Noninvasive hemoglobin measurement based on optimizing Dynamic Spectrum method

Li, Gang; Xu, Sijia; Zhou, Mei

doi:10.1080/00387010.2017.1302481

Cited by 16 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al proposed a novel method with dynamic spectrum, using a spectrograph with a computer to scan the transmission spectrum of fingertip (Li et al, 2017 ). An average prediction correlation coefficient (R) of 0.8399 is achieved in their experiment.…”

Section: Related Workmentioning

confidence: 99%

Anemia detection through non-invasive analysis of lip mucosa images

Mahmud,

Donmez,

Mansour

et al. 2023

Front. Big Data

View full text Add to dashboard Cite

This paper aims to detect anemia using images of the lip mucosa, where the skin tissue is thin, and to confirm the feasibility of detecting anemia noninvasively and in the home environment using machine learning (ML). Data were collected from 138 patients, including 100 women and 38 men. Six ML algorithms: artificial neural network (ANN), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), naive bayes (NB), and support vector machine (SVM) which are widely used in medical applications, were used to classify the collected data. Two different data types were obtained from participants' images (RGB red color values and HSV saturation values) as features, with age, sex, and hemoglobin levels utilized to perform classification. The ML algorithm was used to analyze and classify images of the lip mucosa quickly and accurately, potentially increasing the efficiency of anemia screening programs. The accuracy, precision, recall, and F-measure were evaluated to assess how well ML models performed in predicting anemia. The results showed that NB reported the highest accuracy (96%) among the other ML models used. DT, KNN and ANN reported an accuracies of (93%), while LR and SVM had an accuracy of (79%) and (75%) receptively. This research suggests that employing ML approaches to identify anemia will help classify the diagnosis, which will then help to create efficient preventive measures. Compared to blood tests, this noninvasive procedure is more practical and accessible to patients. Furthermore, ML algorithms may be created and trained to assess lip mucosa photos at a minimal cost, making it an affordable screening method in regions with a shortage of healthcare resources.

show abstract

Section: Related Workmentioning

confidence: 99%

Anemia detection through non-invasive analysis of lip mucosa images

Mahmud,

Donmez,

Mansour

et al. 2023

Front. Big Data

View full text Add to dashboard Cite

show abstract

“…The PPG signal captured under an NIR LED light source should be used for further processing. Since the low SNR may reduce the possibility of better PPG generation, selection, and feature extraction, we recommend utilization of laser diodes as a light source and developing a PPG generation algorithm using the dynamic spectrum method [89], ratio of the superimposition averaging template and pulse wave [130], optimized differential extraction method [131], and spectral difference coefficient and dynamic spectrum [132].…”

Section: Response Calculationmentioning

confidence: 99%

Noninvasive Hemoglobin Level Prediction in a Mobile Phone Environment: State of the Art Review and Recommendations

Hasan¹,

Aziz²,

Zarif³

et al. 2021

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

Background There is worldwide demand for an affordable hemoglobin measurement solution, which is a particularly urgent need in developing countries. The smartphone, which is the most penetrated device in both rich and resource-constrained areas, would be a suitable choice to build this solution. Consideration of a smartphone-based hemoglobin measurement tool is compelling because of the possibilities for an affordable, portable, and reliable point-of-care tool by leveraging the camera capacity, computing power, and lighting sources of the smartphone. However, several smartphone-based hemoglobin measurement techniques have encountered significant challenges with respect to data collection methods, sensor selection, signal analysis processes, and machine-learning algorithms. Therefore, a comprehensive analysis of invasive, minimally invasive, and noninvasive methods is required to recommend a hemoglobin measurement process using a smartphone device. Objective In this study, we analyzed existing invasive, minimally invasive, and noninvasive approaches for blood hemoglobin level measurement with the goal of recommending data collection techniques, signal extraction processes, feature calculation strategies, theoretical foundation, and machine-learning algorithms for developing a noninvasive hemoglobin level estimation point-of-care tool using a smartphone. Methods We explored research papers related to invasive, minimally invasive, and noninvasive hemoglobin level measurement processes. We investigated the challenges and opportunities of each technique. We compared the variation in data collection sites, biosignal processing techniques, theoretical foundations, photoplethysmogram (PPG) signal and features extraction process, machine-learning algorithms, and prediction models to calculate hemoglobin levels. This analysis was then used to recommend realistic approaches to build a smartphone-based point-of-care tool for hemoglobin measurement in a noninvasive manner. Results The fingertip area is one of the best data collection sites from the body, followed by the lower eye conjunctival area. Near-infrared (NIR) light-emitting diode (LED) light with wavelengths of 850 nm, 940 nm, and 1070 nm were identified as potential light sources to receive a hemoglobin response from living tissue. PPG signals from fingertip videos, captured under various light sources, can provide critical physiological clues. The features of PPG signals captured under 1070 nm and 850 nm NIR LED are considered to be the best signal combinations following a dual-wavelength theoretical foundation. For error metrics presentation, we recommend the mean absolute percentage error, mean squared error, correlation coefficient, and Bland-Altman plot. Conclusions We addressed the challenges of developing an affordable, portable, and reliable point-of-care tool for hemoglobin measurement using a smartphone. Leveraging the smartphone’s camera capacity, computing power, and lighting sources, we define specific recommendations for practical point-of-care solution development. We further provide recommendations to resolve several long-standing research questions, including how to capture a signal using a smartphone camera, select the best body site for signal collection, and overcome noise issues in the smartphone-captured signal. We also describe the process of extracting a signal’s features after capturing the signal based on fundamental theory. The list of machine-learning algorithms provided will be useful for processing PPG features. These recommendations should be valuable for future investigators seeking to build a reliable and affordable hemoglobin prediction model using a smartphone.

show abstract

“…During the development of dynamic spectroscopy, several dynamic spectral extraction methods have been proposed, such as frequency domain extraction [25] , single-edge extraction [26] , and differential extraction [27] . Basing this method has remarkable results in noninvasive blood component detection, such as red blood cells [28] , hemoglobin [29] , globulin [30] , cholesterol [31] , blood glucose [32] , and so on. Li et al [33] applied this idea to extract the DS from the frequency domain of the PPG signal to calculate SpO 2 .…”

Section: Introductionmentioning

confidence: 99%