The Principles of Hearable Photoplethysmography Analysis and Applications in Physiological Monitoring-A Review

Not long ago, hearables paved the way for biosensing, fitness, and healthcare monitoring. Smart earbuds today are not only producing sound but also monitoring vital signs. Reliable determination of cardiovascular and pulmonary system information can explore the use of hearables for physiological monitoring. Recent research shows that photoplethysmography (PPG) signals not only contain details on oxygen saturation level (SPO2) but also carry more physiological information including pulse rate, respiration rate, blood pressure, and arterial-related information. The analysis of the PPG signal from the ear has proven to be reliable and accurate in the research setting. (1) Background: The present integrative review explores the existing literature on an in-ear PPG signal and its application. This review aims to identify the current technology and usage of in-ear PPG and existing evidence on in-ear PPG in physiological monitoring. This review also analyzes in-ear (PPG) measurement configuration and principle, waveform characteristics, processing technology, and feature extraction characteristics. (2) Methods: We performed a comprehensive search to discover relevant in-ear PPG articles published until December 2022. The following electronic databases: Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, Scopus, Web of Science, and PubMed were utilized to conduct the studies addressing the evidence of in-ear PPG in physiological monitoring. (3) Results: Fourteen studies were identified but nine studies were finalized. Eight studies were on different principles and configurations of hearable PPG, and eight studies were on processing technology and feature extraction and its evidence in in-ear physiological monitoring. We also highlighted the limitations and challenges of using in-ear PPG in physiological monitoring. (4) Conclusions: The available evidence has revealed the future of in-ear PPG in physiological monitoring. We have also analyzed the potential limitation and challenges that in-ear PPG will face in processing the signal.

Challenges and Limitation Analysis of an IoT-Dependent System for Deployment in Smart Healthcare Using Communication Standards Features

The use of IoT technology is rapidly increasing in healthcare development and smart healthcare system for fitness programs, monitoring, data analysis, etc. To improve the efficiency of monitoring, various studies have been conducted in this field to achieve improved precision. The architecture proposed herein is based on IoT integrated with a cloud system in which power absorption and accuracy are major concerns. We discuss and analyze development in this domain to improve the performance of IoT systems related to health care. Standards of communication for IoT data transmission and reception can help to understand the exact power absorption in different devices to achieve improved performance for healthcare development. We also systematically analyze the use of IoT in healthcare systems using cloud features, as well as the performance and limitations of IoT in this field. Furthermore, we discuss the design of an IoT system for efficient monitoring of various healthcare issues in elderly people and limitations of an existing system in terms of resources, power absorption and security when implemented in different devices as per requirements. Blood pressure and heartbeat monitoring in pregnant women are examples of high-intensity applications of NB-IoT (narrowband IoT), technology that supports widespread communication with a very low data cost and minimum processing complexity and battery lifespan. This article also focuses on analysis of the performance of narrowband IoT in terms of delay and throughput using single- and multinode approaches. We performed analysis using the message queuing telemetry transport protocol (MQTTP), which was found to be efficient compared to the limited application protocol (LAP) in sending information from sensors.

Mapping of Spatiotemporal Auricular Electrophysiological Signals Reveals Human Biometric Clusters

Underneath the ear skin there are rich vascular network and sensory nerve branches. Hence, the 3D mapping of auricular electrophysiological signals can provide new biomedical perspectives. However, it is still extremely challenging for current sensing techniques to cover the entire ultra-curved auricle. Here, a 3D graphene-based ear-conformable sensing device with embedded and distributed 3D electrodes for full-auricle physiological monitoring is reported. As a proof-of-concept, spatiotemporal auricular electrical skin resistance (AESR) mapping is demonstrated for the first time, and human subject-specific AESR distributions are observed. From the data of more than 30 ears (both right and left ears), the auricular region-specific AESR changes after cycling exercise are observed in 98% of the tests and are clustered into four groups via machine learning-based data analyses. Correlations of AESR with heart rate and blood pressure are also studied. This 3D electronic platform and AESR-based biometrical findings show promising biomedical applications.

Evaluation of a Novel Ear Pulse Oximeter: Towards Automated Oxygen Titration in Eyeglass Frames

Current oxygen delivery modes lack monitoring and can be cumbersome for patients with chronic respiratory diseases. Integrating a pulse oximeter and nasal oxygen cannulas into eyeglasses would reduce the burden of current solutions. An ear pulse oximeter (OxyFrame) was evaluated on 16 healthy volunteers and 20 hypoxemic patients with chronic respiratory diseases undergoing a prespecified protocol simulating daily activities. Correlation, error, and accuracy root mean square error (ARMS) were calculated to compare SpO2 measured by OxyFrame, a standard pulse oximeter (MASIMO), and arterial blood gas analysis (aBGA). SpO2 measured by OxyFrame and MASIMO correlated strongly in volunteers, with low error and high accuracy (r = 0.85, error = 0.2 ± 2.9%, ARMS = 2.88%). Performances were similar in patients (r = 0.87, error 0 ± 2.5%, ARMS = 2.49% compared with MASIMO; and r = 0.93, error = 0.4 ± 1.9%, ARMS = 1.94% compared with aBGA). However, the percentage of rejected measurements was high (volunteers 77.2%, patients 46.9%). The OxyFrame cavum conchae pulse oximeter was successfully evaluated, and demonstrated accurate SpO2 measurements, compliant with ISO 80601-2-61:2017. Several reasons for the high rejection rate were identified, and potential solutions were proposed, which might be valuable for optimization of the sensor hardware.

Machine Learning Classification for Assessing the Degree of Stenosis and Blood Flow Volume at Arteriovenous Fistulas of Hemodialysis Patients Using a New Photoplethysmography Sensor Device

The classifier of support vector machine (SVM) learning for assessing the quality of arteriovenous fistulae (AVFs) in hemodialysis (HD) patients using a new photoplethysmography (PPG) sensor device is presented in this work. In clinical practice, there are two important indices for assessing the quality of AVF: the blood flow volume (BFV) and the degree of stenosis (DOS). In hospitals, the BFV and DOS of AVFs are nowadays assessed using an ultrasound Doppler machine, which is bulky, expensive, hard to use, and time consuming. In this study, a newly-developed PPG sensor device was utilized to provide patients and doctors with an inexpensive and small-sized solution for ubiquitous AVF assessment. The readout in this sensor was custom-designed to increase the signal-to-noise ratio (SNR) and reduce the environment interference via maximizing successfully the full dynamic range of measured PPG entering an analog-digital converter (ADC) and effective filtering techniques. With quality PPG measurements obtained, machine learning classifiers including SVM were adopted to assess AVF quality, where the input features are determined based on optical Beer-Lambert's law and hemodynamic model, to ensure all the necessary features are considered. Finally, the clinical experiment results showed that the proposed PPG sensor device successfully achieved an accuracy of 87.84% based on SVM analysis in assessing DOS at AVF, while an accuracy of 88.61% was achieved for assessing BFV at AVF.

Photoplethysmography for Quantitative Assessment of Sympathetic Nerve Activity (SNA) During Cold Stress

The differences in the degree of sympathetic nerve activity (SNA) over cutaneous blood vessels, although known to be more prominent in the periphery than the core vasculature, has not been thoroughly investigated quantitatively. Hence, two studies were carried out to investigate the differences in SNA between the periphery and the core during the cold pressor test (CPT) (right-hand immersion in ice water) and cold exposure (whole body exposed to cold air) using photoplethysmography (PPG). Two methods utilizing PPG, namely differential multi-site PTT measurements and low-frequency spectral analysis were explored for quantitative determination of SNA. Each study involved 12 healthy volunteers, and PPG signals were acquired from the right index finger (RIF), left index finger (LIF) (periphery) and the ear canal (core). During CPT, Pulse Transit Time (PTT) was measured to the respective locations and the mean percentage change in PTT during ice immersion at each location was used as an indicator for the extent of SNA. During cold exposure, the low-frequency spectral analysis was performed on the acquired raw PPGs to extract the power of the sympathetic [low-frequency (LF): 0.04–0.15 Hz] and parasympathetic components [high-frequency (HF): 0.15–0.4 Hz]. The ratio of LF/HF components was then used to quantify the differences in the influence of SNA on the peripheral and core circulation. PTT measured from the EC, and the LIF has dropped by 5 and 7%, respectively during ice immersion. The RIF PTT, on the other hand, has dropped significantly (P < 0.05) by 12%. During the cold exposure, the LF/HF power ratio at the finger has increased to 86.4 during the cold exposure from 19.2 at the baseline (statistically significant P = 0.002). While the ear canal LF/HF ratio has decreased to 1.38 during the cold exposure from 1.62 at baseline (P = 0.781). From these observations, it is evident that differential PTT measurements or low-frequency analysis can be used to quantify SNA. The results also demonstrate the effectiveness of the central auto-regulation during both short and long-term stress stimulus as compared to the periphery.

A Portable, Wireless Photoplethysomography Sensor for Assessing Health of Arteriovenous Fistula Using Class-Weighted Support Vector Machine

A portable, wireless photoplethysomography (PPG) sensor for assessing arteriovenous fistula (AVF) by using class-weighted support vector machines (SVM) was presented in this study. Nowadays, in hospital, AVF are assessed by ultrasound Doppler machines, which are bulky, expensive, complicated-to-operate, and time-consuming. In this study, new PPG sensors were proposed and developed successfully to provide portable and inexpensive solutions for AVF assessments. To develop the sensor, at first, by combining the dimensionless number analysis and the optical Beer Lambert’s law, five input features were derived for the SVM classifier. In the next step, to increase the signal-noise ratio (SNR) of PPG signals, the front-end readout circuitries were designed to fully use the dynamic range of analog-digital converter (ADC) by controlling the circuitries gain and the light intensity of light emitted diode (LED). Digital signal processing algorithms were proposed next to check and fix signal anomalies. Finally, the class-weighted SVM classifiers employed five different kernel functions to assess AVF quality. The assessment results were provided to doctors for diagonosis and detemining ensuing proper treatments. The experimental results showed that the proposed PPG sensors successfully achieved an accuracy of 89.11% in assessing health of AVF and with a type II error of only 9.59%.

An Antenna Proximity Sensor for Mobile Terminals Using Reflection Coefficient

This paper presents a new antenna proximity sensor for mobile terminals based on the measured reflection coefficient using a bidirectional coupler which is positioned between the main antenna and the front-end module. Using the coupled forward and reverse long-term evolution signals by the bidirectional coupler, the reflection coefficient looking into the antenna was calculated in the base-band processor. The measured reflection coefficients showed clear differences for both the types of objects, and the distances between the terminal and the objects. The proposed antenna proximity sensor showed a recognition distance that was approximately 5 mm longer than that of a conventional capacitive proximity sensor.

A multi-channel opto-electronic sensor to accurately monitor heart rate against motion artefact during exercise

This study presents the use of a multi-channel opto-electronic sensor (OEPS) to effectively monitor critical physiological parameters whilst preventing motion artefact as increasingly demanded by personal healthcare. The aim of this work was to study how to capture the heart rate (HR) efficiently through a well-constructed OEPS and a 3-axis accelerometer with wireless communication. A protocol was designed to incorporate sitting, standing, walking, running and cycling. The datasets collected from these activities were processed to elaborate sport physiological effects. t-test, Bland-Altman Agreement (BAA), and correlation to evaluate the performance of the OEPS were used against Polar and Mio-Alpha HR monitors. No differences in the HR were found between OEPS, and either Polar or Mio-Alpha (both p > 0.05); a strong correlation was found between Polar and OEPS (r: 0.96, p < 0.001); the bias of BAA 0.85 bpm, the standard deviation (SD) 9.20 bpm, and the limits of agreement (LOA) from -17.18 bpm to +18.88 bpm. For the Mio-Alpha and OEPS, a strong correlation was found (r: 0.96, p < 0.001); the bias of BAA 1.63 bpm, SD 8.62 bpm, LOA from -15.27 bpm to +18.58 bpm. These results demonstrate the OEPS to be capable of carrying out real time and remote monitoring of heart rate.