基于Hybrid Sneaky算法的深度神经网络,用于使用心电图进行心脏声音分类.
Rajveer K Shastri1, Aparna R Shastri1, Prashant P Nitnaware2,3
1Electronics and Telecommunication, Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering and Technology, Baramati, Maharashtra, India.
Network (Bristol, England)
|November 29, 2023
概括
这项研究引入了使用混合优化控制深度学习策略的自动心声分类模块. 该方法通过高效的心电图分析来实现诊断心脏疾病的高准确性.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物医学信号处理
背景情况:
- 心脏声音分析对于诊断心脏疾病至关重要,需要准确有效的分类方法.
- 早期发现心脏病状况可以改善患者的治疗结果,并减少医疗负担.
- 计算机化心脏声音分类为提高诊断准确性和速度提供了一个有希望的途径.
研究的目的:
- 提出使用混合优化控制深度学习策略的自动心声分类模块.
- 优化深度神经网络 (DNN) 分类器参数使用新的混合潜入优化算法.
- 通过整合从心电图 (PCG) 数据中提取的各种特征来提高分类性能.
主要方法:
- 开发了一种混合优化控制的深度学习策略,用于自动分类心脏声音.
- 混合潜入优化算法,结合探索和社会搜索特征,用于DNN参数调整.
- 从PCG数据库提取的特征包括统计特征,心率变量 (HRV) 和Mel频率 Cepstral系数 (MFCC).
主要成果:
- 开发的基于Sneaky优化的DNN分类器展示了高性能指标.
- 报告的精度,准确性,特异性和灵敏度分别为大约97%,96.98%,97%和96.9%.
- 整合MFCC功能进一步增强了该模型的分类能力.
结论:
- 拟议的混合优化控制深度学习模块为自动心声分类提供了有效的方法.
- 混合潜入优化算法成功调整了DNN参数,从而实现了卓越的诊断性能.
- 这种方法具有显著的潜力,可以改善心脏疾病的早期和准确诊断.
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