从语音录音中检测帕金森病,使用关联记忆
Irving Luna-Ortiz1, Mario Aldape-Pérez1, Abril Valeria Uriarte-Arcia1
1Instituto Politécnico Nacional, Center for Computing Innovation and Technological Development (CIDETEC), Computational Intelligence Laboratory (CIL), Mexico City 07700, Mexico.
Healthcare (Basel, Switzerland)
|June 10, 2023
概括
一个新的算法,ISNDAM,显著改善了早期帕金森症.
科学领域:
- 神经学和机器学习
- 生物医学信号处理
背景情况:
- 帕金森病 (PD) 诊断是具有挑战性的,因为它具有慢性和渐进性.
- 早期的PD诊断对于管理病情和改善患者的治疗结果至关重要.
- 现有的联想性记忆 (AM) 模型用于PD诊断缺乏去除无关特征的机制,这可能会限制分类性能.
研究的目的:
- 增强最小规范差异关联记忆 (SNDAM) 算法,以改进PD分类.
- 在SNDAM算法中引入一个学习强化阶段,创建ISNDAM模型.
- 评估拟议的ISNDAM模型的分类性能,使用PD患者和健康个体的语音样本.
主要方法:
- 这项研究提出了一种改进的关联记忆算法ISNDAM,它包含一个学习强化阶段.
- 利用了来自UCI机器学习库的两个公开数据集,包括早期PD患者和健康个体的语音样本.
- 将ISNDAM模型的效率与WEKA工作台和之前的研究中的七十个其他模型进行了比较,并进行了统计学意义分析.
主要成果:
- 在数据集1上,ISNDAM的分类准确率达到99.48%,在数据集2上达到99.66%.
- 在数据集1上,ISNDAM的性能超过了其他知名算法,包括ANN Levenberg-Marquardt (95.89%) 和SVM RBF内核 (88.21%) 在数据集1上.
- 统计学意义测试证实ISNDAM的分类性能与以前研究中的模型相当.
结论:
- 拟议的ISNDAM算法有效地提高了使用语音样本进行早期帕金森病诊断的分类性能.
- 与现有算法和以前的研究相比,ISNDAM表现出卓越的准确性和竞争性性能.
- 学习强化阶段的整合是ISNDAM改善诊断能力的一个关键因素.
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