在COPD诊断中使用基于CycleGAN和机器学习的呼吸信号生成的新方法
Kien Le Trung1, Phuong Nguyen Anh1, Trong-Thanh Han1
1School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam.
Computer methods in biomechanics and biomedical engineering
|March 29, 2024
概括
这项研究使用人工智能和呼吸道声音分析来诊断慢性阻塞性肺病 (COPD). 一种新的方法实现了99.75%的准确性,使得早期发现COPD成为可能.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 呼吸系统医学 呼吸系统医学
背景情况:
- 慢性阻塞性肺病 (COPD) 诊断通常依赖于晚期指标,影响患者的生活质量并增加医疗保健成本.
- 早期和准确的COPD检测对于有效的疾病管理和改善患者结果至关重要.
- 呼吸道声音中独特的声学特征为非侵入性诊断标记提供了潜在的潜力.
研究的目的:
- 开发和评估一种用于诊断COPD的分类方法,使用从呼吸声中提取的独特特征.
- 基于声信号的COPD识别的各种算法的诊断性能进行比较.
- 利用逆转换技术和深度学习来提高COPD检测.
主要方法:
- 呼吸声被细分为个别的呼吸周期,并使用CycleGAN模型增强数据多样性.
- 波形家族和光谱转换被用来分析特征性呼吸信号.
- 在分类任务中使用了深度学习模型,包括VGG16,ResNet50和InceptionV3.
主要成果:
- 这项研究证明了COPD诊断的拟议分类方法的有效性.
- 最优的方法是将Wavelet Bior1.3标准化与InceptionV3深度学习模型相结合.
- 这种表现最好的方法实现了高精度的F1得分99.75%.
结论:
- 与深度学习模型集成的逆转换技术提供了高度准确的COPD检测.
- 对声学呼吸系统特征的AI驱动分析表明,早期COPD诊断的可行性.
- 这种方法有望改善COPD早期干预策略和患者管理.
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