Multifunctional Photoplethysmography Sensor Design for Respiratory and Cardiovascular Diagnosis

Uguz, Durmus Umutcan; Venema, Boudewijn; Leonhardt, Steffen; Teichmann, Daniel

doi:10.1007/978-981-10-9038-7_167

Cited by 10 publications

(7 citation statements)

References 17 publications

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“…So there should be enough mechanism to get rid of noises for accurate processing and reliable results. The alarm/alert mechanism should also be validated, such as confirmed by the patient [70]. A critical analysis of systems has been presented in Table 1.…”

Section: Discussionmentioning

confidence: 99%

“…Photoplethysmography (PPG) is a non-invasive technique that has many clinical applications such as to measure changes in blood volume, monitoring of Blood Pressure (BP), cardiac output, oxygen saturation, Heart Rate [69], and many other cardiovascular and heart diseases detection [70]. The technique was firstly reported by Hertzman in 1930 [18] by light absorption capability of blood and reflection of signal components.…”

Section: Photoplethysmography (Ppg) Analysis Using Smartphonementioning

confidence: 99%

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Survey: smartphone-based assessment of cardiovascular diseases using ECG and PPG analysis

Shabaan

Arshid

Yaqub

et al. 2020

BMC Med Inform Decis Mak

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A number of resources, every year, being spent to tackle early detection of cardiac abnormalities which is one of the leading causes of deaths all over the Globe. The challenges for healthcare systems includes early detection, portability and mobility of patients. This paper presents a categorical review of smartphone-based systems that can detect cardiac abnormalities by the analysis of Electrocardiogram (ECG) and Photoplethysmography (PPG) and the limitation and challenges of these system. The ECG based systems can monitor, record and forward signals for analysis and an alarm can be triggered in case of abnormality, however the limitation of smart phone’s processing capabilities, lack of storage and speed of network are major challenges. The systems based on PPG signals are non-invasive and provides mobility and portability. This study aims to critically review the existing systems, their limitation, challenges and possible improvements to serve as a reference for researchers and developers.

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Section: Discussionmentioning

confidence: 99%

Section: Photoplethysmography (Ppg) Analysis Using Smartphonementioning

confidence: 99%

Survey: smartphone-based assessment of cardiovascular diseases using ECG and PPG analysis

Shabaan

Arshid

Yaqub

et al. 2020

BMC Med Inform Decis Mak

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“…A PPG device can identify the finger motion with a motion detector and can work with more than one wavelength. Compared with data captured by an electrocardiogram machine, a PPG device reliably (R 2 =0.93) calculated the heart rate of 170 subjects [99].…”

Section: Sensorsmentioning

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

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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.

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“…Similarly, a multipurpose photoplethysmographic sensor was proposed in [10] to detect multiwavelength photoplethysmographs by penetrating skin at various depths. The proposed device was named "SmartPPG," and it could operate under transmission and reflection modes with the transmission mode being confined to thin body parts-fingers and earlobes.…”

Section: Introductionmentioning

confidence: 99%

Wireless Photoplethysmography Sensor for Continuous Blood Pressure Biosignal Shape Acquisition

2020

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Blood pressure assessment plays a vital role in day-to-day clinical diagnosis procedures as well as personal monitoring. Thus, blood pressure monitoring devices must afford convenience and be easy to use with no side effects on the user. This paper presents a compact, economical, power-efficient, and convenient wireless plethysmography sensor for real-time blood pressure biosignal monitoring. The proposed sensor facilitates blood pressure signal shape sensing, signal conditioning, and data conversion as well as its wireless transmission to a monitoring terminal. Received data can, subsequently, be compiled and stored on a computer via a Wi-Fi module. During monitoring, users can observe blood pressure signals being processed and displayed on the graphical user interface (GUI)—developed using a virtual instrumentation (VI) application. The proposed device comprises a finger clip optical pulse sensor, analogue signal preprocessing, microcontroller, and Wi-Fi module. It consumes approximately 500 mW power when operating in the active mode and synthesized using commercial off-the-shelf (COTS) components. Experimental results reveal that the proposed device is reliable and facilitates efficient blood pressure monitoring. The proposed wireless photoplethysmographic (PPG) sensor is a preliminary (or first) version of the intended device manifestation. It provides raw blood pressure data for further classification. Additionally, the collected data concerning the blood pressure wave shape can be easily analysed for use in other biosignal observations, interpretations, and investigations. The design approach also allows the device to be built into a wearable system for further research purposes.

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Multifunctional Photoplethysmography Sensor Design for Respiratory and Cardiovascular Diagnosis

Cited by 10 publications

References 17 publications

Survey: smartphone-based assessment of cardiovascular diseases using ECG and PPG analysis

Survey: smartphone-based assessment of cardiovascular diseases using ECG and PPG analysis

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

Wireless Photoplethysmography Sensor for Continuous Blood Pressure Biosignal Shape Acquisition

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