A Neural Network-based method for continuous blood pressure estimation from a PPG signal
A Neural Network-based method for continuous blood pressure estimation from a PPG signal
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DOI：
10.1109/i2mtc.2013.6555424
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发表时间：
2013-05
期刊：
影响因子：
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通讯作者：
Y. Kurylyak;F. Lamonaca;D. Grimaldi
中科院分区：
文献类型：
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作者：
Y. Kurylyak;F. Lamonaca;D. Grimaldi
从光容积脉搏波（PPG）信号中得到的血压和脉搏持续时间之间存在一种关系，但并不总是线性的。为了从PPG信号中估计血压，本文使用了人工神经网络（ann）。训练数据从重症监护多参数智能监测波形数据库中提取，以便更好地表示可能的脉搏和压力变化。总共分析了15000多个心跳，并从每个心跳中提取了21个参数，这些参数定义了人工神经网络的输入向量。估计值与参考值的比较表明，该方法的准确度优于线性回归方法，符合美国医疗器械进步协会国家标准。
There is a relation, not always linear, between the blood pressure and the pulse duration, obtained from photoplethysmography (PPG) signal. In order to estimate the blood pressure from the PPG signal, in this paper the Artificial Neural Networks (ANNs) are used. Training data were extracted from the Multiparameter Intelligent Monitoring in Intensive Care waveform database for better representation of possible pulse and pressure variation. In total there were analyzed more than 15000 heartbeats and 21 parameters were extracted from each of them that define the input vector for the ANN. The comparison between estimated and reference values shows better accuracy than the linear regression method and satisfy the American National Standards of the Association for the Advancement of Medical Instrumentation.