Photoplethysmography Sensor Research Articles

A review on wearable photoplethysmography sensors and their potential future applications in health care.

Photoplethysmography (PPG) is an uncomplicated and inexpensive optical measurement method that is often used for heart rate monitoring purposes. PPG is a non-invasive technology that uses a light source and a photodetector at the surface of skin to measure the volumetric variations of blood circulation. Recently, there has been much interest from numerous researchers around the globe to extract further valuable information from the PPG signal in addition to heart rate estimation and pulse oxymetry readings. PPG signal’s second derivative wave contains important health-related information. Thus, analysis of this waveform can help researchers and clinicians to evaluate various cardiovascular-related diseases such as atherosclerosis and arterial stiffness. Moreover, investigating the second derivative wave of PPG signal can also assist in early detection and diagnosis of various cardiovascular illnesses that may possibly appear later in life. For early recognition and analysis of such illnesses, continuous and real-time monitoring is an important approach that has been enabled by the latest technological advances in sensor technology and wireless communications. The aim of this article is to briefly consider some of the current developments and challenges of wearable PPG-based monitoring technologies and then to discuss some of the potential applications of this technology in clinical settings.

CHRV Uncovering Daily Stress Dynamics Using Bio-Signal from Consumer Wearables.

Knowing the dynamics of one's daily stress is essential to effective stress management in the context of smart and connected health. However, there lacks a practical and unobtrusive means to obtain real-time and longitudinal stress information. In this paper, we attempt to derive a convenient HRV-based (heart rate variability) biomarker named cHRV, which can be used to reliably reflect stress dynamics. cHRV's key advantage lies in its low maintenance and high practicality. It can be efficiently calculated only using data from photoplethysmography (PPG) sensors, the mainstream heart rate sensor embedded in most of the consumer wearables like Apple Watch. Benefiting from the proliferation of wearables, cHRV is ideal for day-to-day stress monitoring. To evaluate its feasibility and performance, we have conducted 14 in-lab controlled experiments. The result shows that the proposed cHRV has strong correlation with the stress dynamics (r > 0.95), therefore exhibits great potential for continuous stress assessment.

Development and optimization of a miniaturized fiber-optic photoplethysmographic sensor

Photoplethysmography (PPG) is a widely used technique for measuring blood oxygen saturation, commonly using an external pulse oximeter applied to a finger, toe, or earlobe. Previous research has demonstrated the utility of direct monitoring of the oxygen saturation of internal organs, using optical fibers to transmit light between the photodiode/light emitting diode and internal site. However, little research into the optimization and standardization of such a probe has yet been carried out. This research establishes the relationship between fiber separation distance and PPG signal, and between fiber core width and PPG signal. An ideal setup is suggested: 1000-μm fibers at a separation distance of 3 to 3.5 mm, which was found to produce signals around 0.35 V in amplitude with a low variation coefficient.

Design and Functional Testing of a Novel Blood Pulse Wave Velocity Sensor

The multiphotodiode array (MPA) is a novel transmission photoplethysmography (PPG) sensor to measure pulse wave velocity (PWV) in the finger. To validate the MPA, a setup was built to generate a red laser dot traveling over the MPA with known and constant scanning velocities. These scanning velocities were chosen to include speeds at least twice as high as those found in the normal range of PWV in healthy populations and were set at 12.9, 25.8, 36, or 46.7 m/s. The aim of this study was to verify the functionality of the MPA: performing local noninvasive PWV measurements. To illustrate the applicability of the MPA in clinical practice, an in vivo pilot study was conducted using the flow-mediated dilation (FMD) technique. The in vitro accuracy of the MPA was ±0.2%, 0.3%, 0.5%, and 0.6% at the applied scanning velocities. The MPA can measure PWVs with a maximum deviation of 3.0%. The in vivo pilot study showed a PWV before the FMD of 1.1±0.2 m/s. These results suggest that this novel MPA can reliably and accurately measure PWV within clinically relevant ranges and even well beyond.

Detection of atrial fibrillation using an earlobe photoplethysmographic sensor

Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world, associated with increased risk of thromboembolic events and an increased mortality rate. In addition, a significant portion of AF patients are asymptomatic. Current AF diagnostic methods, often including a body surface electrocardiogram or implantable loop recorder, are both expensive and invasive and offer limited access within the general community. Objective: We tested the feasibility of the detection of AF using a photoplethysmographic signal acquired from an inexpensive, non-invasive earlobe photoplethysmographic sensor. This technology can be implemented into wearable devices and would enable continuous cardiac monitoring capabilities, greatly improving the rate of asymptomatic AF detection. Approach: We conducted a clinical study of patients going through electrical cardioversion for AF treatment. Photoplethysmographic recordings were taken from these AF patients before and after their cardioversion procedure, along with recordings from a healthy control group. Using these recordings, cardiac beats were identified and the inter-systolic interval was calculated. The inter-systolic interval was used to calculate four parameters to quantify the heart rate variability indicative of AF. Receiver operating characteristic curves were used to calculate discriminant thresholds between the AF and non-AF cohorts. Main results: The parameter with the greatest discriminant capability resulted in a sensitivity and specificity of 90.9%. These results are comparable to expensive ECG-based and invasive implantable loop recorder AF detection methods. Significance: These results demonstrate that using a non-invasive earlobe photoplethysmographic signal is a viable and inexpensive alternative to ECG-based AF detection methods, and an alternative that could be invaluable in detecting subclinical AF.

Angle-selective optical filter for highly sensitive reflection photoplethysmogram.

We report an angle-selective optical filter (ASOF) for highly sensitive reflection photoplethysmography (PPG) sensors. The ASOF features slanted aluminum (Al) micromirror arrays embedded in transparent polymer resin, which effectively block scattered light under human tissue. The device microfabrication was done by using geometry-guided resist reflow of polymer micropatterns, polydimethylsiloxane replica molding, and oblique angle deposition of thin Al film. The angular transmittance through the ASOF is precisely controlled by the angle of micromirrors. For the mirror angle of 30 degrees, the ASOF accepts an incident light between - 90 to + 50 degrees and the maximum transmittance at - 55 degrees. The ASOF exhibits the substantial reduction of both the in-band noise of PPG signals over a factor of two and the low-frequency noise by three times. Consequently, this filter allows distinguishing the diastolic peak that allows miscellaneous parameters with diverse vascular information. This optical filter provides a new opportunity for highly sensitive PPG monitoring or miscellaneous optical tomography.

The PPG Physiological Signal for Heart Rate Variability Analysis

The photoplethysmography (PPG) sensor can be applied to measure the situation and function of human blood circulation. The PPG sensor is not only existed the characteristics of simple, convenient and low price but also easy non-invasive to measure physiological signal. The advantage of PPG signal is easy to measure from various sensing location. The physiological information of the clinical detection method is broadly implemented for such type. In this paper, we utilize "the green LED reflective" PPG sensor to capture physiological signals operated in static and exercise modes. Therefore, we adopted the short-term measurement in 5 min. Those captured signals are divided into five segments and 1 min for each segment. We calculated heart beats per minute and heart rate variability (HRV) operated in time domain analysis criteria. The related theory of short-time Fourier transform (STFT) combined with power spectral density (PSD) is implemented for finding HRV in frequency domain analysis. Then, we derived random process theory and the autocorrelation function which are verified the PPG measurement is stationary process or not. In the future experiment, we can compare the 24 h data with the previous results. Consequently, we apply the physical health status monitoring of long-term and short-term modes to observe subject varies of HRV and ANS after listening music concurrently.

Enhanced detection of sleep apnoea using heart-rate, respiration effort and oxygen saturation derived from a photoplethysmography sensor.

This paper presents a study on identifying sleep apnoea using the photoplethysmography (PPG) measurements, which is obtained from the SpO2 sensor. Using a database of polysomnogram (PSG) records of 52 patients, the heart rate and breathing effort information was derived from the PPG measurements and then features are extracted and processed by a classifier to detect one-minute epochs of sleep apnoea. The ground truth labels for the epochs were determined by trained technicians using the full PSG signal. Pulse oximetry (SpO2) measurements from the same sensor are also used in the classification process for comparison and in combination with the PPG results. The results show that both the heart rate and respiratory effort information derived from the PPG signal were able to detect apnoeic epochs with some success. The best classification performance of 87% for correctly labelling the epochs was obtained when the SpO2 features and the PPG features were combined.

The SPLENDID chewing detection challenge.

Monitoring of eating behavior using wearable technology is receiving increased attention, driven by the recent advances in wearable devices and mobile phones. One particularly interesting aspect of eating behavior is the monitoring of chewing activity and eating occurrences. There are several chewing sensor types and chewing detection algorithms proposed in the bibliography, however no datasets are publicly available to facilitate evaluation and further research. In this paper, we present a multi-modal dataset of over 60 hours of recordings from 14 participants in semi-free living conditions, collected in the context of the SPLENDID project. The dataset includes raw signals from a photoplethysmography (PPG) sensor and a 3D accelerometer, and a set of extracted features from audio recordings; detailed annotations and ground truth are also provided both at eating event level and at individual chew level. We also provide a baseline evaluation method, and introduce the "challenge" of improving the baseline chewing detection algorithms. The dataset can be downloaded from http: //dx.doi.org/10.17026/dans-zxw-v8gy, and supplementary code can be downloaded from https://github. com/mug-auth/chewing-detection-challenge.git.

Flexible wearable sensor nodes with solar energy harvesting.

Wearable sensor nodes have gained a lot of attention during the past few years as they can monitor and record people's physical parameters in real time. Wearable sensor nodes can promote healthy lifestyles and prevent the occurrence of potential illness or injuries. This paper presents a flexible wearable sensor system powered by an efficient solar energy harvesting technique. It can measure the subject's heartbeats using a photoplethysmography (PPG) sensor and perform activity monitoring using an accelerometer. The solar energy harvester adopts an output current based maximum power point tracking (MPPT) algorithm, which controls the solar panel to operate within its high output power range. The power consumption of the flexible sensor nodes has been investigated under different operation conditions. Experimental results demonstrate that wearable sensor nodes can work for more than 12 hours when they are powered by the solar energy harvester for 3 hours in the bright sunlight.

Simple and objective screening of major depressive disorder by heart rate variability analysis during paced respiration and mental task conditions.

Since objective biomarkers for major depressive disorder (MDD) are not readily available, clinical psychiatrists diagnose patients with MDD subjectively based on clinical interviews and diagnostic criteria. It often raises various concerns, including false responses by patients, subjective factors, and inexperience of the attendants leading to incorrect diagnosis. Here, we developed a self-monitoring system for simple and objective screening of MDD using a photoplethysmography (PPG) sensor and a 24-GHz microwave radar, which was based on the analysis of heart rate variability (HRV) during paced respiration and mental task conditions. In our previous study, we assessed the reactivity of HRV measurements during a mental task (random number generation) condition in patients with MDD and healthy control subjects. The HRV indices are less reactive in patients with MDD compared to healthy subjects during the mental task, which enabled us to identify the patients at risk for depression. In this study, the reactivity of HRV was measured not only in the mental task but also during paced respiration (i.e., 5-s inhalation and 5-s exhalation) conditions, thereby assessing more detailed autonomic nervous system (ANS) activity via HRV indices. To investigate the effect of paced respiration on MDD screening, we compared the ANS activity via HRV indices in with/without paced respiration conditions in 28 drug-naïve patients with MDD and 27 healthy control subjects. The result showed that ANS significantly activated during the paced respiration condition (p<;0.05). The sensitivity in detecting patients with MDD was 86% under paced respiration and mental task conditions, which was higher than the sensitivity (68%) under mental task condition alone.

Wearable PPG sensor based alertness scoring system.

Quantifying mental alertness in today's world is important as it enables the person to adopt lifestyle changes for better work efficiency. Miniaturized sensors in wearable devices have facilitated detection/monitoring of mental alertness. Photoplethysmography (PPG) sensors through Heart Rate Variability (HRV) offer one such opportunity by providing information about one's daily alertness levels without requiring any manual interference from the user. In this paper, a smartwatch based alertness estimation system is proposed. Data collected from PPG sensor of smartwatch is processed and fed to machine learning based model to get a continuous alertness score. Utility functions are designed based on statistical analysis to give a quality score on different stages of alertness such as awake, long sleep and short duration power nap. An intelligent data collection approach is proposed in collaboration with the motion sensor in the smartwatch to reduce battery drainage. Overall, our proposed wearable based system provides a detailed analysis of alertness over a period in a systematic and optimized manner. We were able to achieve an accuracy of 80.1% for sleep/awake classification along with alertness score. This opens up the possibility for quantifying alertness levels using a single PPG sensor for better management of health related activities including sleep.

A low-power high-sensitivity analog front-end for PPG sensor.

This paper presents a low-power analog front-end (AFE) photoplethysmography (PPG) sensor fabricated in 0.35 μm CMOS process. The AFE amplifies the weak photocurrent from the photodiode (PD) and converts it to a strong voltage at the output. In order to decrease the power consumption, the circuits are designed in subthreshold region; so the total biasing current of the AFE is 10 μ A. Since the large input DC photocurrent is a big issue for the PPG sensing circuit, we apply a DC photocurrent rejection technique by adding a DC current-cancellation loop to reject the large DC photocurrent up to 10 μA. In addition, a pseudo resistor is used to reduce the high-pass corner frequency below 0.5 Hz and Gm-C filter is adapted to reject the out-of-band noise higher than 16 Hz. For the whole sensor, the amplifier chain can achieve a total gain of 140 dBμ and an input integrated noise current of 68.87 pArms up to 16 Hz.

Development of an intraluminal intestinal photoplethysmography sensor.

Intestinal ischemia is a serious medical condition and can lead to life threatening sepsis. Currently, there are no reliable techniques available for directly monitoring intestinal viability for prolonged periods of time, and intraoperatively, the majority of the surgeons still rely on subjective methods, such as visual inspection to assess viability of the intestine. The development of an intraluminal optical sensor for monitoring intestinal viability is being proposed. The sensor will continuously monitor changes in blood volume and oxygen saturation. The developed reflectance photoplethysmography/pulse oximetry sensor comprises of two emitters (red and infrared) and a photodiode. A photoplethysmography processing and data acquisition system was also utilized. The prototype sensor was evaluated in a pilot study in the buccal mucosa of 12 healthy volunteers, given the locations similarity to the intestinal mucosa and its easy accessibility. Good quality photoplethysmography signals with high signal-to-noise ratio were acquired from the buccal mucosa in all the volunteers. Preliminary blood oxygen saturation values from the intraluminal sensor were in broad agreement with the standard finger pulse oximeter probes.

Application of infrared scanning of the neck muscles to control a cursor in Human-Computer Interface.

The feasibility of using infrared (IR) spectroscopy of the neck muscles in controlling a cursor in a 2-dimensional screen was assessed. The proposed technique utilizes two IR photoplethysmography sensors (λ = 940nm) to monitor the morphological changes of the Scalene and Sternocleidomastoid muscles. Since the reflection of the light has valuable information about the type of contraction, the direction of the movement (right/left, up/down) can be simply derived using two sensors. A MATLAB platform was developed in which a cursor moves using the recorded signal. Three scenarios of high/low sensitivity and joystick mode were tested. The results from 4 different healthy subjects shows the feasibility of control in terms of throughput, overshoot, and path efficiency.

New method of sudomotor function measurement to detect microvascular disease and sweat gland nerve or unmyelinated C fiber dysfunction in adults with retinopathy

BackgroundDiabetes-associated microvascular complications such as retinopathy and neuropathy often lead to end-organ and tissue damage. Impaired skin microcirculation often precedes the detection of other advanced diabetic complications. The ANS-1 system contains a redesigned sympathetic skin response (ANS-1 SSR) device that measures sudomotor function, a photoplethysmography sensor, and a blood pressure device to comprehensively assess cardiac autonomic neuropathy and endothelial dysfunction. The purpose of this study was to determine the relationships between the ANS-1 SSR amplitude measured at the: (a) negative electrode (Nitric Oxide [NO] Sweat Peak) with microvascular diseases and associated vascular blood markers and (b) positive electrode (iSweat Peak) with C fiber function.MethodsAll participants (healthy controls n = 50 and retinopathy patients n = 50) completed the ANS-1 system evaluation and a basic sociodemographic and medical history questionnaire, including a quality of life measure (SF-36). A small sample of blood was drawn to determine levels of homocysteine, blood urea nitrogen (BUN), C-reactive protein (CRP), and fibrinogen. Symptoms of peripheral foot neuropathy were assessed with a scale from 1 (none) to 10 (the worst). We used Spearman rank correlations, independent samples t-tests, and receiver operating characteristic curves to determine the specificity and sensitivity of the NO Sweat Peak as a potential screening marker of retinopathy.ResultsThe ANS-1 System Cardiometabolic Risk Score and all indicators of quality of life on the SF-36, other than Emotional Role Functioning, were significantly worse in the retinopathy patients. The sudomotor response marker NO Sweat Peak had a sensitivity of 88% and a specificity of 68% (Area Under the Curve = 0.81, p < 0.0001) to detect retinopathy. The NO Sweat Peak response marker inversely correlated with BUN (ρ = −0.41, p < 0.0001), homocysteine (ρ = −0.44, p < 0.0001), fibrinogen (ρ = −0.41, p < 0.0001), the Cardiac Autonomic Neuropathy score (ρ = −0.68, p < 0.0001), and the heart rate variability Total Power (ρ = −0.57, p < 0.0001), and it positively correlated with the Photoplethysmography Index (PTGi; ρ = 0.53 p < 0.0001). The ANS-1 system sudomotor response marker iSweat Peak inversely correlated with the severity of symptoms on the peripheral neuropathy scale (ρ = −0.56, p < 0.0001).ConclusionThe results of the study show that this new method of measuring sympathetic skin response should be useful for detecting the earliest manifestations of microvascular disease and symptoms of C fiber dysfunction.

Pulse Rate Measurement During Sleep Using Wearable Sensors, and its Correlation with the Menstrual Cycle Phases, A Prospective Observational Study

An affordable, user-friendly fertility-monitoring tool remains an unmet need. We examine in this study the correlation between pulse rate (PR) and the menstrual phases using wrist-worn PR sensors. 91 healthy, non-pregnant women, between 22–42 years old, were recruited for a prospective-observational clinical trial. Participants measured PR during sleep using wrist-worn bracelets with photoplethysmographic sensors. Ovulation day was estimated with “Clearblue Digital-Ovulation-urine test”. Potential behavioral and nutritional confounders were collected daily. 274 ovulatory cycles were recorded from 91 eligible women, with a mean cycle length of 27.3 days (±2.7). We observed a significant increase in PR during the fertile window compared to the menstrual phase (2.1 beat-per-minute, p < 0.01). Moreover, PR during the mid-luteal phase was also significantly elevated compared to the fertile window (1.8 beat-per-minute, p < 0.01), and the menstrual phase (3.8 beat-per-minute, p < 0.01). PR increase in the ovulatory and mid-luteal phase was robust to adjustment for the collected confounders. There is a significant increase of the fertile-window PR (collected during sleep) compared to the menstrual phase. The aforementioned association was robust to the inter- and intra-person variability of menstrual-cycle length, behavioral, and nutritional profiles. Hence, PR monitoring using wearable sensors could be used as one parameter within a multi-parameter fertility awareness-based method.

Dynamic Detection of PPG(Photo Plethysmography) Signals Using Mems Self Designed Sensors

Objectives: To design Micro Electro Mechanical System (MEMS) based sensor with novel design considerations to detect/ identify PhotoPlethysmography(PPG) signals when the person is in dynamic mode. To simulate the above design using COMSOL Multiphysics and evaluate the results with the existing methodologies. Methods/Statistical Analysis: A MEMS based sensor with a four T-shaped cantilever with middle load hanged structure is designed in COMSOL Multiphysics and after attaining the required resonant frequency of PPG, by using capacitive actuation technique the acquired mechanical signals are evaluated and digitised. Findings: PPG sensors optically recognize changes in tidal volume of blood (i.e., changes in the force of light perceived) in the vascular bed of little scale tissue by reflection from or transmission through the tissue. If the person is in static mode it is very easy to detect blood flow rate. If suppose the person is not in rest, i.e., may be dynamic we will observe many variations in blood flow rate. So it is very difficult to estimate the blood flow rate. This Patch type device easily measures the blood flow rate. Application/Improvements: Mainly these type of devices are useful to detect variations in blood flow rate in human whether in static condition or in dynamic condition. In future we are planning to develop implantable sensors with reporting system to doctor in particular intervals of time. This may be helpful to doctors to cater the needs of human society at right time whenever it is needed

Flexible heartbeat sensor for wearable device

We demonstrate a flexible strain-gauge sensor and its use in a wearable application for heart rate detection. This polymer-based strain-gauge sensor was fabricated using a double-sided fabrication method with polymer and metal, i.e., polyimide and nickel-chrome. The fabrication process for this strain-gauge sensor is compatible with the conventional flexible printed circuit board (FPCB) processes facilitating its commercialization. The fabricated sensor showed a linear relation for an applied normal force of more than 930 kPa, with a minimum detectable force of 6.25Pa. This sensor can also linearly detect a bending radius from 5mm to 100mm. It is a thin, flexible, compact, and inexpensive (for mass production) heart rate detection sensor that is highly sensitive compared to the established optical photoplethysmography (PPG) sensors. It can detect not only the timing of heart pulsation, but also the amplitude or shape of the pulse signal. The proposed strain-gauge sensor can be applicable to various applications for smart devices requiring heartbeat detection.

SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals.

Although wrist-type photoplethysmographic (hereafter referred to as WPPG) sensor signals can measure heart rate quite conveniently, the subjects’ hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise.