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首页> 外文会议>Iberoamerican congress on pattern recognition >Onset and Peak Pattern Recognition on Photoplethysmographic Signals Using Neural Networks
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Onset and Peak Pattern Recognition on Photoplethysmographic Signals Using Neural Networks

机译：使用神经网络对光电容积描记信号的起峰和峰模式识别

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摘要

Traditional methodologies use electrocardiographic (ECG) signals to develop automatic methods for onset and peak detection on the arterial pulse wave. In the present work a Multilayer Perceptron (MLP) neural network is used for classifying fiducial points on photoplethysmographic (PPG) signals. System was trained with a dataset of temporal segments from signals located based on information about onset and peak points. Different segments sizes and units in the neural network were used for the classification, and optimal values were searched. Results of the classification reach 98.1% in worse of cases. This proposal takes advantages from MLP neural networks for pattern classification. Additionally, the use of ECG signal was avoided in the presented methodology, making the system robust, less expensive and portable in front of this problem.
机译：传统方法使用心电图（ECG）信号来开发自动方法以对动脉脉搏波进行发病和峰值检测。在本工作中，多层感知器（MLP）神经网络用于对光电容积描记（PPG）信号上的基准点进行分类。系统根据时间段的数据集对系统进行了训练，这些时间段是根据有关发病和高峰点的信息而定位的。使用神经网络中不同的片段大小和单位进行分类，并搜索最佳值。在较差的情况下，分类结果达到98.1％。该提议利用了MLP神经网络进行模式分类的优势。另外，在所提出的方法中避免了使用ECG信号，从而使该系统更坚固，成本更低且可移植性更强。

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