在心电图传感器数据中应用反复神经网络用于异常检测
Ana Minic1, Luka Jovanovic2, Nebojsa Bacanin2
1Teacher Education Faculty, University of Pristina in Kosovska Mitrovica, 38220 Kosovska Mitrovica, Serbia.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
这项研究使用循环神经网络来检测心电图 (ECG) 读数中的心脏异常. 优化的模型显示了心脏诊断中的真实应用的巨大潜力.
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:
- 电心电图 (ECG) 对于诊断心脏疾病至关重要.
- 准确的ECG解释需要广泛的培训和专业知识.
- 在ECG中自动检测异常可以提高诊断效率.
研究的目的:
- 探索复发性神经网络 (RNN) 在心电图数据中异常检测的有效性.
- 优化RNN架构和训练参数以提高性能.
- 将优化RNN的性能与其他当代优化方法进行比较.
主要方法:
- 经常性神经网络 (RNN) 用于在心电图信号中检测异常.
- 使用修改后的粒子群优化 (PSO) 算法来调整网络参数和架构.
- 性能评估涉及将优化的RNN与其他优化器的模型进行比较.
主要成果:
- 优化的RNN模型显示了在ECG异常检测中实际应用的巨大潜力.
- 与当代优化器相比,提出的优化方法产生了更高的性能.
- 对表现最好的模型进行了特征重要性分析.
结论:
- 经常性神经网络,当优化时,为自动ECG异常检测提供了一个有希望的方法.
- 粒子群集优化有效地提高了RNN在心脏诊断方面的性能.
- 这种方法有可能支持临床决策和改善患者护理.
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