一个多尺度的卷积LSTM密集网络,用于从心电图信号中强大的心律失常分类
Kishor Kumar Reddy C1, Advaitha Daduvy1, Vijaya Sindhoori Kaza1
1Department of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Hyderabad, Telangana, India.
Computers in biology and medicine
|April 15, 2025
概括
一个新的深度学习模型,MS-CLDNet,从心电图信号中准确检测心律不整. 这种先进的方法通过克服噪音和失衡等数据挑战,改善了早期心血管疾病诊断.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 心律不整是心律不规律,需要及早检测以预防严重的心血管疾病.
- 从心电图 (ECG) 信号进行自动心律失常的分类面临诸如类不平衡和噪音等挑战.
- 准确的检测对于预防性医疗保健和有效的治疗策略至关重要.
研究的目的:
- 开发一个高效的深度学习模型,MS-CLDNet,用于从心电图信号准确地分类心律失常.
- 为了应对在心电图信号分析中的阶级失衡和噪声干扰的挑战.
- 提高自动心血管诊断系统的精度和准确性.
主要方法:
- 开发了多尺度卷积式LSTM密集网络 (MS-CLDNet) 模型.
- 集成的双向长短期记忆 (LSTM) 网络,密集块和多尺度卷积神经网络 (CNN).
- 采用基于波纹的无声化来进行心电图信号预处理,并使用了MIT-BIH心律失常数据集.
主要成果:
- 该MS-CLDNet模型实现了98.22%的分类准确性.
- 与基线模型相比,与低平均损失值 (0.084) 的基线模型相比,表现出优异的性能.
- 在心律失常分类指标中显示显著改善.
结论:
- 将复杂的神经网络架构与高效的预处理相结合,可以增强自动心血管诊断.
- MS-CLDNet模型为早期和准确的心律失常检测提供了一个有希望的方法.
- 这项研究在改善心血管诊断中的医疗保健应用方面具有重大潜力.
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