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心脏声音分类用于早期检测心血管疾病使用XGBoost和工程声学特征
P P Satya Karthikeya1, P Rohith1, B Karthikeya1
1Amrita School of Artificial Intelligence, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore 641112, India.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究表明,传统的声学特征,而不是Wav2Vec嵌入式,是使用机器学习对心脏声音进行分类的最佳选择. XGBoost实现了99%的准确性,改善了心血管疾病的检测.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 通过心脏声音分析早期发现心血管疾病 (CVD) 对患者的治疗结果至关重要.
- 传统的声学分析和现代的深度学习嵌入式技术为心脏声音的分类提供了潜力.
研究的目的:
- 为了比较传统声学特征与Wav2Vec嵌入式的有效性,用于多类心脏声音分类.
- 用这些不同的功能集来评估混合CNN+LSTM和XGBoost模型.
主要方法:
- 提取了十个工程声学特征 (例如,MFCC,DWT,ZCR) 和Wav2Vec嵌入式.
- 在五个心声类别中训练和评估CNN+LSTM和XGBoost模型.
- 使用超参数调整和交叉验证在一个持有测试集.
主要成果:
- 用工程声学特征训练的模型始终优于使用Wav2Vec嵌入式的模型.
- XGBoost实现了最高的精度 (99%),超过了混合CNN+LSTM模型 (98%).
结论:
- 域特定特征工程对于心脏声音分类仍然非常有效.
- XGBoost表现出强大的性能,为早期心血管疾病检测提供了一个有前途的工具.
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