优化深度残留网络,通过心电图信号早期检测心肌梗塞
Pon Bharathi A1, Madavan R2, Sakthivel E3
1Department of Electronics and Communication Engineering, Amrita College of Engineering and Technology, Nagercoil, Kanyakumari, Tamil Nadu, 629901, India. ponbharathia122@gmail.com.
BMC cardiovascular disorders
|May 17, 2025
概括
这项研究引入了一种新的深度残留网络 (DRN),通过社会滑雪蜘蛛 (SSS) 算法进行优化,以准确检测心肌梗塞 (MI). 该SSS-DRN方法减少了过和计算负载,改善了早期发现心脏病发作.
科学领域:
- 心脏病学和人工智能的人工智能
- 生物医学信号处理
背景情况:
- 心肌梗塞 (MI) 是全球主要的死亡原因,需要迅速和准确的检测.
- 现有的MI识别方法经常在实时应用中扎过度装配和高计算需求.
研究的目的:
- 开发一种新的,高效的技术来检测心肌梗塞,使用一个优化的深度残留网络 (DRN).
- 解决当前MI检测系统中过度装配和计算负担的局限性.
主要方法:
- 一个混合优化算法,社会滑雪蜘蛛 (SSS),结合社会滑雪驾驶员 (SSD) 和蜘蛛优化 (SMO),被开发来优化一个深度剩余网络 (DRN).
- 该SSS-DRN模型通过特征提取 (信号,转换,医学,统计) 和数据增强 (变换,随机生成,重新采样) 来处理心电图 (ECG) 信号.
主要成果:
- 该SSS-DRN模型实现了高检测准确度,灵敏度和特异性,分别为0.916,0.921和0.926.
- 开发的模型比传统方法显著提高了性能,精度提高了2%至13.96%.
结论:
- SSS-DRN为心肌梗塞检测提供了一个高度准确和计算高效的方法.
- 该模型的有效性表明,它有可能被整合到实时临床环境中,包括心电图机,可穿戴设备和移动健康应用程序,以提高患者的治疗效果.
关键词:
数据增强的数据增强.深度学习 (Deep Learning) 是一种深度学习.这是一个ECGECGECGECGECG.功能提取 功能提取在这里,我们可以看到MI MI MI MI MI.优化优化 优化优化这是SSS-DRNN.更多相关视频
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