Photoplethysmography and its application in clinical physiological measurement

Accurate determination of pulse onset and peak is important in many photoplethysmogram (PPG) signal analysis applications. This paper presents a new automatic robust pulse onset and peak detection method which consists of the stationary wavelet transform; the multiscale sum and product; the adaptive amplitude thresholding; the smooth Shannon entropy envelope extraction; the Gaussian derivative filter-based peak finding; and the peak and onset determination and correction. The proposed method achieves an average sensitivity (Se) of 99.66%, positive predictivity (Pp) of 99.90%, and overall accuracy (OA) of 99.55% on a total number of 1 16 255 beats taken from the Massachusetts Institute of Technology–Beth Israel Hospital polysomnographic sleep and complex system laboratory databases and finger pulse database. We further studied the robustness of three peak detection methods such as bandpass filter and Hilbert transform (BPF + HT) [12] , pulse waveform delineator (PUD) [20] , and the proposed method using the noisy PPG signals with a signal-to-noise ratio (SNR) ranging from 30 to 10 dB. The method achieved an average Se of 99.51%, Pp of 99.92%, and OA of 99.43% for the PPG signals with SNR of 10 dB whereas the BPF + HT and PUD methods achieved an average Se = 99.66%, Pp = 87.71%, and OA = 87.45%, and Se = 68.04%, Pp = 70.38%, and OA = 52.89%, respectively. The proposed method significantly outperforms the other methods in terms of detection accuracy and robustness under noise-free and noisy PPG recordings. Unlike the existing methods, the proposed method does not use search-back algorithms to reject or include the noise peaks or missed peaks.

A Robust Pulse Onset and Peak Detection Method for Automated PPG Signal Analysis System

Wave reflections affect the proximal aortic pressure and flow waves and play a role in systolic hypertension. A measure of wave reflection, receiving much attention, is the augmentation index (AI), the ratio of the secondary rise in pressure and pulse pressure. AI can be limiting, because it depends not only on the magnitude of wave reflection but also on wave shapes and timing of incident and reflected waves. More accurate measures are obtainable after separation of pressure in its forward (P f ) and reflected (P b ) components. However, this calculation requires measurement of aortic flow. We explore the possibility of replacing the unknown flow by a triangular wave, with duration equal to ejection time, and peak flow at the inflection point of pressure (F tΙP ) and, for a second analysis, at 30% of ejection time (F t30 ). Wave form analysis gave forward and backward pressure waves. Reflection magnitude (RM) and reflection index (RI) were defined as RM=P b /P f and RI=P b (P f +P b ), respectively. Healthy subjects, including interventions such as exercise and Valsalva maneuvers, and patients with ischemic heart disease and failure were analyzed. RMs and RIs using F tIP and F t30 were compared with those using measured flow (F m ). Pressure and flow were recorded with high fidelity pressure and velocity sensors. Relations are: RM tIP =0.82RM mf +0.06 (R 2 =0.79; n=24), RM t30 =0.79RM mf +0.08 (R 2 =0.85; n=29) and RI tIP =0.89RI mf +0.02 (R 2 =0.81; n=24), RI t30 =0.83RI mf +0.05 (R 2 =0.88; n=29). We suggest that wave reflection can be derived from uncalibrated aortic pressure alone, even when no clear inflection point is distinguishable and AI cannot be obtained. Epidemiological studies should establish its clinical value.

Quantification of wave reflection in the human aorta from pressure alone a proof of principle. Commentary

In this paper, we propose a new automated method for determining systolic peaks in the photoplethysmography(PPG) signal. The proposed systolic peak detection method includes four stages. In the first stage, variational mode decomposition (VMD) technique is used for enhancing the PPG signal and suppression of background noises such as baseline wanders, powerline and high-frequency noises. In the second stage, the candidate envelope is obtained using a center of gravity (CoG) technique. In the third stage, the locations of peaks present in the local waves of the envelope are determined by detecting the positive zerocrossing points of the CoG envelope. Finally, the detected peak locations are used for finding true locations of systolic peaks present in the PPG signal using the peak adjustment procedure. The proposed method is validated and tested by using variety of PPG signals. Evaluation results showed that the method achieves an average Se of 99.10%, Pp of 99.77%, DER of 1.13% and ACC of 98.88% for MIT-BIH Polysomnographic database and an average Se of 99.39 %, Pp of 99.67 %, DER of 0.94 %, and ACC of 99.06% for the real-time PPG signals.

Effective systolic peak detection algorithm using variational mode decomposition and center of gravity

Venous refilling time (VRT) can diagnose the presence of venous diseases in lower limbs. In order to calculate VRT it is necessary to determine the End of the Emptying Maneuvers (EEM). First Derivative Method (FDM) can be employed for automatic detection of the EEM, but its sensitivity to artifacts and noise can degrade its performance. In contrast, studies report that Area Triangulation Method (ATM) evinces effectiveness in biosignals point finding. This work compares the exactness of ATM and FDM for recognition of the EEM. The annotations made by 3 trained human observers on 37 photoplethysmography records were used as a reference. Bland-Altman graphics supported the analysis of agreement among human observers and methods, which was complemented with Analysis of variance and Multiple Comparisons statistical tests. Results showed that ATM is more accurate than FDM for automatic detection of the EEM, with statistically significant differences (p-value < 0.01).

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Area Triangulation Method for Automatic Detection of Venous Emptying Maneuvers

In this paper, a new method for systolic peak detection of pulse wave signals is presented. This method is based on the detection of each peak by the correlation with adaptive Gaussian function template (GFT). The point of maximum correlation is selected as a systolic peak. Its accuracy and noise robustness were evaluated over a set of annotated signals (arterial blood pressure) from CSL database. The GFT method showed an error respect to trained observers less than 5.5 ± 4.2 ms. Furthermore, this method has an error of 8.99 ± 25.09 ms for several noise realization at 9 dB of signal-noise ratio. The results suggest that this method could be used in the measurement of heart rate and pulse transit time, and in the study of autonomic function.

Algorithm for systolic peak detection of pulse wave

Venous refilling time (VRT) can diagnose the presence of venous system diseases. In order to calculate the VRT it is necessary to determine the End of the Empting Maneuvers (EEM). The automation of this process has been identified as a difficult task. Some methods described previously have some difficulties that could be solved. This paper proposes a new method for the automatic detection of the EEM. The new proposed method was compared with other based on sound signals. The annotations made by two trained human observers on 41 photoplethysmography records, were also used as a reference for evaluating the methods. The presented method was more accurate, showing a performance comparable to the observers.

Hugo Fernando Posada Quintero

Papers

Algorithm for systolic peak detection of pulse wave

Automatic detection of the end of the Empting Maneuvers in venous muscle pump test