一个新的基于三元模式的自动精神疾病分类,使用心电图信号
Burak Tasci1, Gulay Tasci2, Sengul Dogan3
1Vocational School of Technical Sciences, Firat University, 23119 Elazig, Turkey.
Cognitive neurodynamics
|February 26, 2024
概括
这项研究引入了一种新的心电图分析方法,可以自动检测双相情感障碍,抑郁症和精神分裂症. 该模型实现了高精度,为心理健康的新可穿戴诊断设备铺平了道路.
科学领域:
- 计算神经科学是一种计算神经科学.
- 生物医学信号处理
- 医疗保健中的人工智能
背景情况:
- 神经精神疾病是导致残疾的主要原因,由于缺乏确定的诊断测试而复杂化.
- 由于不同的治疗方法,区分双相情绪障碍,抑郁症和精神分裂症等疾病至关重要.
- 心电图 (ECG) 信号可能反映出精神疾病中与大脑-心脏自主连接相关的变化.
研究的目的:
- 使用心电图 (ECG) 信号自动分类双相情感障碍,抑郁症和精神分裂症.
- 开发一种新的,手工制作的特征工程模型,用于从线性时间复杂性的心电图节拍中准确检测精神疾病.
- 通过使用新的数据集和先进的信号处理技术来评估拟议模型的性能.
主要方法:
- 收集了3570个ECG心跳的新数据集,分为四个类别:双相情感障碍,抑郁症,精神分裂症和控制.
- 采用基于三元模式的信号分类模型,涉及多层次的特征提取 (离散波形变换,三元模式),特征选择 (代Chi2选择器),分类 (人工神经网络 - ANN) 和集体投票 (代多数投票 - IMV).
- 使用十倍交叉验证进行可靠的绩效评估,计算单个领导和总体投票准确度.
主要成果:
- 该ANN分类器实现了从73.67%到89.19%的对准确率.
- 代多数投票 (IMV) 方法提高了整体分类性能,从89.19%提高到96.25%.
- 提出的基于三元模式的模型成功地通过心电图信号对精神疾病进行了分类.
结论:
- 开发的基于三元模式的信号处理模型有效地使用心电图数据对双相情感障碍,抑郁症和精神分裂症进行分类.
- 取得的高分类准确度验证了该模型在自动化精神疾病检测方面的潜力.
- 该模型的成功表明,开发用于实时心理健康监测的新可穿戴设备的可行性.
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