ECGPsychNet:一种优化的混合组合模型,用于使用心电图信号自动检测精神疾病
Smith Kashiram Khare1, Vikram M Gadre2, Rajendra Acharya3
1Electrical and Computers, Aarhus Universitet, Finlandsgade 22, Aarhus N, Aarhus, 8200, DENMARK.
Physiological measurement
|October 6, 2023
概括
电心电图 (ECG) 信号可以检测精神疾病,如精神分裂症 (SCZ),双相情感障碍 (BD) 和抑郁症 (DPR). ECGPsychNet模型实现了高精度,为这些疾病提供了新的诊断工具.
科学领域:
- 神经科学和计算精神病学
- 生物医学信号处理
- 医疗保健中的人工智能
背景情况:
- 包括精神分裂症 (SCZ),双相情感障碍 (BD) 和抑郁症 (DPR) 在内的精神疾病是全球重大健康挑战.
- 延迟SCZ,BD和DPR的诊断归因于症状的变化,耻辱和有限的生理标志物.
- 在SCZ,BD和DPR患者中,大脑功能变化表明大脑和心脏活动之间存在联系.
研究的目的:
- 调查心电图 (ECG) 信号对于检测SCZ,BD和DPR的有用性.
- 开发一种自动化系统,使用心电图数据来检测精神疾病.
- 探索大脑-心脏相互作用作为这些条件的潜在生物标志物.
主要方法:
- 开发了ECGPsychNet,一个集体分解和分类技术.
- 利用经验模式分解,变化模式分解和可调的Q波束转换 (TQWT) 来将心电图信号分解为子频段 (SB).
- 从SB中提取特征,并采用可优化的组合分类器,具有双重验证.
主要成果:
- 通过使用TQWT第六个SB的特征,ECGPsychNet实现了98.15%的最高分类准确度.
- 该模型显示了高的检测率:SCZ的98.96%,DPR的96.04%,BD的95.12%.
- 这项研究强调了基于心电图的分析在区分精神疾病方面的有效性.
结论:
- 开发的ECGPsychNet原型成功地使用心电图信号检测出精神分裂症,抑郁症和双相情感障碍.
- 这种自动化系统显示出作为精神疾病查的非侵入性工具的前景.
- 为了更广泛的适用性,建议对不同种族和年龄组的不同数据集进行进一步验证.
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