利用多重反向传播神经网络 (BPNN) 实时检测异常心跳的原型

Suryani, Faizal
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引用次数: 0

摘要

实时心率监测和早期心脏异常检测对于在心脏健康恶化之前确定心脏健康状况至关重要。为了实现这一目标,本项目采用了反向传播神经网络(BPNN)方法,包括通过输入以 BPM 为单位的心跳值,将心跳分为正常和异常,这些心跳值是利用传感器 Easy Pulse 和 NodeMCU 等传感器原型得出的,同时还考虑了年龄和体育活动因素。来自传感器的所有数据都将存储在 Firebase 中。然后,Firebase 将连接到 Android,正常和异常心脏分类结果将显示在 Android 系统上。模拟结果成功地以 40 人为样本,并提供了实时心率监测、年龄和运动量等信息作为输入。这项研究旨在为改善各公共卫生服务中心的医疗服务和独立早期检测心脏健康做出贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prototype Realtime Detection Of Abnormal Heart Beat Using Multiple Back Propagation Neural Network (BPNN)
Real-time heart rate monitoring and early detection of heart abnormalities are vital to determine heart health before it worsens. To achieve this goal, this project uses the backpropagation neural network (BPNN) method including its capability to classify heartbeats into normal or abnormal by inputting heartbeat values in BPM units derived from prototypes utilizing sensors like Sensor Easy Pulse and NodeMCU, along with considerations of age and sports activity. All data from sensors will be stored in Firebase. Then Firebase will connect to Android, and the normal and abnormal heart classification results will be displayed on the Android system. Simulation results successfully examined 40 people as a sample and provided information from real-time heart rate monitoring, age, and sports activity as input. This research seeks to contribute to improving health services at various public health service centers and independently in detecting heart health early.
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