Finger Photoplethysmographic Signal Research Articles

Estimation of carotid-femoral pulse wave velocity from finger photoplethysmography signal

Objective. This project compared a new method to estimate the carotid-femoral pulse wave velocity (cf-PWV) to the gold-standard cf-PWV technique. Approach. The cf-PWV was estimated from the pulse transit time (FPS-PTT) calculated by processing the finger photoplethysmographic signal of Finapres (FPS) and subject’s height only (brief mode) as well as along with other variables (age, heart rate, arterial pressure, weight; complete mode). Doppler ultrasound cf-PWVs and FPS-PTTs were measured in 90 participants equally divided into 3 groups (18–30; 31–59; 60–79 years). Predictions were performed using multiple linear regressions (MLR) and with the best regression model identified by using MATLAB Regression Learner App. A validation set approach (60 training datasets, 30 testing datasets; VSA) and leave-one-out cross-validation (LOOCV) were used. Main results. With MLR, the discrepancies were: 0.01 ± 1.21 m s−1 (VSA) and 0.001 ± 1.11 m s−1 (LOOCV) in brief mode; −0.02 ± 0.83 m s−1 (VSA) and 0.001 ± 0.84 m s−1 (LOOCV) in complete mode. Using a linear support vector machine model (SVM) in brief mode, the discrepancies were: 0.01 ± 1.19 m s−1 (VSA) and −0.01 ± 1.06 m s−1 (LOOCV). Using an Exponential Gaussian process regression model (GPR) in complete mode, the discrepancies were: −0.03 ± 0.79 m s−1 (VSA) and 0.01 ± 0.75 m s−1 (LOOCV). Significance. The cf-PWV can be estimated by processing the FPS-PTT and subjects’ height only, but the inclusion of other variables improves the prediction performance. Predictions through MLR qualify as acceptable in both brief and complete modes. Predictions via linear SVM in brief mode improve but still qualify as acceptable. Interestingly, predictions through Exponential GPR in complete mode improve and qualify as excellent.

Optimal fiducial points for pulse rate variability analysis from forehead and finger photoplethysmographic signals

Objective: The aim of this work is to evaluate and compare five fiducial points for the temporal location of each pulse wave from forehead and finger photoplethysmographic (PPG) pulse wave signals to perform pulse rate variability (PRV) analysis as a surrogate for heart rate variability (HRV) analysis. Approach: Forehead and finger PPG signals were recorded during a tilt-table test simultaneously with the electrocardiogram (ECG). Artefacts were detected and removed and five fiducial points were computed: apex, middle-amplitude and foot points of the PPG signal, apex point of the first derivative signal and the intersection point of the tangent to the PPG waveform at the apex of the derivative PPG signal and the tangent to the foot of the PPG pulse, defined as the intersecting tangents method. Pulse period (PP) time interval series were obtained from both PPG signals and compared with the RR intervals obtained from the ECG. HRV and PRV signals were estimated and classical time and frequency domain indices were computed. Main results: The middle-amplitude point of the PPG signal (nM), the apex point of the first derivative (), and the tangent intersection point (nT) are the most suitable fiducial points for PRV analysis, resulting in the lowest relative errors estimated between PRV and HRV indices and higher correlation coefficients and reliability indices. Statistically significant differences according to the Wilcoxon test between PRV and HRV signals were found for the apex and foot fiducial points of the PPG, as well as the lowest agreement between RR and PP series according to Bland–Altman analysis. Hence, these signals have been considered less accurate for variability analysis. In addition, the relative errors are significantly lower for nM and using Friedman statistics with a Bonferroni multiple-comparison test, and we propose that nM is the most accurate fiducial point. Based on our results, forehead PPG seems to provide more reliable information for a PRV assessment than finger PPG. Significance: The accuracy of the pulse wave detection depends on the morphology of the PPG. There is therefore a need to widely define the most accurate fiducial point for performing a PRV analysis under non-stationary conditions based on different PPG sensor locations and signal acquisition techniques.

Pulse wave amplitude and heart period variability in children with upper airway obstruction

ObjectiveThe aim of this study was to assess cardiovascular autonomic modulation in children with upper airway obstruction (UAO), to compare this modulation to that of non-snoring children and to investigate the effect of adenotonsillectomy (AT). MethodsECG and finger photoplethysmographic signals obtained from overnight polysomnographic (PSG) recordings of 31 children with mild-to-moderate UAO and 34 non-snoring children were analysed. The extent of autonomic modulation was assessed by symbolic analysis of heart period (HP), pulse wave amplitude (PWA), and their joint dynamics during non–rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep. ResultsChildren with UAO showed more frequent patterns of monotonically increasing and decreasing HP in NREM sleep and monotonically increasing and decreasing joint PWA-HP patterns in REM and NREM sleep at baseline compared to controls, even when considering only periods of sleep free of discrete respiratory events. Following AT, HP, and joint PWA-HP dynamics significantly altered towards levels observed in the control group. ConclusionsIn children with mild-to-moderate UAO, cardiovascular autonomic modulation is more prevalent, even during quiet, event-free sleep. AT appears to reverse this pattern.

Potential for Health Screening Using Long-Term Cardiovascular Parameters Measured by Finger Volume-Oscillometry: Pilot Comparative Evaluation in Regular and Sleep-Deprived Activities

We explored the potential of health screening based on the long-term measurement of cardiovascular parameters using the finger volume-oscillometric technique. An automated instrument made simultaneous measurements of key cardiovascular parameters, including blood pressure, pulse pressure, heart rate, normalized pulse volume as an index of α-adrenalin-mediated sympathetic activity, and finger arterial elasticity. These were derived from finger photo-plethysmographic signals during application of cuff pressure. To assess the feasibility of achieving a screening function, measurements were made in ten healthy volunteers during 10days of day-to-day living (normal condition), and carried out several times at a fixed time every day. During successive 10-day measurements, a 30-hour period of total sleep deprivation was introduced as a physiological challenge (abnormal condition). A linear discriminant analysis of the data was conducted to determine whether these two conditions could be discriminated. Periodic data collection was performed rapidly and easily, and the %-correct classifications of normal and abnormal conditions were 78.2% and 77.5%, respectively. This ability of the method to discriminate between regular and sleep-deprived activities demonstrates its potential for healthcare screening during day-to-day living. Further investigations using larger age and gender groups of subjects including patients with cardiovascular diseases under real-life situations are required.

Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network

Extraction of extra-cardiac information from photoplethysmography (PPG) signal is a challenging research problem with significant clinical applications. In this study, radial basis function neural network (RBFNN) is used to reconstruct the gastric myoelectric activity (GMA) slow wave from finger PPG signal. Finger PPG and GMA (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the sampling rate of 100 Hz from ten healthy subjects. Discrete wavelet transform (DWT) was used to extract slow wave (0-0.1953 Hz) component from the finger PPG signal; this slow wave PPG was used to reconstruct EGG. A RBFNN is trained on signals obtained from six subjects in both fasting and postprandial conditions. The trained network is tested on data obtained from the remaining four subjects. In the earlier study, we have shown the presence of GMA information in finger PPG signal using DWT and cross-correlation method. In this study, we explicitly reconstruct gastric slow wave from finger PPG signal by the proposed RBFNN-based method. It was found that the network-reconstructed slow wave provided significantly higher (P < 0.0001) correlation (≥ 0.9) with the subject's EGG slow wave than the correlation obtained (≈0.7) between the PPG slow wave from DWT and the EEG slow wave. Our results showed that a simple finger PPG signal can be used to reconstruct gastric slow wave using RBFNN method.

Evaluation of the surgical stress index during spinal and general anaesthesia

BackgroundAlthough in modern anaesthesia, monitoring depth of anaesthesia and quality of neuromuscular block are routine, monitoring of analgesia still remains challenging. Recently, the surgical stress index (SSI), derived from finger photoplethysmographic signal, was introduced as a surrogate variable reflecting the nociception–antinociception balance. This study aimed at evaluating the SSI in patients undergoing regional anaesthesia either alone or combined with sedation compared with patients undergoing general anaesthesia (GA). MethodsSeventy-one patients undergoing general (n=24) or spinal anaesthesia with (n=24) or without sedation (n=23) were included. SSI was measured the day before surgery and at defined time points during anaesthesia and surgery and also in the recovery room. SSI was compared with haemodynamic variables like heart rate and systolic arterial pressure. ResultsThe SSI was higher in patients undergoing spinal anaesthesia [mean 65, CI (59.3–70.5)] compared with GA [48 (39.9–56.4), P<0.01], and baseline [41 (37.3–44.2), P<0.001]. During spinal anaesthesia with sedation [44 (36.2–50.9)], it was comparable with the baseline level (P>0.05). In comparison with baseline, SSI in the recovery room was higher in patients after GA [59 (48.4–67.9), P<0.025] but not after spinal anaesthesia [53 (47.6–60.1), P>0.05] or after spinal anaesthesia with sedation [54 (45.8–65.1), P>0.05]. Changes of the SSI were not reflected by changes of haemodynamic variables. ConclusionsIn fully awake patients under spinal anaesthesia, the SSI does not reflect the nociception–antinociception balance. This may be due to the influence of mental stress on the sympathetic nervous system. Even light sedation attenuates these influences.

On Non-Invasive Measurement of Gastric Motility from Finger Photoplethysmographic Signal

This article investigates the possibility of extracting gastric motility (GM) information from finger photoplethysmographic (PPG) signals non-invasively. Now-a-days measuring GM is a challenging task because of invasive and complicated clinical procedures involved. It is well-known that the PPG signal acquired from finger consists of information related to heart rate and respiratory rate. This thread is taken further and effort has been put here to find whether it is possible to extract GM information from finger PPG in an easier way and without discomfort to the patients. Finger PPG and GM (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the rate of 100 Hz from eight healthy subjects for 30 min duration in fasting and postprandial states. In this study, we process the finger PPG signal and extract a slow wave that is analogous to actual EGG signal. To this end, we chose two advanced signal processing approaches: first, we perform discrete wavelet transform (DWT) to separate the different components, since PPG and EGG signals are non-stationary in nature. Second, in the frequency domain, we perform cross-spectral and coherence analysis using autoregressive (AR) spectral estimation method in order to compare the spectral details of recorded PPG and EGG signals. In DWT, a lower frequency oscillation (≈0.05 Hz) called slow wave was extracted from PPG signal which looks similar to the slow wave of GM in both shape and frequency in the range (0-0.1953) Hz. Comparison of these two slow wave signals was done by normalized cross-correlation technique. Cross-correlation values are found to be high (range 0.68-0.82, SD 0.12, R = 1.0 indicates exact agreement, p < 0.05) for all subjects and there is no significant difference in cross-correlation between fasting and postprandial states. The coherence analysis results demonstrate that a moderate coherence (range 0.5-0.7, SD 0.13, p < 0.05) exists between EGG and PPG signal in the "slow wave" frequency band, without any significant change in the level of coherence in postprandial state. These results indicate that finger PPG signal contains GM-related information. The findings are sufficiently encouraging to motivate further exploration of finger PPG as a non-invasive source of GM-related information.

Extraction of Gastric Myoelectric Activity from Finger Photoplethysmographic Signal

This paper is an experimental study to examine the possibility of extracting gastric myoelectric activity (GMA) from photoplethysmographic (PPG) signals. Diagnosing GMA is a clinically challenging task because of its invasive/cumbersome methods. It is known that the PPG consists of information related to heart rate, respiratory rate and phenomena. Here we take this thread further and see whether GMA can be extracted from PPG in a simpler way and without discomfort to the patients. Since PPG and GMA signals are nonstationary, we choose discrete wavelet transform (DWT) to separate the different frequency components. PPG and Electrogastrog-ram (EGG, a method of measuring GMA) signals were acquired simultaneously at the rate of 100 Hz from 8 healthy subjects for 30 minutes duration in fasting and postprandial states. Both the signals were decomposed using DWT up to the frequency range (0 - 0.1) Hz. A lower frequency oscillation (≈ 0.05 Hz) called slow wave was extracted from PPG signal which looks similar to the slow wave of GMA in both shape and frequency. Normalized cross-correlation technique was used for comparing the two signals. Cross-correlation values were found to be high (R ≥ 0.73, R = 1.0 indicates exact agreement) for all subjects without any significant change between fasting and postprandial states. The results suggest that there is a possibility of extracting gastric related information from PPG signals using appropriate signal processing techniques. In future this novel technique could be used as a diagnostic tool for gastrointestinal system disorders.