture.机器学习在医疗可穿戴设备中的应用:全局概览。
J Healthc Eng. 2022 Apr 18;2022:4653923. doi: 10.1155/2022/4653923. eCollection 2022.
8
Emotion Recognition Using Electrodermal Activity Signals and Multiscale Deep Convolutional Neural Network.使用皮肤电活动信号和多尺度深度卷积神经网络进行情绪识别。
J Med Syst. 2021 Mar 4;45(4):49. doi: 10.1007/s10916-020-01676-6.
9
Skin conductance responses in Major Depressive Disorder (MDD) under mental arithmetic stress.精神算术应激下的重性抑郁障碍(MDD)的皮肤电反应。
PLoS One. 2019 Apr 3;14(4):e0213140. doi: 10.1371/journal.pone.0213140. eCollection 2019.
10
Detecting Manic State of Bipolar Disorder Based on Support Vector Machine and Gaussian Mixture Model Using Spontaneous Speech.基于支持向量机和高斯混合模型利用自发语音检测双相情感障碍的躁狂状态。
Psychiatry Investig. 2018 Jul;15(7):695-700. doi: 10.30773/pi.2017.12.15. Epub 2018 Jul 4.