Photoplethysmography and its application in clinical physiological measurement

Cardiovascular diseases (CVDs) are a leading cause of death worldwide. For early diagnosis, intervention and management of CVDs, it is highly desirable to frequently monitor blood pressure (BP), a vital sign closely related to CVDs, during people's daily life, including sleep time. Towards this end, wearable and cuffless BP extraction methods have been extensively researched in recent years as part of the mobile healthcare initiative. This review focuses on the enabling technologies for wearable and cuffless BP monitoring platforms, covering both the emerging flexible sensor designs and BP extraction algorithms. Based on the signal type, the sensing devices are classified into electrical, optical, and mechanical sensors, and the state-of-the-art material choices, fabrication methods, and performances of each type of sensor are briefly reviewed. In the model part of the review, contemporary algorithmic BP estimation methods for beat-to-beat BP measurements and continuous BP waveform extraction are introduced. Mainstream approaches, such as pulse transit time-based analytical models and machine learning methods, are compared in terms of their input modalities, features, implementation algorithms, and performances. The review sheds light on the interdisciplinary research opportunities to combine the latest innovations in the sensor and signal processing research fields to achieve a new generation of cuffless BP measurement devices with improved wearability, reliability, and accuracy. 

/pdf/emerging-sensing-and-modeling-technologies-for-wearable-and-3v2amilm.pdf

Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring

This paper presents a bibliographical survey of recently-published research on different techniques to extract feature from photoplethysmogram (PPG). These techniques and approaches have been implemented for better accuracy in detecting diseases. Moreover, several aspects in analyzing PPG waveform are discussed on the techniques in feature extraction, parameters involved and performance comparisons. This review will serve as a comparative study and reference for researches working on PPG waveform in health care applications.

/pdf/methods-of-extracting-feature-from-photoplethysmogram-7aze2mqyx7.pdf

Methods of Extracting Feature from Photoplethysmogram Waveform for Non-Invasive Diagnostic Applications

In this paper, a microfluidic sensor is proposed using a microwave planar multiple split-ring resonator (MSRR). The main objective of this real time non-destructive microfluidic sensor is to detect and analyse the dielectric properties of materials using extremely small amount of liquid volumes $(2.5\mu \mathrm {L})$. The sensing mechanism of the proposed microfluidic sensor is based on frequency shifting and amplitude changing of material under test. An MSRR is employed using a Rogers RT/Duroid 5880 substrate. Then, a microcapillary made from glass material is loaded on the outer ring slot to observe the perturbation response. The microcapillary is filled with several common solvents and the ideal value of those solvents are used as a reference result. The resonant frequency of the structure with microcapillary is observed to be 2.1 GHz. The functionality and reliability of the proposed sensor are successfully demonstrated through full-wave simulations and measurements. The resonant frequency of the loaded microcapillary showed a shift of 125.5 MHz and 25 MHz when compared to empty microfluidic. Our proposed microfluidic sensor is nondestructive and reliable. Furthermore, it is low cost and fabricated using simple and easy design procedures.

Complex Permittivity Measurement Based on Planar Microfluidic Resonator Sensor

A new planar sensor based on the matryoshka microstrip resonator is presented in this paper. The matryoshka resonator is described and its properties discussed, including initial project equations to calculate the first resonant frequencies. The sensor was fabricated and successfully applied to distinguish three different liquids (water, alcohol 46° INPM and acetone). Furthermore, the proposed sensor is employed to identify different concentrations of alcohol. The achieved results make the new planar sensor potentially attractive to many applications, motivating the investigation of its properties.

A new planar sensor based on the matryoshka microstrip resonator

Globally, chronic kidney disease (CKD) burden healthcare systems. Current treatment systems in many countries have presented a serious socioeconomic situation for low- to middle-income countries. As such, monitoring mechanisms present a clinical need for effective diagnosis, prevention and treatment of numerous non communicable diseases, such as CKD. Non-invasive, continuous monitoring of renal function using a complementary split ring resonator (CSRR) microwave sensor confers potential benefits to meeting this clinical need. The sensor was sensitive to changes in the relative permittivity of a material under test with the electrical properties of blood, for the frequency range investigated in COMSOL Multiphysics®. The correlation of the electrical parameters with relative permittivity changes highlights the applicability of CSRRs as a sensor module for CKD monitoring.

CSRR-based microwave sensor for noninvasive, continuous monitoring of renal function

Heart disease contributes significantly to annual deaths. Personal health monitoring systems can be useful in reducing mortality rates as well as for treatment and prevention of associated complications. A current challenge is the limited accessibility to devices which offer advanced feature extraction for cardiovascular diagnosis. In this work, investigations are presented using non-invasive and continuous monitoring using photoplethysmography (PPG), a simple, low-cost, popular choice for wearable devices, from which features other than pulse rate and blood pressure can be extracted from acquired PPG signals. The investigations outlined in this paper advance work in automated feature extraction for cardiovascular diagnosis, demonstrated through identification of the 'a', 'b' and 'e' waves derived from the second derivative of the PPG waveform, followed by calculation of indices associated with patient arterial stiffness using these waves. Results suggest that the implemented approach is a useful tool in assessing physiological features which could be utilized to determine cardiovascular conditions. The approach is important in providing insights into the emerging design space for cardiovascular monitoring and feature extraction as part of personalized healthcare systems.

Beyond Blood Pressure and Heart Rate Monitoring: Towards a Device for Continuous Sensing and Automatic Feature Extraction of Cardiovascular Data

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CSRR-based microwave sensor for noninvasive, continuous monitoring of renal function

Beyond Blood Pressure and Heart Rate Monitoring: Towards a Device for Continuous Sensing and Automatic Feature Extraction of Cardiovascular Data