光电容积脉搏波( Photoplethysmogram,PPG )传感是一个涉及精确传感器设计和高效信号处理的信号测量领域。由于采用了先进的纳米技术,使得传感界面变得成熟,能够实现高速、低误差的采样。因此,为了提高PPG传感的效率,必须对信号处理单元进行调整。研究者们提出了各种各样的算法,使用不同的分类模型对信号进行调理和减少误差。当应用于血压( BP )监测时,这些模型的效率受限于它们区分BP水平的能力。为了提高这种效率,底层文本提出了一种新的多模态集成分类器。提出的分类器从一系列高效的分类器中积累正确的分类实例,以增强PPG感知的效率。该效率与k近邻( kNN )、随机森林( RF )、线性支持向量机( LSVM )、多层感知器( MLP )、逻辑回归( LR )等标准分类模型进行了比较。观察到,本文提出的模型在分类精度方面比这些模型效率高10 %；因此,可以用于实时BP监测PPG信号的采集场景。这种精度是通过将实际BP值与实测BP值进行比较来估计的,然后评估误差差w . r . t .其他算法。

Photoplethysmogram (PPG) sensing is a field of signal measurement that involves accurate sensor design and efficient signal processing. Sensing interfaces have matured due to use of sophisticated nano-meter technologies, that allow for high speed, and low error sampling. Thus, in order to improve the efficiency of PPG sensing, the signal processing unit must be tweaked. A wide variety of algorithms have been proposed by researchers that use different classification models for signal conditioning and error reduction. When applied to blood pressure (BP) monitoring, the efficiency of these models is limited by their ability to differentiate between BP levels. In order to improve this efficiency, the underlying text proposes a novel multimodal ensemble classifier. The proposed classifier accumulates correct classification instances from a series of highly efficient classifiers in order to enhance the efficiency of PPG sensing. This efficiency is compared with standard classification models like k-nearest neighbors (kNN), random forest (RF), linear support vector machine (LSVM), multilayer perceptron (MLP), and logistic regression (LR). It is observed that the proposed model is 10% efficient than these models in terms of classification accuracy; and thus, can be used for real time BP monitoring PPG signal acquisition scenarios. This accuracy is estimated by comparing actual BP values with measured BP values, and then evaluating error difference w.r.t. other algorithms.