Spectral analysis of photoplethysmographic signals: The importance of preprocessing

Akar, Saime Akdemir; Kara, Sadık; Latifoğlu, Fatma; Bilgiç, Vedat

doi:10.1016/j.bspc.2012.04.002

Cited by 56 publications

(17 citation statements)

References 13 publications

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“…PPG signals were filtered using a 0.05-10 Hz Butterworth band pass filter to compute PWV (Elgendi, 2012;Akdemir Akar et al, 2013;Sun et al, 2016). The PPG, NIRS and BP signals were resampled at 25 Hz.…”

Section: Data Preprocessingmentioning

confidence: 99%

Multimodal Monitoring of Cardiovascular Responses to Postural Changes

et al. 2020

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Background: In the poorly understood relationship between orthostatic hypotension and falls, next to blood pressure (BP), baroreflex sensitivity (BRS) and cerebral autoregulation (CAR) may be key measures. The posture-and movement dependency of orthostatic hypotension requires continuous and unobtrusive monitoring. This may be possible using simultaneous photoplethysmography (PPG), electrocardiography (ECG), and near-infrared spectroscopy (NIRS) signal recordings, from which pulse wave velocity (PWV; potentially useful for BP estimation), BRS and CAR can be derived. The PPG, NIRS and PWV signal correlation with BP and BRS/CAR reliability and validity need to be addressed. Methods: In 34 healthy adults (mean age 25 years, inter quartile range 22-45; 10 female), wrist and finger PPG, ECG, bifrontal NIRS (oxygenated and deoxygenated hemoglobin) and continuous BP were recorded during sit to stand and supine to stand movements. Sixteen participants performed slow and rapid supine to stand movements; eighteen other participants performed a 1-min squat movement. Pulse wave velocity (PWV) was defined as the inverse of the ECG R-peak to PPG pulse delay; PPG, NIRS and PWV signal correlation with BP as their Pearson correlations with mean arterial pressure (MAP) within 30 s after the postural changes; BRS as inter beat interval drop divided by systolic BP (SBP) drop during the postural changes; CAR as oxygenated hemoglobin drop divided by MAP drop. BRS and CAR were separately computed using measured and estimated (linear regression) BP. BRS/CAR reliability was defined by the intra class correlation between repeats of the same postural change; validity as the Pearson correlation between BRS/CAR values based on measured and estimated BP. Results: The highest correlation with MAP was found for finger PPG and oxygenated hemoglobin, ranging from 0.75-0.79 (sit to stand), 0.66-0.88 (supine to stand), and 0.82-0.94 (1-min squat). BRS and CAR reliability was highest during the different supine

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Section: Data Preprocessingmentioning

confidence: 99%

Multimodal Monitoring of Cardiovascular Responses to Postural Changes

et al. 2020

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“…To mitigate this problem, the pre-processing is designed to remove noise using the Butterworth second-order filter [20]. Also, the PPG and ECG signals pass through the Butterworth second-order band-pass filter (0.5 Hz and 11 Hz) [21,22] and Butterworth second-order low-pass filter (cut-off: 30 Hz) [23], respectively.…”

Section: Nn-based Bpementioning

confidence: 99%

Novel Data Augmentation Employing Multivariate Gaussian Distribution for Neural Network-Based Blood Pressure Estimation

2021

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In this paper, we propose a novel data augmentation technique employing multivariate Gaussian distribution (DA-MGD) for neural network (NN)-based blood pressure (BP) estimation, which incorporates the relationship between the features in a multi-dimensional feature vector to describe the correlated real-valued random variables successfully. To verify the proposed algorithm against the conventional algorithm, we compare the results in terms of mean error (ME) with standard deviation and Pearson correlation using 110 subjects contributed to the database (DB) which includes the systolic BP (SBP), diastolic BP (DBP), photoplethysmography (PPG) signal, and electrocardiography (ECG) signal. For each subject, 3 times (or 6 times) measurements are accomplished in which the PPG and ECG signals are recorded for 20 s. And, to compare with the performance of the BP estimation (BPE) using the data augmentation algorithms, we train the BPE model using the two-stage system, called the stacked NN. Since the proposed algorithm can express properly the correlation between the features than the conventional algorithm, the errors turn out lower compared to the conventional algorithm, which shows the superiority of our approach.

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“…Equations (9) and (10) show the calculation of the new signal SSF(i) transformed from the original signal, where y is the original signal, i represents the time index of the signal and w is the window size.…”

Section: Slope Sum Function (Ssf)mentioning

confidence: 99%

“…However, PRV measurement with rPPG is more complicated, because it requires precise peak detection of the BVP signal, which is prone to noise emanating from sensors, light illumination variation, movements, and so forth. Although some methods exist to improve the BVP peak detection and PRV measurement for contact PPG [8][9][10], most of them have not been applied in rPPG applications. Some works used complex equipment such as thermal cameras and 5-band cameras to improve the performance of remote PRV measurement [11,12], but such equipment is either too expensive or not widely available in everyday life.…”

Section: Introductionmentioning

confidence: 99%

Video-Based Pulse Rate Variability Measurement Using Periodic Variance Maximization and Adaptive Two-Window Peak Detection

Benezeth

Macwan

et al. 2020

Sensors

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Many previous studies have shown that the remote photoplethysmography (rPPG) can measure the Heart Rate (HR) signal with very high accuracy. The remote measurement of the Pulse Rate Variability (PRV) signal is also possible, but this is much more complicated because it is then necessary to detect the peaks on the temporal rPPG signal, which is usually quite noisy and has a lower temporal resolution than PPG signals obtained by contact equipment. Since the PRV signal is vital for various applications such as remote recognition of stress and emotion, the improvement of PRV measurement by rPPG is a critical task. Contact based PRV measurement has already been investigated, but the research on remotely measured PRV is very limited. In this paper, we propose to use the Periodic Variance Maximization (PVM) method to extract the rPPG signal and event-related Two-Window algorithm to improve the peak detection for PRV measurement. We have made several contributions. Firstly, we show that the newly proposed PVM method and Two-Window algorithm can be used for PRV measurement in the non-contact scenario. Secondly, we propose a method to adaptively determine the parameters of the Two-Window method. Thirdly, we compare the algorithm with other attempts for improving the non-contact PRV measurement such as the Slope Sum Function (SSF) method and the Local Maximum method. We calculated several features and compared the accuracy based on the ground truth provided by contact equipment. Our experiments showed that this algorithm performed the best of all the algorithms.

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Spectral analysis of photoplethysmographic signals: The importance of preprocessing

Cited by 56 publications

References 13 publications

Multimodal Monitoring of Cardiovascular Responses to Postural Changes

Multimodal Monitoring of Cardiovascular Responses to Postural Changes

Novel Data Augmentation Employing Multivariate Gaussian Distribution for Neural Network-Based Blood Pressure Estimation

Video-Based Pulse Rate Variability Measurement Using Periodic Variance Maximization and Adaptive Two-Window Peak Detection

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