一个深度的Bi-CapsNet用于分析心电图信号以分类心律失常
T Anitha1, S Aanjankumar2, Rajesh Kumar Dhanaraj3
1Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India.
Computers in biology and medicine
|March 14, 2025
概括
一个新的深度双向囊网络 (Bi-CapsNet) 准确地从心电图信号中分类心律不整. 这种智能深度学习模型实现了97.19%的准确性,超过了心脏病预测的传统方法.
科学领域:
- 心脏病学和人工智能的人工智能
- 医疗信号处理 医疗信号处理
- 为医疗保健提供深度学习.
背景情况:
- 电心电图 (ECG) 对于查心律失常至关重要.
- 准确的特征提取和分类对于自动心脏病预测至关重要.
- 现有的深度学习模型需要改进,以便精确地对心律失常进行分类.
研究的目的:
- 开发和验证一个深度双向囊网络 (Bi-CapsNet) 以准确地分类心律失常.
- 使用心电图数据提高自动心脏病预测的精度.
- 将Bi-CapsNet的性能与传统的深度学习模型进行比较.
主要方法:
- 收获和预处理ECG信号 (移除直流漂移,规范化,过,移除文物).
- 使用深度合奏CNN-RNN方法进行特征提取.
- 使用Deep Bi-CapsNet模型对心律不整进行分类.
- 在MIT-BIH心律失常数据库上进行验证,识别正常,RBBB,PVC,APB和LBBB类型.
主要成果:
- 拟议的Bi-CapsNet模型实现了97.19%的整体准确性.
- 双CapsNet的表现优于传统模型:CNN (89.87%),FTBO (85%) 和囊网络 (97.0%).
- 综合性绩效分析包括精度,F1分数,灵敏度和特异性等指标.
结论:
- 开发的Bi-CapsNet模型在将心律不整从心电图信号分类方面表现出卓越的性能.
- 这种智能深度学习方法在自动心脏病预测方面取得了重大进展.
- 混合模型为临床心律失常查提供了一个高度准确和有效的方法.
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