Simple photoplethysmography pulse encoding technique for communicating the detection of peripheral arterial disease—a proof of concept study

Convolutional neural network based on photoplethysmography signals for sleep apnea syndrome detection

Introduction

The current method of monitoring sleep disorders is complex, time-consuming, and uncomfortable, although it can provide scientifc guidance to ensure worldwide sleep quality. This study aims to seek a comfortable and convenient method for identifying sleep apnea syndrome.

Methods

In this work, a one-dimensional convolutional neural network model was established. To classify this condition, the model was trained with the photoplethysmographic (PPG) signals of 20 healthy people and 39 sleep apnea syndrome (SAS) patients, and the influence of noise on the model was tested by anti-interference experiments.

Results and Discussion

The results showed that the accuracy of the model for SAS classifcation exceeds 90%, and it has some antiinterference ability. This paper provides a SAS detection method based on PPG signals, which is helpful for portable wearable detection.

Establishing best practices in photoplethysmography signal acquisition and processing

Photoplethysmography is now widely utilised by clinical devices such as pulse oximeters, and wearable devices such as smartwatches. It holds great promise for health monitoring in daily life. This editorial considers whether it would be possible and beneficial to establish best practices for photoplethysmography signal acquisition and processing. It reports progress made towards this, balanced with the challenges of working with a diverse range of photoplethysmography device designs and intended applications, each of which could benefit from different approaches to signal acquisition and processing. It concludes that there are several potential benefits to establishing best practices. However, it is not yet clear whether it is possible to establish best practices which hold across the range of photoplethysmography device designs and applications.

Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: A review from VascAgeNet

The photoplethysmogram (PPG) signal is widely measured by clinical and consumer devices, and it is emerging as a potential tool for assessing vascular age. The shape and timing of the PPG pulse wave are both influenced by normal vascular aging, changes in arterial stiffness and blood pressure, and atherosclerosis. This review summarizes research into assessing vascular age from the PPG. Three categories of approaches are described: 1) those which use a single PPG signal (based on pulse wave analysis), 2) those which use multiple PPG signals (such as pulse transit time measurement), and 3) those which use PPG and other signals (such as pulse arrival time measurement). Evidence is then presented on the performance, repeatability and reproducibility, and clinical utility of PPG-derived parameters of vascular age. Finally, the review outlines key directions for future research to realize the full potential of photoplethysmography for assessing vascular age.

Deep learning-based photoplethysmography classification for peripheral arterial disease detection: a proof-of-concept study

Objective.A proof-of-concept study to assess the potential of a deep learning (DL) based photoplethysmography PPG ('DLPPG') classification method to detect peripheral arterial disease (PAD) using toe PPG signals.Approach.PPG spectrogram images derived from our previously published multi-site PPG datasets (214 participants; 31.3% legs with PAD by ankle brachial pressure index (ABPI)) were input into a pretrained 8-layer (five convolutional layers + three fully connected layers) AlexNet as tailored to the 2-class problem with transfer learning to fine tune the convolutional neural network (CNN).k-fold random cross validation (CV) was performed (fork = 5 andk = 10), with each evaluated over k training/validation runs. Overall test sensitivity, specificity, accuracy, and Cohen's Kappa statistic with 95% confidence interval ranges were calculated and compared, as well as sensitivities in detecting mild-moderate (0.5 ≤ ABPI < 0.9) and major (ABPI < 0.5) levels of PAD.Main results.CV with eitherk = 5 or 10 folds gave similar diagnostic performances. The overall test sensitivity was 86.6%, specificity 90.2% and accuracy 88.9% (Kappa: 0.76 [0.70-0.82]) (atk= 5). The sensitivity to mild-moderate disease was 83.0% (75.5%-88.9%) and to major disease was 100.0% (90.5%-100.0%).Significance.Substantial agreements have been demonstrated between the DL-based PPG classification technique and the ABPI PAD diagnostic reference. This novel automatic approach, requiring minimal pre-processing of the pulse waveforms before PPG trace classification, could offer significant benefits for the diagnosis of PAD in a variety of clinical settings where low-cost, portable and easy-to-use diagnostics are desirable.

ANALYSIS OF PHOTOPLETHYSMOGRAPHIC MORPHOLOGY IN SLEEP APNEA SYNDROME PATIENTS USING CURVE FITTING AND SUPPORT VECTOR MACHINE

This paper develops a time-saving, simple, and comfortable method for detecting Sleep Apnea Syndrome (SAS). Seventy SAS patients and 17 healthy persons were randomly selected in this study, and nine analytical parameters (i.e., [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] of healthy persons and SAS patients during five sleep stages (i.e., W, R, N1, N2, and N3) were obtained to construct a SAS classification model based on logarithmic normal analytical parameters using the Support Vector Machine (SVM) method to fit Photoplethysmographic (PPG) signals. The results show that there were no statistical differences among the five sleep stages for either the healthy or SAS patients. However, there were significant differences in the measured logarithmic normal analytical parameters between the healthy persons and the SAS patients in each of the five sleep stages. The accuracies of the SAS classification model were 95.00%, 90.00%, 84.00%, 94.67%, and 90.77%, corresponding to the five sleep stages, respectively. The SAS classification model based on the SVM method of logarithmic normal analysis parameters can achieve higher classification accuracy for each of the five sleep stages. It can be considered to collect the patient’s pulse wave during the awake period, but not necessarily during the sleep period to classify and identify the SAS; it provides an idea for a convenient and comfortable SAS detection.

Age-related changes in pulse risetime measured by multi-site photoplethysmography

OBJECTIVE

It is accepted that changes in the peripheral pulse waveform characteristics occur with ageing. Pulse risetime is one important feature which has clinical value. However, it is unclear how it varies across the full age spectrum from child to senior and for different peripheral measurement sites. The objectives of this study were to determine the association between age and pulse risetime characteristics over an 8-decade age range at the ears, fingers, and toes, and to consider effects arising from differences in systolic blood pressure (SBP), height and heart rate.

APPROACH

Multi-site photoplethysmography (MPPG) pulse waveforms were recorded non-invasively from the right and left ears, fingers, and toes of 304 normal healthy human subjects (range 6-87 years; 156 male and 148 female). SBP, height, and heart rate were also measured. Multi-site PPG pulse risetimes, and their site differences, were determined.

MAIN RESULTS

Univariate regression analysis showed positive correlations with risetime for age (ears, fingers and toes: + 0.8, + 1.9, and + 1.1 ms/year, respectively), SBP (+0.5, + 1.3, and + 0.9 ms/mmHg) and height (+0.5, + 1.2, and + 1.0 ms/cm), but with a clear inverse association with heart rate (-1.8, - 2.5, and - 1.6 ms min) (P < 0.0001). No significant differences between male and female subjects were found for pulse risetime.

SIGNIFICANCE

Normative multi-site PPG risetime characteristics have been defined in over 300 subjects and are shown to increase with age linearly up to the 8th decade. In contrast, we have shown that heart rate has a clear inverse relationship with risetime for all measurement sites.

Nurses' and patients' experiences and preferences of the ankle-brachial pressure index and multi-site photoplethysmography for the diagnosis of peripheral arterial disease: A qualitative study

Peripheral arterial disease is a global health problem, affecting around 20% of people aged over 60 years. Whilst ankle-brachial pressure index (ABPI) is regularly used for diagnosis, it has a number of limitations, which have presented a need for alternative methods of diagnosis. Multi-site photoplethysmography (MPPG) is one such method, but evidence of acceptability of both methods is lacking. This study aims to describe and compare preferences and experiences amongst nurses and patients of ABPI and MPPG use in primary care. We used qualitative research methods in the context of a clinical diagnostic study comparing ABPI with MPPG. Use of ABPI and MPPG by 13 nurses were observed with 51 patients across general practice surgeries in North-East England in 2015/16. Follow-up semi-structured interviews were conducted with 12 nurses and 27 patients. Data were thematically analysed. Two major themes were identified: (1) device preferences; (2) test discomfort and anxiety. There was a compelling preference for MPPG due to ease of use, speed of the test, patient comfort, and perceived device accuracy/objectivity. However some patients struggled to identify a preference, describing ambivalence to medical testing. ABPI was deemed uncomfortable and painful, particularly when the blood pressure cuff was inflated at the lower limbs. There was also evidence of anxiety amongst patients when their foot pulses were not identified using ABPI. Whilst ABPI is a non-invasive and routine procedure it was associated with a number of drawbacks in clinical practice. Nurses required considerable dexterity to employ the test, and it resulted in anxiety amongst some patients. Conversely, MPPG was deemed to be easier and quicker to use, and perceived to be less subjective. Should diagnostic accuracy and cost be comparable to ABPI, then the findings of this study suggest MPPG would be preferable to ABPI for patients as well as nurses.