SpectroNet-LSTM:一种可解释的深度学习方法,通过心跳声音分析来检测心脏异常
Abhiram Sharma1, R Srivats1, Krishna P B1
1School of Computer Science and Engineering, Vellore Institute of Technology - Chennai Campus, Chennai 600127, Tamil Nadu, India.
Computers in biology and medicine
|August 11, 2025
概括
本研究介绍了SpectroNet-LSTM,这是一个自动化系统,用于通过深度学习和心跳声学分析来检测心脏异常. 它为心血管诊断提供了一种可解释和可访问的方法.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
背景情况:
- 心脏异常带来重大健康风险,需要早期检测以改善患者的治疗结果.
- 目前用于心脏门疾病的诊断方法往往需要专门的专业知识和设备.
- 每年有超过1300万人受到心脏膜疾病的影响,这突显了对先进诊断工具的需求.
研究的目的:
- 开发一个自动化框架,SpectroNet-LSTM,用于从心跳声音记录中检测心脏异常.
- 提高临床使用的自动心脏异常检测系统的可解释性.
- 利用深度学习和高级特征提取来提高诊断准确度.
主要方法:
- 采用Mel频 cepstral 系数 (MFCC) 和光谱分析用于声学特征提取.
- 训练有素的深度学习模型包括ResNet101,VGG16和Inception V3在提取的心跳特征上.
- 综合可解释AI (XAI) 技术,SHAP和LIME,用于模型解释性.
主要成果:
- 与基准标准相比,SpectroNet-LSTM模型在检测心脏异常方面表现优越.
- 该系统成功捕获了关键的声学特征,以准确识别异常.
- 可解释的AI技术提供了对模型的决策过程的可视化和理解.
结论:
- SpectroNet-LSTM为心脏异常检测提供了一种新的,自动化的,可解释的解决方案.
- 功能提取,深度学习和XAI的整合增强了心血管诊断.
- 这项研究通过自动化促进了全球范围内可访问的医疗保健解决方案和高效的患者结果.
相关概念视频
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