为呼吸阶段检测优化MFCC参数
Assel K Zhantleuova1, Yerbulat K Makashev2, Nurzhan T Duzbayev1
1Department of Computer Engineering, International Information Technology University, Almaty 050040, Kazakhstan.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
优化Mel频率塞普斯特尔系数 (MFCC) 参数可以显著提高呼吸阶段检测的准确性. 优化设置可以提高临床监测应用的分类性能.
科学领域:
- 生物医学工程
- 信号处理
- 数字健康
背景情况:
- 呼吸阶段检测对于临床和数字健康应用至关重要.
- 标准的Mel频率 Cepstral系数 (MFCC) 设置往往会产生低于最佳的分类性能.
- 需要改进呼吸声分析的特征提取方法.
研究的目的:
- 为增强呼吸阶段检测系统地优化Mel频率塞普斯特尔系数 (MFCC) 的参数.
- 使用支持矢量机 (SVM) 和深度学习模型评估MFCC参数优化对分类准确性的影响.
- 在实时临床应用中确定准确性和延迟之间的实际权衡.
主要方法:
- 使用了1500个呼吸声段的专有数据集.
- 包括系数数量,长度和跳跃长度在内的Mel频率塞普斯特尔系数 (MFCC) 参数被系统优化.
- 使用支持矢量机器 (SVM) 评估分类性能,并与深度学习模型 (VGGish,YAMNet,MobileNetV2) 进行比较.
主要成果:
- 最佳的MFCC参数 (30系数,800毫秒的长,10毫秒的跳跃长) 实现了87.16%的准确性,比默认设置 (80.96%) 显著改善.
- 优化的MFCC表现出相当于或优于已知的深度学习方法的性能.
- 在临床上实用的200 - 300毫秒的长度提供了准确度 (85. 08%) 和延迟之间的有利平衡.
结论:
- 优化的MFCC参数大大提高了呼吸阶段分类的准确性.
- 这些优化的参数为实时呼吸监测提供了高效和可解释的解决方案.
- 建议在不同的临床环境中进一步验证并探索高级学习策略.
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