A Real-Time Algorithm for PPG Signal Processing During Intense Physical Activity (original) (raw)
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Photoplethysmography (PPG) is a non invasive technique of estimating Heart rate (HR) using wrist. However motion artifacts (MA) cause the estimation of HR impractical when subjects perform intensive physical exercise. This study implements a fast algorithm based on statistical approach for extracting HR using MA corrupted PPG. The datasets were derived from subjects who were performing various forearm and upper arm activities. The datasets contain PPG signals with accelerometer data in 3-dimensions. The simultaneously recorded ECG signal was also available and used in verification of the algorithm. The algorithm first denoises the signal, and followed by focus reduction in spectrum and peak tracking. Finally the proposed algorithm computes Average Absolute Error (AAE) in each subject and overall AAE. The overall AAE was 1.5926 BPM. The Bland-Altman is used to compare ECG derived HR and PPG derived HR where Limit of Agreement (LOA) was ±7 BPM. This algorithm gives a great efficiency in time and robustness to track the HR on-the-go.
Journal of the Indian Institute of Science, 2017
Non-invasive monitoring of physiological signals during physical exercise is essential to customize the exercise module. Photoplethysmography (PPG) signal has often been used to non-invasively monitor heart-rate, respiratory-rate, and blood-pressure among other physiological signals. Typically, PPG signal is acquired using pulse-oximeter from fingertip or wrist. Advantage of wrist based PPG sensors is that it is more convenient to wear. Other sensors such as accelerometer can also be integrated with it due to large area on the wrist. This article provides a review of the algorithms developed for heart rate estimation during physical exercise from the PPG signals and accelerometer signals. The datasets used to develop these techniques are described. Algorithms for denoising of PPG signals using accelerometer signals are either in time domain and frequency domain.
IEEE Global Conference on Signal and Information Processing (GlobalSIP 2014), 2014
Heart rate monitoring from wrist-type photoplethysmographic (PPG) signals during subjects’ intensive exercise is a difficult problem, since the PPG signals are contaminated by extremely strong motion artifacts caused by subjects’ hand movements. In this work, we formulate the heart rate estimation problem as a sparse signal recovery problem, and use a sparse signal recovery algorithm to calculate high-resolution power spectra of PPG signals, from which heart rates are estimated by selecting corresponding spectrum peaks. To facilitate the use of sparse signal recovery, we propose using bandpass filtering, singular spectrum analysis, and temporal difference operation to partially remove motion artifacts and sparsify PPG spectra. The proposed method was tested on PPG recordings from 10 subjects who were fast running at the peak speed of 15km/hour. The results showed that the averaged absolute estimation error was only 2.56 Beats/Minute, or 1.94% error compared to groundtruth heart rates from simultaneously recorded ECG.
PPG Derived Heart Rate Estimation during Intensive Physical Exercise
IEEE Access
Accurate and reliable estimation of heart rate (HR) from photoplethysmographic (PPG) signals during moderate and vigorous physical activities is a challenging task, since intense motion artifacts can easily disguise the true HR. A novel method for estimating HR from PPG signal, during intensive physical exercise, is presented in this paper. The proposed method employs a recursive Wiener filtering technique for HR estimation from motion artifacts corrupted PPG signal and simultaneously recorded triaxial accelerometer signal. Experimental results demonstrated that the average relative error and average absolute error of the proposed method on a public dataset (IEEE 2015 Signal Processing Cup Database) of 23 PPG recordings were 1.73 and 1.85 beats per minute respectively. Our proposed approach is faster and more accurate than the existing proposals. Therefore, the proposed algorithm can be a reliable solution for HR estimation from noisy PPG signal.
A time-frequency domain approach of heart rate estimation from photoplethysmographic (PPG) signal
Biomedical Signal Processing and Control
Objective-Heart rate monitoring using wrist type Photoplethysmographic (PPG) signals is getting popularity because of construction simplicity and low cost of wearable devices. The task becomes very difficult due to the presence of various motion artifacts. The objective is to develop algorithms to reduce the effect of motion artifacts and thus obtain accurate heart rate estimation. Methods-Proposed heart rate estimation scheme utilizes both time and frequency domain analyses. Unlike conventional single stage adaptive filter, multi-stage cascaded adaptive filtering is introduced by using three channel accelerometer data to reduce the effect of motion artifacts. Both recursive least squares (RLS) and least mean squares (LMS) adaptive filters are tested. Moreover, singular spectrum analysis (SSA) is employed to obtain improved spectral peak tracking. The outputs from the filter block and SSA operation are logically combined and used for spectral domain heart rate estimation. Finally, a tracking algorithm is incorporated considering neighbouring estimates. Results-The proposed method provides an average absolute error of 1.16 beat per minute (BPM) with a standard deviation of 1.74 BPM while tested on publicly available database consisting of recordings from 12 subjects during physical activities. Conclusion-It is found that the proposed method provides consistently better heart rate estimation performance in comparison to that recently reported by TROIKA, JOSS and SPECTRAP methods. Significance-The proposed method offers very low estimation error and a smooth heart rate tracking with simple algorithmic approach and thus feasible for implementing in wearable devices to monitor heart rate for fitness and clinical purpose.
2015
Heart rate monitoring using wrist-type photoplethysmographic (PPG) signals during subjects' intensive exercise is a difficult problem, since the signals are contaminated by extremely strong motion artifacts caused by subjects' hand movements. So far few works have studied this problem. In this work, a general framework, termed TROIKA, is proposed, which consists of signal decomposiTion for denoising, sparse signal RecOnstructIon for high-resolution spectrum estimation, and spectral peaK trAcking with verification. The TROIKA framework has high estimation accuracy and is robust to strong motion artifacts. Many variants can be straightforwardly derived from this framework. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/hour showed that the average absolute error of heart rate estimation was 2.34 beat per minute (BPM), and the Pearson correlation between the estimates and the ground-truth of heart rate was 0.992. This framework is of great values to wearable devices such as smart-watches which use PPG signals to monitor heart rate for fitness.
Journal of Medical and Biological Engineering
Photoplethysmography (PPG) is a non-invasive optical technique that can be used to quantify the arterial blood pulse rate. Signal corruption by motion artifacts limits the practical accuracy and applicability of instruments for monitoring pulse rate during intense physical exercise. This study develops and validates an algorithm, which is based on linear filtering and frequency-domain and heuristic analyses, for extracting the heart rate from a PPG signal in the presence of severe motion artifacts. The basis of the hearth beat frequency selection is the observed high harmonic content of movement artifact signals with respect to the PPG-derived heartbeat. The algorithm, implemented in an experimental PPG measurement device, is developed by analyzing a set of PPG data recorded from a group of athletes exercising on a treadmill. An extensive set of tests is carried out during maximal exercise tests on a treadmill to validate the proposed algorithm by comparison with a reference electro...
Heart rate estimation from wrist-type photoplethysmography signals during physical exercise
Biomedical Signal Processing and Control, 2020
Wearable devices, such as smart watch use photoplethysmography (PPG) signals for estimating heart rate (HR). The motion artifacts (MA) contained in these PPG signals lead to an erroneous HR estimation. In this manuscript, a new de-noising algorithm has been proposed that uses the combination of cascaded recursive least square (RLS), normalized least mean square (NLMS) and least mean square (LMS) adaptive filters. The MA reduced PPG signals obtained from these cascaded adaptive filters are combined using the softmax activation function. Fast Fourier transform (FFT) is used to estimate the HR from the MA reduced PPG signals and phase vocoder is used to refine the estimated HR. The performance of the proposed method in the form of mean error, standard deviation of the mean error and mean relative error is analyzed using the 22 datasets given for IEEE Signal processing cup 2015. This resulted in an error of 1.86 beat per minute (BPM) tested on 22 datasets which is less compared to other existing methods.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2015
Heart rate tracking from a wrist-type photoplethysmogram (PPG) signal during intensive physical activities is a challenge that is attracting more attention thanks to the introduction of wrist-worn wearable computers. Commonly-used motion artifact rejection methods coupled with simple periodicity-based heart rate estimation techniques are incapable of achieving satisfactory heart rate tracking performance during intense activities. In this paper, we propose a two-stage solution. Firstly, we introduce an improved spectral subtraction method to reject the spectral components of motion artifacts. Secondly, instead of using heuristic mechanisms, we formalize the spectral peaks selection process as the shortest path search problem and validate its effectiveness. Analysis on the experimental results based on a published database shows that: (1) Our proposed method outperforms three other comparable methods with regards to heart rate estimation error. (2) The proposed method is a promising ...
An Estimation Technique using FFT for Heart Rate Derived from PPG Signal
Global Journal of Research In Engineering, 2015
Heart rate (HR) observation by using photoplethysmography (PPG) signals during intense physical activity is a crucial task because of the fact that PPG signals are affected by the noise due to movement artifacts by the user's hand movements. This paper addresses the discriminating assessment of a novel encapsulation for wearable PPG sensor during the severe physical activity. In this work, we plan the HR estimation issue, and utilization of proposed algorithm to find high-determination power spectra of PPG signals, from which heart rates are evaluated by selecting and comparing the peaks. The proposed system was applied on PPG recordings obtained from 10 subjects who were quick runners at the top velocity of 15km/hour on a treadmill. Utilizing correlation and HR investigation with the assistance of peak detection, we assessed the simulation of the proposed framework against the existing works. The outcome demonstrated that the average absolute estimation error achieved using pro...