HBNET:一种混合组合模型,用于检测心血管异常,使用声心图
Ann Nita Netto1, Lizy Abraham1, Saji Philip2
1Department of Electronics and Communication Engineering, LBS Institute of Technology for Women, APJ Abdul Kalam Technological University, Trivandrum, India.
概括
这项研究介绍了HbNet,这是一种用于分类心脏声音的深度学习模型. 在成人和儿童中检测心脏病时,HbNet实现了高准确度,有助于早期诊断.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 心脏病导致全球显著的死亡率.
- 早期检测和治疗对于管理心脏病状况至关重要.
- 在儿科心脏声音中区分功能和病理的声是具有挑战性的.
研究的目的:
- 开发一种新的混合组合模型,将成人和儿科心脏声音分为五类.
- 创建一个全面的5类儿科心电图 (PCG) 数据集,包括特定的缺陷和声.
- 提高心声分类的准确性和可靠性,以提供更好的诊断支持.
主要方法:
- 一个混合组合模型,HbNet (心跳网络),是使用混合CNN-BiLSTM和CNN-LSTM模型与Softmax回归开发的.
- 为了提取相关的音频信号特征,使用了Mel频率 Cepstral 系数 (MFCC).
- 该模型在成人和儿科心脏声音数据集上进行了训练和验证.
主要成果:
- 在成年人数据集上,HbNet实现了99.72%的准确度和99.3%的灵敏度,超过了现有的方法.
- 在一个儿科数据集上,HbNet显示了86.5%的准确性.
- 该模型以100%的精度检测到功能声.
结论:
- HbNet在早期发现心脏疾病方面表现出很高的疗效.
- 该模型可以作为医疗从业者的决策支持工具.
- 这种方法促进了早期的诊断和治疗,可能减少不必要的心声回声图.
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