微调Wav2Vec 2.0用于多模式分类异常心脏声音
概括
这项研究提高了心血管疾病 (CVD) 检测使用心脏声音数据的深度学习. 增强数据集提高了分类器的准确性,提供了更有效的CVD预选方法.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因.
- 早期检测心血管疾病对于有效治疗至关重要,需要准确且负担得起的预先查工具.
- 深度学习模型显示,对分类异常的心脏声音 (使用心电图和心电图信号) 表明心血管疾病有希望.
研究的目的:
- 解决同步心电图 (PCG) 和心电图 (ECG) 数据稀缺的局限性,用于训练最先进的深度学习模型.
- 探索使用传统的信号处理和消除噪声的扩散模型 (WaveGrad,DiffWave) 进行数据增强.
- 在增强数据集上微调Wav2Vec 2.0分类器,以改进心血管疾病检测.
主要方法:
- 利用传统的信号处理技术与 WaveGrad 和 DiffWave 结合,消除噪音的扩散模型来增强有限的 PCG 和 ECG 数据集.
- 采用基于Wav2Vec 2.0的分类器,对增强数据集进行了微调.
- 在2016年心脏病计算 (CinC) 培训中评估模型性能 - - 仅使用PCG信号的数据集和所有CinC数据库.
主要成果:
- 在CinC 2016培训数据集上与最先进的方法相比,获得了优异的性能,其准确性,未加权平均回忆,灵敏度,特异性以及马修的相关系数分别为93.14%,92.21%,93.35%,90.10%和0.838.
- 仅使用PCG信号在所有CinC数据库中表现出强的表现,以92.98%,92.48%,93.63%,92.48%和0.8238.8%的得分优于现有方法.
- 在更大的数据集上验证了基于变压器的自我监督微调模型的有效性,并补充了数据增强.
结论:
- 使用扩散模型的数据增强显著提高了深度学习分类器的性能,用于从心脏声信号检测心血管疾病.
- 拟议的方法为开发准确且具有成本效益的心血管疾病预查工具提供了一个有希望的途径.
- 基于增强数据集的微调变压器模型是克服医疗信号处理应用中的数据稀缺性的可行策略.
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