A Smartphone-Based Biosensor for Non-Invasive Monitoring of Total Hemoglobin Concentration in Humans with High Accuracy

Fan, Zhipeng; Zhou, Yong; Zhai, Haoyu; Wang, Qi; He, Honghui

doi:10.3390/bios12100781

Cited by 8 publications

(4 citation statements)

References 38 publications

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“…Hence, smartphone based mobile health methods are gaining a lot of attention form researchers around the globe. Researchers are increasingly harnessing the power of smartphones, often coupled with specialized hardware components, to advance noninvasive methods for hemoglobin estimation [10], [12], [44].…”

Section: Literature Reviewmentioning

confidence: 99%

Fingertip Video Dataset for Non-Invasive Diagnosis of Anemia Using ResNet-18 Classifier

Sabir,

Khan,

Ishaq

et al. 2024

IEEE Access

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Hemoglobin is the iron containing protein in red blood cells which carries oxygen from lungs to rest of the body tissues.Accurate measurement of hemoglobin is essential for diagnosing anemia, a condition characterized by a deficiency of red blood cells. This measurement is particularly vital before initiating blood transfusions for thalassemia patients.Non-invasive estimation of hemoglobin levels can be achieved through photoplethysmography (PPG)-based methods.PPG is an optical method to measure blood volume changes in successive heart beats. PPG signals can be obtained from fingertip videos using a light source and a photodetector. SmartphonePPG utilizes a smartphone's flashlight as a light source and its camera as a photodetector to acquire PPG signals, offering an affordable and portable point-ofcare tool. Despite the ubiquity of smartphones, signals from their cameras often contain noise, making feature selection from PPG characteristics challenging. While PPG-based methods are invaluable, the lack of real-world datasets poses a significant challenge in maximizing the benefits of PPG technology. In this paper, we introduce a dataset comprising 1-minute fingertip video recordings from 150 anemic patients, obtained using a smartphone's camera. The dataset, publicly accessible for research purposes a , covers an age range of 6 months to 32 years, with diverse hemoglobin values (4.3 gm/dL -12.4 gm/dL).Utilizing this dataset, we propose a deep learning-based technique employing the ResNet-18 architecture to estimate hemoglobin levels. This approach eliminates the need for manual feature extraction and selection from PPG signals, overcoming a limitation in existing smartphonePPG-based hemoglobin estimation systems. Our model achieves a hemoglobin level estimation with an RMSE of 0.81-1.39 when compared with the gold standard laboratory method, Complete Blood Count (CBC) test reports.In contrast, HemaApp, a state-ofthe-art research utilizing a machine learning-based classifier (SVM), yields an RMSE of 1.7 on our dataset. The accuracy and simplicity of our model position it as a promising alternative to existing non-invasive hemoglobin level estimation methods. a https://forms.gle/LB4qn81ZMqEuy3V27

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Section: Literature Reviewmentioning

confidence: 99%

Fingertip Video Dataset for Non-Invasive Diagnosis of Anemia Using ResNet-18 Classifier

Sabir,

Khan,

Ishaq

et al. 2024

IEEE Access

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“…Heart-rate sensors, blood-glucose monitors, endoscopic capsules, and other devices make up the Internet of Medical Things (IoMT), which together, create the IoMT diabetic-based WBSN monitoring system [ 58 , 59 ].…”

Section: Internet Of Wearable Thingsmentioning

confidence: 99%

IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review

et al. 2022

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The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoT applications are particularly beneficial for providing healthcare because they enable secure and real-time remote patient monitoring to improve the quality of people’s lives. This review paper explores the latest trends in healthcare-monitoring systems by implementing the role of the IoT. The work discusses the benefits of IoT-based healthcare systems with regard to their significance, and the benefits of IoT healthcare. We provide a systematic review on recent studies of IoT-based healthcare-monitoring systems through literature review. The literature review compares various systems’ effectiveness, efficiency, data protection, privacy, security, and monitoring. The paper also explores wireless- and wearable-sensor-based IoT monitoring systems and provides a classification of healthcare-monitoring sensors. We also elaborate, in detail, on the challenges and open issues regarding healthcare security and privacy, and QoS. Finally, suggestions and recommendations for IoT healthcare applications are laid down at the end of the study along with future directions related to various recent technology trends.

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“…Hasan et al [ 4 ] determined the region of interest in the smartphone video and randomly input the extracted features into the artificial neural network (ANN) to estimate Hb concentration. Fan et al [ 5 ] proposed a smartphone-based biosensor to predict Hb concentration using multiple linear regression, showing that the “a” parameter has better performance than the “R” parameter for predicting Hb concentration in RGB color space. Dimauro et al [ 6 ] published a novel public Eyes-defy-anemia dataset and developed a decision-making system based on the RUSBoost classifier to support the automatic diagnosis of anemia.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Extreme Learning Machine Method for Hemoglobin Estimation Based on PhotoPlethysmoGraphic Signals

Peng,

Zhang,

Chen

et al. 2024

Sensors

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Non-invasive detection of hemoglobin (Hb) concentration is of great clinical value for health screening and intraoperative blood transfusion. However, the accuracy and stability of non-invasive detection still need to be improved to meet clinical requirement. This paper proposes a non-invasive Hb detection method using ensemble extreme learning machine (EELM) regression based on eight-wavelength PhotoPlethysmoGraphic (PPG) signals. Firstly, a mathematical model for non-invasive Hb detection based on the Beer-Lambert law is established. Secondly, the captured eight-channel PPG signals are denoised and fifty-six feature values are extracted according to the derived mathematical model. Thirdly, a recursive feature elimination (RFE) algorithm is used to select the features that contribute most to the Hb prediction. Finally, a regression model is built by integrating several independent ELM models to improve prediction stability and accuracy. Experiments conducted on 249 clinical data points (199 cases as the training dataset and 50 cases as the test dataset) evaluate the proposed method, achieving a root mean square error (RMSE) of 1.72 g/dL and a Pearson correlation coefficient (PCC) of 0.76 (p < 0.01) between predicted and reference values. The results demonstrate that the proposed non-invasive Hb detection method exhibits a strong correlation with traditional invasive methods, suggesting its potential for non-invasive detection of Hb concentration.

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A Smartphone-Based Biosensor for Non-Invasive Monitoring of Total Hemoglobin Concentration in Humans with High Accuracy

Cited by 8 publications

References 38 publications

Fingertip Video Dataset for Non-Invasive Diagnosis of Anemia Using ResNet-18 Classifier

Fingertip Video Dataset for Non-Invasive Diagnosis of Anemia Using ResNet-18 Classifier

IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review

Ensemble Extreme Learning Machine Method for Hemoglobin Estimation Based on PhotoPlethysmoGraphic Signals

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