The use of successive systolic differences in photoplethysmographic (PPG) signals for respiratory rate estimation.

Most PPG-based methods for extracting the respiratory rate (RR) rely on changes in the PPG signal's amplitude, baseline, or frequency. However, several other parameters may provide more valuable information for accurate RR computation. In this study, we explored the capabilities of the respiratory-induced variations in successive systolic differences (RISSDV) of PPG signals to estimate RR. We partitioned fifty-three publicly available recordings into eight 1-min segments and identified peaks and troughs of the PPG signals to quantify respiratory-induced variations in amplitude (RIAV), baseline (RIIV), frequency (RIFV), and peak-to-peak amplitude differences (RISSDV). RR values were extracted by determining the peak frequency of the power spectral density of the four variations and the reference respiratory signal. We assessed each feature's performance by computing the root-mean-squared (RMSE) and mean absolute errors (MAE). RISSDV errors were significantly lower than those of RIAV (RMSE and MAE: p < 0.001), RIIV (RMSE: p < 0.01; MAE p < 0.05), and RIFV (RMSE and MAE: p < 0.001), and it appeared less sensitive to absent or missed PPG pulses than respiratory-induced frequency variations. Further research is necessary to extrapolate these findings to subjects under ambulatory rather than stationary conditions, including pediatric and neonatal populations.

A low-complexity PPG pulse detection method for accurate estimation of the pulse rate variability (PRV) during sudden decreases in the signal amplitude.

BACKGROUND: One of the biggest obstacles to reliable pulse rate variability (PRV) analysis is the erroneous detection of photoplethysmographic (PPG) pulses. Among all the disturbances that may hinder pulse detection, the ripples appearing at the smooth segments of the PPG signal can become a serious problem when the amplitude of the signal decreases considerably. OBJECTIVE: To present a low-complexity PPG pulse detection method for reliable PRV estimation under conditions in which a sudden decrease in the amplitude of the PPG signal can be expected. APPROACH: 2-min ECG and PPG data (sampling rate at 500 Hz) were obtained from thirty healthy subjects, who were asked to take a deep inspiration to provoke a sudden amplitude decrease (SAD) of the PPG signal. After introducing a new parameter denoted as C, through which it is possible to jump over the ripples hindering the accurate detection of the systolic peaks, 500 Hz-sampled PPG recordings were down-sampled (400, 300, 200 and 100 Hz) to investigate the effect of the sampling rate on pulse detection. For ECG recordings, automatic R-peak detection was performed by the Pan and Tompkins (PT) algorithm, whereas PPG pulse detection was performed by the well-known maximum of the first derivative (M1D) and the proposed method, once the C-value for best detection results on 500 Hz-sampled PPG recordings was found. The agreement between heart rate variability (HRV) and PRVs estimated from each pulse detection method was assessed and the correlation between HRV and PRV-derived indexes was computed for comparison. MAIN RESULTS: The proposed method can perform well on PPG-SAD segments, provided that the proper value of the parameter C is used. Moreover, a good agreement between HRV and PRV series, as well as lower relative errors and higher correlation coefficients between HRV and PRV indexes, were achieved by the proposed pulse detection method during SADs. SIGNIFICANCE: Results show that the proposed method can dynamically adapt to circumstances in which a decrease in the amplitude of the PPG signal can be expected, providing continuous systolic peak detection and reliable PRV estimation under those conditions. However, more extensive testing under a wide range of conditions is needed to perform a more rigorous validation.

A novel and low-complexity peak detection algorithm for heart rate estimation from low-amplitude photoplethysmographic (PPG) signals.

Low-amplitude PPG signals are more affected by noise contamination and other undesirable effects because the signal strength is comparable to noise power. Although several authors claim that decreases in the amplitude of the PPG wave should be addressed from signal acquisition and conditioning stages such decreases can also be associated with changes in the patient condition. In that instance, it is important to ensure continuous and reliable HR monitoring which, in turn, depends on how robust is the peak detection method. Numerous efforts have been made to develop algorithms for accurate PPG peak detection under high motion artefact conditions. However, little has been done regarding peak detection in low-amplitude PPG signals. In an attempt to address this issue, a novel and simple peak detection algorithm for PPG signals was proposed. Results show that our method could be a good contribution for robust strategies that can dynamically adapt their peak detection method to circumstances in which a decrease in the amplitude of the PPG signal is expected. Still, more extensive testing under a wide range of conditions (e.g. intensive physical exercise) is needed to perform a rigorous validation.

The implementation of the Neuroid in the Gate Control System leads to new ideas about pain processing

INTRODUCTION: Several theories have been proposed to elucidate the mechanisms related with pain perception, among which, the Gate Control Theory (GCT) provides one of the most explicit explanations. This theory, as elegantly conceived, is unable to explain how the Frequency-Intensity (F-I) curves exhibited by Aβ- and C-fibres influence pain processing. In this paper, a novel neuron-model known as the Neuroid, which emphasizes the functional rather the physiological character of nerve cells, was used as the main building block to replicate the Gate Control System (GCS). METHODS: Two Aβ-fibre models were built: one model that preserved the paradoxical relation between the activation threshold and the F-I curve slope, and one model based on the hypothetical average response across the receptive field. RESULTS: The results suggest that the average response of the Aβ-fibres does not increase monotonically but reaches a plateau for high intensity stimuli. In addition, it was seen that activation of C-fibres does not necessarily imply the activation of projection neurons and, therefore, the onset of pain sensation. Also, we observed that the activation of Aβ-fibres may both, decrease and increase the activity of the projections neurons, an aspect which has not been directly described in previous works. CONCLUSION: Hypothetical implications arise as a consequence of the implementation of the Neuroid, specifically, about the correlation between the intensity of stimulation and the physiological pain threshold.