The jugular venous pulse (JVP) is the reference physiological signal used to detect right atrial and central venous pressure (CVP) abnormalities in cardio-vascular diseases (CVDs) diagnosis. Invasive central venous line catheterization has always been the gold standard method to extract it reliably. However, due to all the risks it entails, novel non-invasive approaches, exploiting distance cameras and lasers, have recently arisen to measure the JVP at the external and internal jugular veins. These remote options however, constraint patients to very specific body positions in front of the imaging system, making it inadequate for long term monitoring. In this study, we demonstrate, for the first time, that reflectance photoplethysmography (PPG) can be an alternative for extracting the JVP from the anterior jugular veins, in a contact manner. Neck JVP-PPG signals were recorded from 20 healthy participants, together with reference ECG and arterial finger PPG signals for validation. B-mode ultrasound imaging of the internal jugular vein also proved the validity of the proposed method. The results show that is possible to identify the characteristic a, c, v pressure waves in the novel signals, and confirm their cardiac-cycle timings in consistency with established cardiac physiology. Wavelet coherence values (close to 1 and phase shifts of ±180°) corroborated that neck contact JVP-PPG pulses were negatively correlated with arterial finger PPG. Average JVP waveforms for each subject showed typical JVP pulses contours except for the singularity of an unknown "u" wave occurring after the c wave, in half of the cohort. This work is of great significance for the future of CVDs diagnosis, as it has the potential to reduce the risks associated with conventional catheterization and enable continuous non-invasive point-of-care monitoring of CVP, without restricting patients to limited postures.
This paper presents a comparison between finger and neck photoplethysmography (PPG) in order to assess the potential and limitations of this, non-conventionally used, body site for application in pulse oximetry. PPG signals were recorded at both sites from healthy subjects to inspect the differences in average waveforms, as well as in oxygen saturation (SpO 2) and heart rate (HR) estimation. The results show significant differences in the average PPG pulse waveforms for different contour features such as diastolic or dicrotic notch amplitude, among others. The results show that the HR estimated from signals obtained with the neck sensor are strongly correlated to the output of the reference finger (R=0.862, MAE=1.27 BPM), whereas SpO 2 measurements are not that accurately predicted (R=0.129, MAE=11.7%). Spectrograms under different breathing conditions revealed that the respiratory frequency is more predominant in neck PPG than in finger, which has a great potential for respiratory rate (RR) extraction. These are very promising results for the suitability of the neck as an alternative location for monitoring of respiratory diseases, and specifically for sleep apnea.
Objective: The neck is a very attractive measurement location for multimodal physiological monitoring, since it offers the possibility of extracting clinically relevant parameters, which cannot be obtained from other body locations, such as lung volumes. It is for this reason that obtaining PPG from the neck would be of interest. PPG signals, however, are very susceptible to artifacts which greatly compromise their quality. But the extent of this is going to depend on, the nature of the artifacts and the strength of the sensed signal, both of which are location dependent. This paper presents for the first time the characterization of artifacts affecting neck PPG signals. Methods: Neck PPG data was recorded from 19 participants, who performed ten different activities to deliberately introduce common artifacts. 41 PPG features were extracted and statistically analyzed to investigate which ones showed the greatest ability to differentiate normal PPG from each artifact. A customized minimum Redundancy Maximum Relevance (mRMR) feature selection approach was implemented, to select the top 10 features. Results: Artifacts caused by Swallowing, Yawning and Coughing exhibited larger Spectral Entropy, Average Power and smaller Spectral Kurtosis, than normal PPG. Head movement artifacts, also demonstrated highly disordered and noisy frequency spectra, and were characterized by having larger and irregular time domain features. In addition, the analysis showed that different respiratory states that could be of clinical interests, such as presence of apneas, were also distinguishable from sources of interference. Significance: These findings are important for the development of PPG denoising algorithms and subsequent obtention of biomarkers of interest, or alternatively for applications where the events of interest are the artifacts themselves.
Continuous overnight vital signs monitoring would be ideal for patients suffering from epilepsy, where life-threatening hypoxemias can occur during sleep. However, existing physiological monitoring systems suffer from limitations in terms of usability factors and/or limited information of the signals being acquired. The body location of the monitoring system is a crucial consideration, seldom addressed by the wider community. This paper presents a proof-of-concept, neck worn photoplethysmography system, which was developed and tested to assess the feasibility of the neck as a monitoring site for longitudinal sensing of cardiac and respiratory responses during sleep. The novel system was compared against a gold-standard commercial multichannel cardiorespiratory polysomnography system during oxygen desaturation cycles, to assess its ability to measure heart rate, respiratory rate, and peripheral blood oxygen saturation (SpO2) on 15 participants. The findings for heart rate showed a marginal mean error of 0.47 beats/minute with limits of agreement at 95 (%) confidence between -3.17 and 4 bpm. Respiratory rate comparisons had an overall mean error of 0.43 breaths/minute, with limits of agreement at 95 (%) confidence between -2.73 and 3.3 Bpm. Lastly, the system accurately outputs SpO2 with an overall-root-meansquare error of 1.44 (%) between 90-100 (%) SpO2 using a custom calibration method. Moreover, it was observed the neck made it possible for the system to detect desaturation events on average 12.6 seconds prior to the polysomnography system, which used a peripheral fingerbased PPG system. Ultimately, this proof-of-concept study illustrates the viability of neck-based sensing for minimally invasive monitoring of cardiac and respiratory vitals during sleep.
The novel pulse oximetry measurement site of the neck is a promising location for multi-modal physiological monitoring. Specifically, in the context of respiratory monitoring, in which it is important to have direct information about airflow. The neck makes this possible, in contrast to common photoplethysmography (PPG) sensing sites. However, this PPG signal is susceptible to artifacts that critically impair the signal quality. To fully exploit neck PPG for reliable physiological parameters extraction and apneas monitoring, this paper aims to develop two classification algorithms for artifacts and apnea detection. Features from the time, correlogram, and frequency domains were extracted. Two SVM classifiers with RBF kernels were trained for different window (W) lengths and thresholds (Thd) of corruption. For artifacts classification, the maximum performance was attained for the parameters combination of [W = 6s-Thd= 20%], with an average accuracy= 85.84%(ACC), sensitivity= 85.43%(SE) and specificity= 86.26%(SP). For apnea detection, the model [W = 10s-Thd= 50%] maximized all the performance metrics significantly (ACC= 88.25%, SE= 89.03%, SP= 87.42%). The findings of this proof of concept are significant for denoising novel neck PPG signals, and demonstrate, for the first time, that it is possible to promptly detect apnea events from neck PPG signals in an instantaneous manner. This could make a big impact in crucial real-time applications, like devices to prevent sudden-unexpected-death-in-epilepsy (SUDEP).
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