基于CNN的医疗物联网高效PPG分析设备中原始PPG信号质量评估

Yalagala Sivanjaneyulu, M. Manikandan, Srinivas Boppu
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引用次数: 2

摘要

对于医疗物联网(IoMT)支持的长期健康监测和疾病预测应用,需要自动光容积脉搏图(PPG)信号质量评估(SQA),以减少误报和能耗。利用原始PPG信号和具有最优参数的卷积神经网络(CNN),提出了一种PPG- sqa方法。本文的主要重点是寻找具有整流线性单元(ReLU)激活函数的最优滤波器数(16、32、64)和层数(2层和4层),并利用未见过的PPG数据集和不同类型的噪声源研究训练后的CNN模型的鲁棒性,这是以往基于CNN的PPG- sqa方法研究中没有解决的问题。评估结果表明,基于cnn的4层方法对无噪声PPG (NF-PPG)与带腕杯噪声PPG信号数据库(MA-DB01)的准确率为99.58%,对带随机噪声PPG (RN-PPG)信号的准确率为99.99%,对NF-PPG与加速损坏PPG信号(MA-DB02)的准确率为75.80%。对于未知数据集,4层CNN模型对NF-PPG与MA-DB01的准确率为96.71%,对NF-PPG与RN-PPG的准确率为99.04%,2层CNN模型对NF-PPG与MA-DB02的准确率为76.16%。结果表明,基于cnn的最优参数PPG-SQA方法与其他现有方法相比,不仅提高了精度,而且减少了计算量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CNN Based PPG Signal Quality Assessment Using Raw PPG Signal for Energy-Efficient PPG Analysis Devices in Internet of Medical Things
For the internet of medical things (IoMT) enabled long-term health monitoring and disease prediction applications, there is a demand for an automatic photoplethysmogram (PPG) signal quality assessment (SQA) for reducing false alarms and energy consumption. This paper presents a PPG-SQA method by using raw PPG signal and convolutional neural network (CNN) with optimal parameters. The main focus of this paper is to find an optimal number of filters (16, 32, 64) and number of layers (2 and 4 layers) with rectified linear unit (ReLU) activation function and to study robustness of trained CNN models by using unseen PPG datasets and different kinds of noise sources, which are not addressed in the past studies on the CNN-based PPG-SQA methods. Evaluation results showed that the 4 layer CNN-based method had the higher accuracy of 99.58% for noise-free PPG (NF-PPG) versus wrist-cup noisy PPG signal database (MA-DB01), 99.99% for NF versus random noises added PPG (RN-PPG) signals, and 75.80% for NF-PPG versus acceleration corrupted PPG signals (MA-DB02). For the unknown dataset, the 4-layer CNN model had the higher accuracy of 96.71% for NF-PPG versus MA-DB01 and 99.04% for NF versus RN-PPG, and the 2-layer CNN model had the higher accuracy of 76.16% for NF-PPG versus MA-DB02 PPG segments. Results demonstrate that the CNN-based PPG-SQA method with optimal parameters is not only improve the accuracy but can also reduce the computational load as compared with other existing methods.
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