Classification of anemic condition based on photoplethysmography signals and clinical dataset.

Neven Saleh, Ahmed M Salaheldin, Yasser Ismail, Heba M Afify
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Abstract

Objectives: One of the worldwide public health issues mostly affecting children and expectant mothers is Anemia. Recently, non-invasive hemoglobin (Hb) measurements, such as machine learning (ML) algorithms, can diagnose Anemia more quickly and efficiently.

Methods: To diagnose Anemia using photoplethysmography (PPG), two tracks are investigated in this paper, based on clinical data and PPG signals. We use state-of-the-art data for Hb levels, extracted from PPG signals. This first track's methodology is divided into three stages: the labelling of the data as normal and abnormal; the data pre-processing; and applying ML algorithms based on four given features. We extracted nineteen features for red and infrared measurements in the second track. The second track's methodology is broken down into five stages: labelling of the data; data processing; signal augmentation; feature extraction; and applying ML algorithms. A five-fold cross-validation technique was applied for both tracks.

Results: We succeeded in classifying the anemic condition with 100 % classification accuracy. Our accurate detection of anemic status will promote preventive healthcare.

Conclusions: Ultimately, this proposed ML model in this paper validated the effectiveness of the ML algorithms as non-invasive techniques for identifying Anemia.

基于光容积脉搏波信号和临床数据集的贫血状况分类。
目的:贫血是影响儿童和孕妇最多的世界性公共卫生问题之一。最近,非侵入性血红蛋白(Hb)测量,如机器学习(ML)算法,可以更快速有效地诊断贫血。方法:根据临床资料和光容积脉搏波信号,探讨光容积脉搏波诊断贫血的两种途径。我们使用最先进的Hb水平数据,从PPG信号中提取。第一个轨道的方法分为三个阶段:将数据标记为正常和异常;数据预处理;并应用基于四个给定特征的ML算法。我们在第二轨道中提取了19个特征用于红色和红外测量。第二条轨道的方法分为五个阶段:给数据贴标签;数据处理;信号增强;特征提取;以及应用ML算法。两种轨迹采用五重交叉验证技术。结果:以100% %的准确率成功地对贫血进行了分类。我们对贫血状况的准确检测将促进预防性保健。结论:最终,本文提出的ML模型验证了ML算法作为非侵入性贫血识别技术的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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