A Novel Noninvasive Hemoglobin Sensing Device for Anemia Screening

Kumar, Ratendra; Sogunuru, Guru Prasad; Kansara, Krity; Rao, K. Narasimha; Mohan, Murali; Reddy, Manjunath Ramakrishna; Prabhu, Uday Haleangadi; Prakash, Patil; Chakraborty, Sushovan; Das, Sreetama

doi:10.1109/jsen.2021.3070971

Cited by 25 publications

(5 citation statements)

References 29 publications

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“…Investigating the limitations inherent in conventional finger clips, as elucidated by Dinesh Kumar et al [ 22 ], revealed that individual variations in finger dimensions and the presence of tremors during detection can induce alterations in a finger clip’s opening and closing angles. Such deviations subsequently impact the area of light captured by the photodiode, causing fluctuations in received light intensity.…”

Section: System Compositionmentioning

confidence: 99%

A Non-Invasive Hemoglobin Detection Device Based on Multispectral Photoplethysmography

Zhu,

Sun,

Liu

et al. 2023

Biosensors

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The measurement of hemoglobin is a vital index for diagnosing and monitoring diseases in clinical practice. At present, solutions need to be found for the soreness, high risk of infection, and inconvenient operation associated with invasive detection methods. This paper proposes a method for non-invasively detecting hemoglobin levels based on multi-wavelength photoplethysmography (PPG) signals. AFE4490 and TMUX1109 were used to implement the low-cost collection of an eight-LED transmissive PPG signal. We used seven regular LEDs and one broadband LED (Osram SFH4737) as light sources. Additionally, a finger clip integrating multiple sensors was designed and manufactured via 3D printing to simultaneously monitor the LED–sensor distance and the pressure from the tester’s finger during PPG signal acquisition. We used a method to extract features from PPG signals using a sliding-window’s variance and an evaluation metric for PPG signals based on the AdaCost classification. Data were gathered from 56 participants from the Nephrology department, including 16 anemic patients. Pearson correlation analysis was conducted on the collected data to remove any data with a weak correlation. The advantage of using a broadband LED as a light source was also demonstrated. Several non-invasive hemoglobin regression models were created by applying AdaBoost, BPNN, and Random Forest models. The study’s results indicate that the AdaBoost model produced the best performance, with a mean absolute error (MAE) of 2.67 g/L and a correlation coefficient (R2) of 0.91 The study results show that the device we designed and manufactured can achieve effective non-invasive hemoglobin detection and represents a new methodological approach to obtaining measurements that can be applied in a clinical setting.

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Section: System Compositionmentioning

confidence: 99%

A Non-Invasive Hemoglobin Detection Device Based on Multispectral Photoplethysmography

Zhu,

Sun,

Liu

et al. 2023

Biosensors

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“…Very low valued subject's Hb prediction was still a challenging task. By raising the common issues of conventional oximetry probes for different finger geometries and finger sizes, Kumar et al (2021a) presented a mechanical lever-operated fibre-optic-based sensor system that can measure up to 1.6 g dl −1 (Kumar et al 2021a).…”

Section: Optimal Benefits Of Prior Techniques Their Challenges and Re...mentioning

confidence: 99%

Progressive evaluation in spectroscopic sensors for non-invasive blood haemoglobin analysis—a review

Kumar¹,

Dogra²,

Kumar³

2022

Physiol. Meas.

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Frequent monitoring of haemoglobin concentration is highly recommended by physicians to diagnose anaemia and polycythemia Vera. Moreover, Some other conditions also demand assessment of haemoglobin, and these conditions are blood loss, before blood donation, during pregnancy, preoperative, perioperative and postoperative conditions. Cyanmethaemoglobin/haemiglobincyanide method, portable haemoglobinometers and haematology analyzers are few standard methods to diagnose mentioned ailments. However, discomfort, delay and risk of infection are typical limitations of traditional measuring solutions. These limitations create the necessity to develop a non-invasive haemoglobin monitoring technique for a better lifestyle. Various methods and products are already developed and popular due to their non-invasiveness; however, invasive solutions are still considered as the reference standard method. Therefore, this review summarizes the attributes of existing non-invasive solutions. These attributes are finalized as brief details, accuracy, optimal benefits, and research challenges for exploring potential gaps, advancements and possibilities to consider as futuristic alternative methodologies. Non-invasive total haemoglobin assessing techniques are mainly based on optical spectroscopy (reflectance/transmittance) or digital photography or spectroscopic imaging in spot check/continuous monitoring mode. In all these techniques, we have noticed that there is a need to consider different light conditions, motion artefacts, melanocytes, other blood constituents, smoking and precise fixing of the sensor from the sensing spot for exact formulation. Moreover, based on careful and critical analysis of outcomes, none of these techniques or products is used independently or intended to replace invasive laboratory testing. Therefore there is a requirement for a more accurate technique that can eliminate the requirement of blood samples and likely end up as a reference standard method.

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“…Automated Beckman Coulter LH780, Artocell-200 [Haematology Analyzer], Nihon Kohden Celltac and Mek-6420P were used during the process of data collection. Further details of the architecture and hardware and the system design is provided as part of another study [23].…”

Section: Hardwarementioning

confidence: 99%

Non-invasive Estimation of Clinical Severity of Anemia Using Hierarchical Ensemble Classifiers

et al. 2022

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Current techniques of anemia classification are either invasive, expensive or inaccurate, making them illsuited for community health-worker based screening programs. In this study, we propose an Artificial Intelligence (AI) based anemia classification method using a multi-wavelength non-invasive photometry device. A finger mounted photo-plethysmogram (PPG) device was designed to acquire PPG signals at four wavelengths (590, 660, 810, and 940 nm). A set of 13 attenuation and ratio-of-ratio features, derived using the peak and trough information extracted from the PPG signals, were used to develop a three-way hierarchical ensemble classification scheme using a machine-learning algorithm. PPG data from the device and true hemoglobin data from laboratory-based cell counters was collected for 1583 women of childbearing age and subjects were classified into either healthy (Hemoglobin, Hb >11 g/dL), anemic (Hb: 7-11 g/dl) or severely anemic (Hb <7g/dL) categories. We report a classification sensitivity of 92% (p<0.05) and specificity of 84% (p<0.05) in differentiating anemic and non-anemic women. We also report a sensitivity of 76% (p<0.05), and specificity of 74% (p<0.05) in identifying severe anemia. We believe that the proposed anemia classification algorithm, along with the associated sensor has the potential to be productized as a low-cost non-invasive anemia-screening device to rapidly determine next steps in clinical decision making in widespread community interventions.

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A Novel Noninvasive Hemoglobin Sensing Device for Anemia Screening

Cited by 25 publications

References 29 publications

A Non-Invasive Hemoglobin Detection Device Based on Multispectral Photoplethysmography

A Non-Invasive Hemoglobin Detection Device Based on Multispectral Photoplethysmography

Progressive evaluation in spectroscopic sensors for non-invasive blood haemoglobin analysis—a review

Non-invasive Estimation of Clinical Severity of Anemia Using Hierarchical Ensemble Classifiers

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