一个新的粒子群优化增强的深度神经网络,用于自动ECG心律失常的分类
Yaaqoub Kahlessenane1,2, Fatiha Bouaziz1,2, Patrick Siarry3
1Electronic Department, Jijel University, BP 98, Ouled Aissa Jijel 18000, Algeria.
概括
粒子群优化 (PSO) 自动深度神经网络超参数调整为心电图 (ECG) 分类实现了99.76%的准确性. 这种方法提高了模型的可靠性,并减少了分析心跳的手动工作.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 电心电图 (ECG) 信号分析对于诊断心脏病状况至关重要.
- 自动分类系统需要对深度神经网络 (DNN) 进行高效的超参数优化.
- 手动调整 DNN 超参数是耗时且低于最佳的.
研究的目的:
- 开发一个使用粒子群优化 (PSO) 进行DNN超参数调整的自动ECG分类系统.
- 提高心电图心跳分类的准确性和可靠性.
- 为了减少用于优化深度学习模型用于心电图分析的手工工作.
主要方法:
- 用波纹分解处理心电图信号以提取特征.
- 粒子群优化 (PSO) 用于调整五个关键的DNN超参数:神经元数量,中断率,学习率和优化器.
- 该分类是在MIT-BIH心律失常数据库上进行的,区分了五个心跳类别.
主要成果:
- 针对PSO优化的DNN模型实现了99.76%的分类准确度.
- 该系统显示了高精度 (99.34%) 和F1得分 (99.21%).
- 结果表明,与传统方法相比,性能优越.
结论:
- 粒子群集优化在心电图分类中的自动化超参数调整中是有效的.
- 拟议的系统显著提高了自动心跳分析的准确性和可靠性.
- 这种方法为临床心电图解释提供了强大而高效的解决方案.
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