Photoplethysmography and its application in clinical physiological measurement

The measurement of blood pressure (BP) is critical to the treatment and management of many medical conditions. High blood pressure is associated with many chronic disease conditions, and is a major source of mortality and morbidity around the world. For outpatient care as well as general health monitoring, there is great interest in being able to accurately and frequently measure BP outside of a clinical setting, using mobile or wearable devices. One possible solution is photoplethysmography (PPG), which is most commonly used in pulse oximetry in clinical settings for measuring oxygen saturation. PPG technology is becoming more readily available, inexpensive, convenient, and easily integrated into portable devices. Recent advances include the development of smartphones and wearable devices that collect pulse oximeter signals. In this article, we review (i) the state-of-the-art and the literature related to PPG signals collected by pulse oximeters, (ii) various theoretical approaches that have been adopted in PPG BP measurement studies, and (iii) the potential of PPG measurement devices as a wearable application. Past studies on changes in PPG signals and BP are highlighted, and the correlation between PPG signals and BP are discussed. We also review the combined use of features extracted from PPG and other physiological signals in estimating BP. Although the technology is not yet mature, it is anticipated that in the near future, accurate, continuous BP measurements may be available from mobile and wearable devices given their vast potential.

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The use of photoplethysmography for assessing hypertension

There is an ever-growing demand for measuring respiratory variables during a variety of applications, including monitoring in clinical and occupational settings, and during sporting activities and exercise. Special attention is devoted to the monitoring of respiratory rate because it is a vital sign, which responds to a variety of stressors. There are different methods for measuring respiratory rate, which can be classed as contact-based or contactless. The present paper provides an overview of the currently available contact-based methods for measuring respiratory rate. For these methods, the sensing element (or part of the instrument containing it) is attached to the subject’s body. Methods based upon the recording of respiratory airflow, sounds, air temperature, air humidity, air components, chest wall movements, and modulation of the cardiac activity are presented. Working principles, metrological characteristics, and applications in the respiratory monitoring field are presented to explore potential development and applicability for each method.

Contact-Based Methods for Measuring Respiratory Rate

Respiratory rate (RR) is an important physiological parameter whose abnormality has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to perform, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies.

https://iopscience.iop.org/article/10.1088/1361-6579/ab299e/pdf

Recent development of respiratory rate measurement technologies

Blood pressure estimation from appropriate and inappropriate PPG signals using A whole-based method

This paper proposes a novel wavelet neural network algorithm for the continuous and noninvasive dynamic estimation of blood pressure (BP). Unlike prior algorithms, the proposed algorithm capitalizes on the correlation between photoplethysmography (PPG) and BP. Complete BP waveforms are reconstructed based on PPG signals to extract systolic blood pressure (SBP) and diastolic blood pressure (DBP). To improve the robustness, Daubechies wavelet is implemented as the hidden layer node function for the neural network. An optimized neural network structure is proposed to reduce the computational complexity. Further, this paper investigates an inhomogeneous resilient backpropagation (IRBP) algorithm to calculate the weight of hidden layer nodes. The IRBP improves the convergence speed and reconstruction accuracy. Multiparameter intelligent monitoring in Intensive Care (MIMIC) databases, which contain a variety of physiological parameters captured from patient monitors, are used to validate this algorithm. The standard deviation σ between reconstructed and actual BP signals is 4.4797 mmHg, which satisfies the American National Standards of the Association for the Advancement of Medical Instrumentation. The reconstructed BP waveform can be used to extract the SBP and DBP, whose standard deviations σ are 2.91 mmHg and 2.41 mmHg respectively.

Novel wavelet neural network algorithm for continuous and noninvasive dynamic estimation of blood pressure from photoplethysmography

This paper proposes a low power and efficient QRS processor for real-time and continuous mobile ECG monitoring. The QRS detector contains the wavelet transform (WT), the modulus maxima pair identification (MMPI), and the R position modification (RPM). In order to reduce power consumption, we choose the Haar function as the mother wavelet of WT. It is implemented by an optimized FIR filter structure where none of the multiplier is used. The MMPI processes the wavelet coefficients at scale 24 and provides candidate R peak positions for the RPM. To improve the accuracy and robust performance, a number of modules have been designed in MMPI, including the preprocessing unit, the automatic threshold updating, and the decision state machine. The RPM is designed to eliminate digital time delay in wavelet transform and locate the R peak position precisely. Raw ECG signals and QRS detection results are output simultaneously. Fabricated in 0.18-μm N-well CMOS 1P6M technology, the power consumption of this chip is only about 410 nW in 1 V voltage supply. Validated by all 48 sets of data in the MIT-BIH arrhythmia database, the sensitive and the positive prediction are 99.60% and 99.77% respectively.

A 410-nW Efficient QRS Processor for Mobile ECG Monitoring in 0.18-μm CMOS

One of the most important applications of photoplethysmography (PPG) signal is heartrate (HR) estimation. For its applications in wearable devices, motion artifact (MA) may be the most serious challenge for randomness both in format and temporal distribution. This paper proposes an advanced time-frequency analysis framework based on empirical mode decomposition (EMD) to select specific time slices for signal reconstruction. This framework operates with a type of pre-processing called variance characterization series (VCS), EMD, singular value decomposition (SVD), and a precise and adaptive 2-D filtration reported first. This filtration is based on Harr wavelet transform (HWT) and 3rd order cumulant analysis, to make it have resolution in both the time domain and different components. The simulation results show that the proposed method gains 1.07 in absolute average error (AAE) and 1.87 in standard deviation (SD); AAEs 1st and 3rd quartiles are 0.12 and 1.41, respectively. This framework is tested by the PhysioBank MIMIC II waveform database.

A novel approach framework based on statistics for reconstruction and heartrate estimation from PPG with heavy motion artifacts

In a gradually aging society, families and hospitals have a growing demand for reliable and unobtrusive physiological signal monitoring for elderly people. However, the existing respiration rate monitoring methods and algorithms are still unsatisfactory. In this work, we introduce a physiological signal acquisition patch which integrates 3-axis accelerator and 3-axis gyroscope to estimate respiration rate, as well as ECG(electrocardiogram) sensor and surface temperature sensor. A complete set of respiration rate estimation algorithms is embedded in our patch, which can be used to identify whether the patch is worn or not, and to recognize, segment, de-noise and reconstruct the respiration signal. In-situ experiments have been conducted to prove the validity of the algorithms described in this paper and the possibilities of estimating respiration rate using a physiological signal acquisition patch. The mean absolute error (MAE) is 0.11(about ±0.7 times in a minute), which is the least among similar studies that acquire respiratory rate from 3-axis accelerators or electrocardiogram.

A new physiological signal acquisition patch designed with advanced respiration monitoring algorithm based on 3-axis accelerator and gyroscope

This paper proposes a low power and efficient QRS processor for real-time and continuous mobile ECG monitoring. The QRS detection algorithm is based on the harr wavelet transform. In order to reduce power consumption, an optimized FIR filter structure is proposed. To improve accuracy, R position modification (RPM) has been designed. Fabricated with the 0.18-μm N-well CMOS 1P6M technology, power consumption of this chip is only 410 nW in 1 V voltage supply, which is much lower than that of previous work. Validated by all 48 databases in the MIT-BIH arrhythmia database, sensitivity (Se) and positive prediction (Pr) are 99.60% and 99.77% respectively.

Bo Pang

Papers

Novel wavelet neural network algorithm for continuous and noninvasive dynamic estimation of blood pressure from photoplethysmography

A 410-nW Efficient QRS Processor for Mobile ECG Monitoring in 0.18-μm CMOS

A novel approach framework based on statistics for reconstruction and heartrate estimation from PPG with heavy motion artifacts

A new physiological signal acquisition patch designed with advanced respiration monitoring algorithm based on 3-axis accelerator and gyroscope

A 410-nW efficient QRS processor for mobile ECG monitoring in 0.18-μm CMOS