卷积神经网络转换器模型用于预测和分类早期心律失常使用心电图信号
B N Manjesh1, K N Raja Praveen2, Azadeh Amoozegar3
1Jain University; manjeshbn@gmail.com.
Journal of visualized experiments : JoVE
|July 21, 2025
概括
这项研究提出了一种深度学习模型,用于从心电图信号中准确检测心律失常,在识别五种关键心跳类型时达到99.99%的准确性,以改善心血管疾病诊断.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 心血管疾病,特别是心律失常,是全球死亡的主要原因.
- 精确和自动化的诊断技术对于早期检测至关重要.
研究的目的:
- 开发一种深度学习模型,通过心电图 (ECG) 信号对五种主要心跳类型进行分类.
- 为了实现高精度和稳健的自动心律失常检测.
主要方法:
- 使用来自多个大型数据库 (INCART,MIT-BIH等) 的I心电图信号. 有超过390万个培训段.
- 采用混合深度学习架构,将变压器层用于时间依赖和1D CNN用于空间特征提取.
- 应用数据预处理技术,包括窗口细分,Min-Max规范化和SMOTE用于类平衡.
主要成果:
- 拟议的模型在所有五种心跳类别 (正常,左束分支区块,右束分支区块,心房早跳,早心室收缩) 中实现了99.99%的卓越准确度,精度和F1得分.
- 与TN4模型等现有基准相比,表现优越.
- 通过深度混合架构和CNN验证的功能稳定性.
结论:
- 开发的AI驱动模型为可扩展的实时心律失常识别提供了一个有希望的解决方案.
- 这项技术有可能推进针对心血管疾病管理的个性化数字医疗保健.
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