Photoplethysmography (PPG) technology has been used to develop small, wearable, pulse rate sensors. These devices, consisting of infrared light-emitting diodes (LEDs) and photodetectors, offer a simple, reliable, low-cost means of monitoring the pulse rate noninvasively. Recent advances in optical technology have facilitated the use of high-intensity green LEDs for PPG, increasing the adoption of this measurement technique. In this review, we briefly present the history of PPG and recent developments in wearable pulse rate sensors with green LEDs. The application of wearable pulse rate monitors is discussed.

Wearable Photoplethysmographic Sensors—Past and Present

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an aging population. This, in turn, places an ever-increasing burden on healthcare due to the increasing prevalence of patients with chronic illnesses and the reducing income-generating population base needed to sustain them. The need to urgently address this healthcare “time bomb” has accelerated the growth in ubiquitous, pervasive, distributed healthcare technologies. The current move from hospital-centric healthcare toward in-home health assessment is aimed at alleviating the burden on healthcare professionals, the health care system and caregivers. This shift will also further increase the comfort for the patient. Advances in signal acquisition, data storage and communication provide for the collection of reliable and useful in-home physiological data. Artifacts, arising from environmental, experimental and physiological factors, degrade signal quality and render the affected part of the signal useless. The magnitude and frequency of these artifacts significantly increases when data collection is moved from the clinic into the home. Signal processing advances have brought about significant improvement in artifact removal over the past few years. This paper reviews the physiological signals most likely to be recorded in the home, documenting the artifacts which occur most frequently and which have the largest degrading effect. A detailed analysis of current artifact removal techniques will then be presented. An evaluation of the advantages and disadvantages of each of the proposed artifact detection and removal techniques, with particular application to the personal healthcare domain, is provided.

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Artifact Removal in Physiological Signals—Practices and Possibilities

Accurate estimation of heart rates from photoplethysmogram (PPG) signals during intense physical activity is a very challenging problem. This is because strenuous and high intensity exercise can result in severe motion artifacts in PPG signals, making accurate heart rate (HR) estimation difficult. In this study we investigated a novel technique to accurately reconstruct motion-corrupted PPG signals and HR based on time-varying spectral analysis. The algorithm is called Spectral filter algorithm for Motion Artifacts and heart rate reconstruction (SpaMA). The idea is to calculate the power spectral density of both PPG and accelerometer signals for each time shift of a windowed data segment. By comparing time-varying spectra of PPG and accelerometer data, those frequency peaks resulting from motion artifacts can be distinguished from the PPG spectrum. The SpaMA approach was applied to three different datasets and four types of activities: (1) training datasets from the 2015 IEEE Signal Process. Cup Database recorded from 12 subjects while performing treadmill exercise from 1 km/h to 15 km/h; (2) test datasets from the 2015 IEEE Signal Process. Cup Database recorded from 11 subjects while performing forearm and upper arm exercise. (3) Chon Lab dataset including 10 min recordings from 10 subjects during treadmill exercise. The ECG signals from all three datasets provided the reference HRs which were used to determine the accuracy of our SpaMA algorithm. The performance of the SpaMA approach was calculated by computing the mean absolute error between the estimated HR from the PPG and the reference HR from the ECG. The average estimation errors using our method on the first, second and third datasets are 0.89, 1.93 and 1.38 beats/min respectively, while the overall error on all 33 subjects is 1.86 beats/min and the performance on only treadmill experiment datasets (22 subjects) is 1.11 beats/min. Moreover, it was found that dynamics of heart rate variability can be accurately captured using the algorithm where the mean Pearson’s correlation coefficient between the power spectral densities of the reference and the reconstructed heart rate time series was found to be 0.98. These results show that the SpaMA method has a potential for PPG-based HR monitoring in wearable devices for fitness tracking and health monitoring during intense physical activities.

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A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor

A photoplethysmography (PPG) signal provides very useful information about a subject's hemodynamic status in a hospital or ubiquitous environment. However, PPG is very vulnerable to motion artifacts, which can significantly distort the information belonging to the PPG signal itself. Thus, the reduction of the effects of motion artifacts is an important issue when monitoring the cardiovascular system by PPG. There have been many adaptive techniques to reduce motion artifacts from PPG signals. In the present study, we compared a method based on the fixed-interval Kalman smoother with the usual adaptive filtering algorithms, e.g. the normalized least mean squares, recursive least squares and the conventional Kalman filter. We found that the fixed-interval Kalman smoother reduced motion artifacts from the PPG signal most effectively. Therefore, the use of the fixed-interval Kalman smoother can reduce motion artifacts in PPG, thus providing the most reliable information that can be deduced from the reconstructed PPG signals.

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Improved elimination of motion artifacts from a photoplethysmographic signal using a Kalman smoother with simultaneous accelerometry

Pulse rates obtained from wearable photoplethysmography (PPG) sensors are important for monitoring cardiovascular condition, especially during exercise. However, it is difficult to precisely count pulse rates during exercise because PPG is sensitive to body movement. The artifacts from body movement are caused by a change in the blood volume at the measurement site, in addition to pulsatile changes. Here, we investigated the influence of motion artifact with respect to light source and anatomical sites. In this study, we compared the signal from green-light PPG to that from infrared PPG at different anatomical sites. In these experiments, 11 subjects were asked to either assume a resting position or generate spontaneous motion artifact by jumping and swinging their arm. As a result, pulse rates obtained from green-light PPG showed a higher correlation with the ECG R-R interval as compared to those obtained with infrared. Additionally, the signal from the upper arm showed less artifact than did the peripheral one. Therefore, the green-light PPG may be useful for pulse rate monitoring.

Relationship Between Measurement Site and Motion Artifacts in Wearable Reflected Photoplethysmography

Artifact free photoplethysmographic (PPG) signals are necessary for non-invasive estimation of oxygen saturation (SpO2) in arterial blood. Movement of a patient corrupts the PPGs with motion artifacts, resulting in large errors in the computation of Sp02. This paper presents a study on using Kalman Filter in an innovative way by modeling both the Artillery Blood Pressure (ABP) and the unwanted signal, additive motion artifact, to reduce motion artifacts from corrupted PPG signals. Simulation results show acceptable performance regarding LMS and variable step LMS, thus establishing the efficacy of the proposed method. Keywords—Kalman filter, Motion artifact, PPG, Photoplethysmography.

Kalman Filter Based Adaptive Reduction of Motion Artifact from Photoplethysmographic Signal

New flat phase margin fractional order PID design: Perturbed UAV roll control study

Multi-machine optimal power system stabilizers design based on system stability and nonlinearity indices using Hyper-Spherical Search method

An incipient short-circuit fault does not cause a severe condition in the steady-state operation of transformers, but in transient conditions - the lightning or switching - it can result in an extended turn-to-turn short-circuit fault (TSCF) and eventually in transformer outage. This study aims to determine and localise small TSCFs using the frequency response analysis. For this purpose, an appropriate mathematical model is developed, which can accurately simulate the winding transient behaviour. Using this model, turn-to-turn short-circuit patterns are extracted for different turn-to-turn faults in the winding; then, the fault location is determined by comparing the measured faulty signal with mathematical short-circuit patterns. In this study, a heuristic index is proposed to identify small TSCF locations. The presented solutions are implemented on a real winding with a nominal rating of 1600 kVA and a nominal voltage of 20/0.4 kV.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-smt.2017.0125

Determination and localisation of turn-to-turn fault in transformer winding using frequency response analysis

Nowadays, power supply has become a business asset. The quality and reliability of power system needs to be maintained in order to obtain optimum performance. Therefore, it is extremely important that transmission line faults from various sources to be identified accurately, reliably and be corrected as soon as possible. In this paper, a new technique is discussed by which avoiding noise in fault detection in high voltage transmission lines is achieved. Later, a comparative study of the performance of Fourier transform and wavelet transform based methods combined with protective relaying pattern classifier algorithm Neural Network for classification of faults is presented. A new classification method is proposed for decreasing training time and dimensions of NN. The proposed algorithms are based on Fourier transform analysis of fundamental frequency of current signals in the event of a short circuit. Similar analysis is performed on transient current signals using multi-resolution Haar wavelet transform, and comparative characteristics of the two methods are discussed.

Saeed Seyedtabaii

Papers

Kalman Filter Based Adaptive Reduction of Motion Artifact from Photoplethysmographic Signal

New flat phase margin fractional order PID design: Perturbed UAV roll control study

Multi-machine optimal power system stabilizers design based on system stability and nonlinearity indices using Hyper-Spherical Search method

Determination and localisation of turn-to-turn fault in transformer winding using frequency response analysis

Comparison of fourier & wavelet transform methods for transmission line fault classification