使用深度学习算法实时计数来自肺部声音的喘息事件:对疾病预测和早期干预的影响
Sunghoon Im1, Taewi Kim1, Choongki Min2
1Department of Mechanical Engineering, Ajou University, Suwon-si, Gyeonggi-do, Republic of Korea.
PloS one
|November 20, 2023
概括
本研究引入了使用1D-CNN-LSTM模型的实时喘息计数算法. 它准确地识别出异常的呼吸声,以改善远程医疗和肺部疾病监测.
科学领域:
- 计算智能是一种计算智能.
- 医疗信息学医学信息学
- 呼吸系统药物 呼吸系统药物
背景情况:
- 远程患者监测和自我症状管理对于慢性呼吸道疾病至关重要.
- 现有的肺声分类算法在连续数据和实时分析方面存在困难.
- 准确的喘息检测对于肺部疾病的早期诊断和干预至关重要.
研究的目的:
- 开发和验证一个实时喘息计数算法,以彻底改变远程医疗和远程监控.
- 为了准确地识别和量化个体呼吸周期内的喘息.
- 为长期呼吸模式研究提供呼吸声的持续分析.
主要方法:
- 结合一个一维卷积神经网络 (1D-CNN) 和一个长短期记忆 (LSTM) 网络的混合模型被采用.
- 呼吸循环被细致地分为三个类别:休息,正常和喘息.
- 来自多个来源的535个呼吸周期的多样化数据集被用于培训和验证.
主要成果:
- 开发的算法在识别呼吸周期和检测异常声音方面实现了90%的分类准确性.
- 成功启用了所有呼吸的实时喘息计数.
- 该系统展示了处理连续呼吸声数据的能力.
结论:
- 实时喘息计数算法在呼吸声分析方面取得了重大进展.
- 这项技术有可能通过长期呼吸模式分析来改变肺部疾病的预测.
- 该算法适用于临床和非临床环境,用于立即检测和远程干预呼吸系统症状.
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