Photoplethysmography Technique Research Articles

Heart rate variability modulates memory function in a virtual task

Heart rate variability (HRV) is considered one of the most relevant indicators of physical well-being and relevant biomarker for preventing cardiovascular risks. More recently, a growing amount of research has tracked an association between HRV and cognitive functions (i.e., attention). Research is still scarce on spatial orientation, a basic capability in our daily lives. It is also an important indicator of memory performance, and its malfunctioning working as an early sign of dementia. In this study, a total of 43 female students (M Age = 18.76; SD = 2.02) were measured in their lnRMSSD using the photoplethysmography technique with the Welltory smartphone app. They were also tested in their spatial memory with The Boxes Room, a virtual navigation test. Measures of physical activity were obtained with the International Physical Activity Questionnaire (IPAQ). Correlation analyses and repeated measures ANOVA were performed, comparing participants with high / low lnRMSSD in their spatial performance. Results showed that, at an equal level of physical activity, participants with a higher lnRMSSD were more effective in the early trials of The Boxes Room, being more precise in estimating the correct position of the stimuli. Moreover, a subsequent simple linear regression showed that a higher lnRMSSD was related to a smaller number of errors at the beginning of the spatial task. Overly, these results outline the relationship between HRV and navigation performance in early stages of processing, where the environment is still unknown and the situation is more demanding.

Branchial Motion Assessment in Abalone Using Photoplethysmography

The abalone, a gastropod mollusk belonging to the Haliotidae family, has been a subject of research with a focus on its behavior in response to various environmental factors, including temperature fluctuations and hypoxia. While existing studies have primarily explored abalone behavior in relation to environmental changes, there remains a significant amount to be discovered regarding their behavioral patterns. There are studies in which recordings of readings have been made (indirect methods) of the heart rate of the abalone since it is believed that it is one of the parameters affected due to the changes in temperature and dissolved oxygen in its environment. The main disadvantage of some procedures to measure heart rate in abalone is that an incision is made near the heart, through this incision the measurement is made using the photoplethysmography technique. Another parameter that is affected by these changes (temperature and hypoxia) is respiratory rate. Temperature is an important factor that can reduce oxygen solubility and thus alter abalone respiration; so in this work, we propose to use the technique of photoplethysmography: a method involving the measurement of changes in volume within a biological organ, which offers a unique opportunity to monitor these changes. We adapt this technique to measure gill movements in abalone, a critical indicator of respiratory activity in these marine mollusks. By utilizing a pulse oximetry setup and synchronous detection, we establish an innovative approach to gill movement analysis. This approach employs a pulse train and synchronous demodulation technique, the train of pulses is guided through an optical fiber to one of the respiratory orifices of the abalone to effectively detect and analyze the gill movements of abalone. Additionally, the role of frequency of its gill movements is highlighted as another critical variable of interest within the broader study of Haliotis fulgens, shedding light on the multifaceted nature of abalone behavior and responses to their surroundings. This research contributes to a deeper understanding of abalone physiology and behavior, offering insights into their adaptation to changing environmental conditions.

Baseline plethysmographic signal of arterial pulse during polysomnography: a potential insight into venous return and its clinical implications

Pulse oximetry is based on the technique of photoplethysmography. It is known that the pulsatile arterial component can provide a range of information, such as arterial blood oxygen saturation, the response to fluid infusion, and the nature of respiratory events during sleep. Until now, little importance has been given to the venous component of the plethysmographic signal, which is non-pulsatile but subject to volumetric variations in relation to changes in endopleuric pressure.Respiratory-induced intensity variations contained in the baseline of the photoplethysmographic signal have been well documented. We hypothesize that in polysomnography, variations in the baseline of the plethysmographic signal (referred to as the BPS, baseline plethysmographic signal) reflect variations in venous return induced by respiration, and that these variations present morphological aspects that could have clinical utility in the interpretation of polysomnographic signals, particularly regarding respiratory effort associated with respiratory events, their nature (central or obstructive), and the hemodynamic alterations associated with them.This article presents a selection of polysomnographic traces and images with physiopathological considerations that support our hypothesis.

Anti-motion imaging photoplethysmography via self-adaptive multi-ROI tracking and selection

Objective. The imaging photoplethysmography (IPPG) technique allows people to measure heart rate (HR) from face videos. However, motion artifacts caused by rigid head movements and nonrigid facial muscular movements are one of the key challenges. Approach. This paper proposes a self-adaptive region of interest (ROI) pre-tracking and signal selection method to resist motion artifacts. Based on robust facial landmark detection, we split the whole facial skin (including the forehead, cheeks, and chin) symmetrically into small circular regions. And two symmetric sub-regions constitute a complete ROI. These ROIs are tracked and the motion state is simultaneously assessed to automatically determine the visibility of these ROIs. The obscured or invisible sub-regions will be discarded while the corresponding symmetric sub-regions will be retained as available ROIs to ensure the continuity of the IPPG signal. In addition, based on the frequency spectrum features of IPPG signals extracted from different ROIs, a self-adaptive selection module is constructed to select the optimum IPPG signal for HR calculation. All these operations are updated per frame dynamically for the real-time monitor. Results. Experimental results on the four public databases show that the IPPG signal derived by our proposed method exhibits higher quality for more accurate HR estimation. Compared with the previous method, metrics of the evaluated HR value on our approach demonstrates superior or comparable performance on PURE, VIPL-HR, UBFC-RPPG and MAHNOB-HCI datasets. For instance, the RMSEs on PURE, VIPL-HR, and UBFC-RPPG datasets decrease from 4.29, 7.62, and 3.80 to 4.15, 3.87, and 3.35, respectively. Significance. Our proposed method can help enhance the robustness of IPPG in real applications, especially given motion disturbances.

Adapting remote photoplethysmography for Indonesian subjects: an examination of diverse rPPG techniques

Vital sign measurements are essential in intensive care patients, such as in the ICU or emergency department, and also for newborns or prenatal babies. The duty nurse usually monitors these vital signs by manually writing down the patient's condition on a large piece of paper in front of the patient's room. The lack of nurses can hinder the process of monitoring patient vital signs. However, since the COVID-19 pandemic, people have limited contact with their surroundings, making measuring vital signs with contact uncomfortable and unhygienic. The typical non-contact method for measuring heart rate is the remote photoplethysmography (rPPG) technique. In this study, we proposed to assess the performance of various rPPG algorithms on the Indonesian subjects dataset. The algorithms used are CHROM, GREEN, ICA, LGI, PBV, PCA, and POS on 70 pieces of data. Based on the test results with three types of evaluation metrics, namely MAE (Mean Absolute Error), RMSE (Root Mean Square Error), and Bland Altman, it is found that the measurement results with the best performance POS algorithm with a low prediction error rate with the resulting MAE value of 2.59 and RMSE of 4.65.

IOT Platform for Vital Signs Detection Using Node Microcontroller

It is possibly possible to stop the spread of the coronavirus by utilizing IoT technology to monitor the vital signs of patients. As a consequence, a proposal has been developed for an efficient and affordable remote monitoring system that measures vital signs. This system makes use of a temperature sensor, a dual heart rate and oxygen level sensor, a temperature sensor, two microcontrollers, a UNO as a power supply, and a Node as the main controller. The vital signs are monitored in a non-invasive manner using photoplethysmography technique, and the information is transmitted wirelessly to the individual involved via the Blynk platform, making use of a Wi-Fi device. It also demonstrates how the system can connect to the internet anywhere in the world, which enables its utilization in a variety of clinical trials.

Opportunistic screening for atrial fibrillation using a photoplethysmography technique in geriatric patients, a preliminary analysis of the Dutch-GERAF Study

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Happitech. Provided the mobile photoplethysmography application Background Undiagnosed atrial fibrillation (AF) poses a great risk for stroke in high-risk patients. Different screening strategies have been developed to identify these patients. Geriatric patients often have multimorbidity, and are to be considered a population at very high risk for development of AF and cardiovascular events, but may have less access to wearable screening devices. Purpose The Dutch multicentre study into opportunistically screening of Geriatric patients for Atrial Fibrillation using a PPG smartphone App (the Dutch-GERAF Study) applies opportunistic screening to ambulatory geriatric patients, to identify new cases of AF and optimize stroke prevention. This preliminary analysis describes the feasibility and willingness to use of a smart phone photoplethysmography (PPG) application in an ambulatory cohort. Method Analysis of participation data and PPG use, of all included patients aged 70 years or older within the GERAF study. The GERAF study is a multicentre study on opportunistic screening for AF using a PPG application among patients visiting a geriatric outpatient clinic. All patients without a pacemaker/ICD, severe tremor, or severe dementia were invited to participate, during a 6 months screening period. Patients were stimulated to use the application at home on their own mobile device at least 3 times within 6 months following inclusion. If preferred by the patient, the application was installed on the mobile device of a spouse or other family member. The PPG signals were analysed using the commercially available algorithm. Results Data on PPG use for patients was available for 568 patients. Of these, 122 (21.5%) had a history of AF, and therefore not included in this analysis. Data on PPG use was analysed of 446 patients, 240 were female (54%), average age was 79 ± 5.5 years, the oldest patient was 95 years old. In total, patients performed 2232 PPG recordings within 6 months, in which a sufficient signal was acquired in 1874 (84%) recordings. The signal appeared sufficient at the first attempt in 79.3% of the recordings. In case of a failed first attempt, 60.5% of the patients immediately performed at least one extra recording until sufficient signal quality was achieved, and overall 61.2% of the patients performed at least 2 PPG recordings. Only 69 (15.5%) patients who consented to participation, did not perform any recordings. Conclusion This analysis of preliminary GERAF PPG data shows that very old subjects (with a mean age of almost 80 years) are willing and able to use eHealth applications on a mobile device. Most PPG recordings were of sufficient signal quality, and more than half of the patients were willing to perform repeated recordings.

Correlation between retinal vein pulse amplitude, estimated intracranial pressure, and postural change

Spaceflight associated neuro-ocular syndrome (SANS) is common amongst astronauts on long duration space missions and is associated with signs consistent with elevated cerebrospinal fluid (CSF) pressure. Additionally, CSF pressure has been found to be elevated in a significant proportion of astronauts in whom lumbar puncture was performed after successful mission completion. We have developed a retinal photoplethysmographic technique to measure retinal vein pulsation amplitudes. This technique has enabled the development of a non-invasive CSF pressure measurement apparatus. We tested the system on healthy volunteers in the sitting and supine posture to mimic the range of tilt table extremes and estimated the induced CSF pressure change using measurements from the CSF hydrostatic indifferent point. We found a significant relationship between pulsation amplitude change and estimated CSF pressure change (p < 0.0001) across a range from 2.7 to 7.1 mmHg. The increase in pulse amplitude was highest in the sitting posture with greater estimated CSF pressure increase (p < 0.0001), in keeping with physiologically predicted CSF pressure response. This technique may be useful for non-invasive measurement of CSF pressure fluctuations during long-term space voyages.

Noncontact Blood Pressure Estimation Using BP-Related Cardiovascular Knowledge: An Uncalibrated Method Based on Consumer-Level Camera

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective</i> : The tiny change of skin color, caused by heartbeat, can be captured with consumer-level cameras by using the imaging photoplethysmography (iPPG) technique, offering a non-contact way of extracting pulse signals. The pulse signals have been demonstrated to contain information of human physiological characteristics and have been used for blood pressure (BP) estimation in recent years. According to BP-related cardiovascular knowledge, this paper presents a new method for BP estimation based on the iPPG pulse signals, featured by incorporating cardiovascular characteristics including heart rate (HR), stroke volume (SV), elasticity of vessel walls (EVW), and peripheral vascular resistance (PVR). Correlations between the systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), and the cardiovascular characteristics are extracted, which facilitates the selection of pulse features consistent with BP properties. Based on the selected features, two Bayesian neural network (BNN) models are constructed for the estimation of SBP and DBP respectively, where the machine learning (ML) uncertainty of the estimation is also evaluated. This method is uncalibrated that means it can work without additional information except for the videos from the camera. The proposed method has been tested on 220 patients with a history of cardiovascular diseases. Errors of the BP estimation are 9±13 (MAE±STD) mmHg for SBP, 7±10 (MAE±STD) mmHg for DBP, and the ML uncertainty of the estimation indicates reliability of the proposed method.

Towards a Machine Learning-Based Digital Twin for Non-Invasive Human Bio-Signal Fusion

Human bio-signal fusion is considered a critical technological solution that needs to be advanced to enable modern and secure digital health and well-being applications in the metaverse. To support such efforts, we propose a new data-driven digital twin (DT) system to fuse three human physiological bio-signals: heart rate (HR), breathing rate (BR), and blood oxygen saturation level (SpO2). To accomplish this goal, we design a computer vision technology based on the non-invasive photoplethysmography (PPG) technique to extract raw time-series bio-signal data from facial video frames. Then, we implement machine learning (ML) technology to model and measure the bio-signals. We accurately demonstrate the digital twin capability in the modelling and measuring of three human bio-signals, HR, BR, and SpO2, and achieve strong performance compared to the ground-truth values. This research sets the foundation and the path forward for realizing a holistic human health and well-being DT model for real-world medical applications.

Hypertension Detection Based on Photoplethysmography Signal Morphology and Machine Learning Techniques

In our modern digitalized world, hypertension detection represents a key feature that enables self-monitoring of cardiovascular parameters, using a wide range of smart devices. Heart rate and blood oxygen saturation rate are some of the most important ones, easily computed by wearable products that are provided by the photoplethysmography (PPG) technique. Therefore, this low-cost technology has opened a new horizon for health monitoring in the last decade. Another important parameter is blood pressure, a major predictor for cardiovascular characterization and health related events. Analyzing only PPG signal morphology and combining the medical observation with machine learning (ML) techniques, this paper develops a hypertension diagnosis tool, named the ANC Test™. During the development process, distinguishable characteristics have been observed among certain waveforms and certain types of patients that leads to an increased confidence level of the algorithm. The test was enchanted by machine learning models to improve blood pressure class detection between systolic normotensive and hypertensive patients. A total of 359 individual recordings were manually selected to build reference signals using open-source available databases. During the development and testing phases, different ML models accuracy of detecting systolic hypertension scored in many cases around 70% with a maximum value of 72.9%. This was resulted from original waveform classification into four main classes with an easy-to-understand nomenclature. An important limitation during the recording processing phase was given by a different PPG acquisition standard among the consulted free available databases.

MAMDANI-TYPE FUZZY CONTROLLER TO DETERMINE BLOOD PRESSURE LEVELS BY REFLECTION PPG

This document presents the design of a fuzzy control system applying photoplethysmography techniques to generate input data and Mamdani-type fuzzy logic for its processing and generation of system output values, through which blood pressure levels can be indicated based on the parameters recommended by the medical area for an adult person. The integral design of the system includes the acquisition of the input signals, obtained through the reflection photoplethysmography technique, by means the optical coupling between IR light-emitting diodes and photo receptors, which allow the flow of current proportional to the amount of reflected light by blood, which is related to the flow level of the cardiac cavities, derived from the systolic and diastolic stages of the cardiac cycle. The fuzzy control was designed with two inputs and one output of 5 triangular membership functions each using the hardware description language (VHDL), the fuzzification, inference, aggregation and defuzzification stages were designed using the COSAA method. The response values ​​obtained using the Test Bench tool of ISE Design suite were compared with theoretical values ​​and with values ​​obtained with the Fuzzy Logic Designer tool of Matlab, the results showed an excellent approximation.

Assessment of the systolic rise time by photoplethysmography in peripheral arterial diseases: a comparative study with ultrasound Doppler.

AimsPeripheral arterial disease (PAD) is a major public health burden requiring more intensive population screening. Ankle brachial index (ABI) using arm and ankle cuffs is considered as the reference method for the detection of PAD. Although it requires a rigorous methodology by trained operators, it remains time-consuming and more technically difficult in patients with diabetes due to mediacalcosis. Techniques based on the study of hemodynamic, such as the systolic rise time (SRT), appear promising but need to be validated. We retrospectively compared the reliability and accuracy of SRT using a photoplethysmography (PPG) technique to the SRT measured by ultrasound doppler (UD) in PAD patients diagnosed with the ABI (137 patients, 200 lower limbs).Methods and resultsThere was a significant correlation between SRT measured with UD (SRTud) compared with that with PPG (SRTppg, r = 0.25; P = 0.001). Best correlation was found in patients without diabetes (r = 0.40; P = 0.001). Bland and Altman analysis showed a good agreement between the SRTud and SRTppg. In contrast, there was no significant correlation between UD and PPG in diabetes patients. Furthermore, patients with diabetes exhibited a significant increase of SRTppg (P = 0.02) compared with patients without diabates but not with the SRTud (P = 0.18). The SRTppg was significantly linked to the arterial velocity waveforms, the type of arterial lesion but not vascular surgery revascularization technique.ConclusionThis monocentric pilot study shows that SRT measured with the PPG signal reliably correlates with SRT recorded with UD. The PPG is an easy to use technique in the hand of non-expert with a potential interest for general screening of PAD, especially in diabetes patients, due to its ease to use.

Accuracy of Heart Rate Measurement by the Fitbit Charge 2 During Wheelchair Activities in People With Spinal Cord Injury: Instrument Validation Study.

BackgroundHeart rate (HR) is an important and commonly measured physiological parameter in wearables. HR is often measured at the wrist with the photoplethysmography (PPG) technique, which determines HR based on blood volume changes, and is therefore influenced by blood pressure. In individuals with spinal cord injury (SCI), blood pressure control is often altered and could therefore influence HR accuracy measured by the PPG technique.ObjectiveThe objective of this study is to investigate the HR accuracy measured with the PPG technique with a Fitbit Charge 2 (Fitbit Inc) in wheelchair users with SCI, how the activity intensity affects the HR accuracy, and whether this HR accuracy is affected by lesion level.MethodsThe HR of participants with (38/48, 79%) and without (10/48, 21%) SCI was measured during 11 wheelchair activities and a 30-minute strength exercise block. In addition, a 5-minute seated rest period was measured in people with SCI. HR was measured with a Fitbit Charge 2, which was compared with the HR measured by a Polar H7 HR monitor used as a reference device. Participants were grouped into 4 groups—the no SCI group and based on lesion level into the <T5 (midthoracic and lower) group, T5-T1 (high-thoracic) group, and >T1 (cervical) group. Mean absolute percentage error (MAPE) and concordance correlation coefficient were determined for each group for each activity type, that is, rest, wheelchair activities, and strength exercise.ResultsWith an overall MAPEall lesions of 12.99%, the accuracy fell below the standard acceptable MAPE of –10% to +10% with a moderate agreement (concordance correlation coefficient=0.577). The HR accuracy of Fitbit Charge 2 seems to be reduced in those with cervical lesion level in all activities (MAPEno SCI=8.09%; MAPE<T5=11.16%; MAPET1−T5=10.5%; and MAPE>T1=20.43%). The accuracy of the Fitbit Charge 2 decreased with increasing intensity in all lesions (MAPErest=6.5%, MAPEactivity=12.97%, and MAPEstrength=14.2%).ConclusionsHR measured with the PPG technique showed lower accuracy in people with SCI than in those without SCI. The accuracy was just above the acceptable level in people with paraplegia, whereas in people with tetraplegia, a worse accuracy was found. The accuracy seemed to worsen with increasing intensities. Therefore, high-intensity HR data, especially in people with cervical lesions, should be used with caution.

A Facial-Image-Based Blood Pressure Measurement System Without Calibration

Blood pressure (BP) is a major vital sign that is highly correlated with human health. However, for decades BP measurement has involved a cuff, which can be uncomfortable and even carry the risk of infection. The remote photoplethysmography (rPPG) technique has gained great success in heart rate monitoring for tackling these problems. Some studies integrate the pulse transit time (PTT) into the system and ask users put their hand beside their face. A calibration procedure is usually needed for such methods. Other methods measure the BP with several rPPG waveform features or the whole rPPG segment. However, these methods remain inconvenient, and few methods evaluate their algorithm with large enough datasets to be effective. Hence, we propose a calibration-free and facial-image-based blood pressure measurement system which takes a deep neural network as the backbone. Seven physiological indicators are derived and fed into the network together with three-channel rPPG signals. We also propose a novel strategy for training the models. For a fair and complete evaluation, we compile three massive datasets containing 1143 subjects and 2291 data. These datasets were collected under different scenarios: one from a school laboratory and the other two from two hospital departments. On the complete testing data, the proposed approach yields an MAE of 11.54 mmHg for SBP and 8.09 mmHg for DBP, outperforming previous studies.

Simultaneous Estimation of Instantaneous Heart and Respiratory Rates using Image Photoplethysmography on a Single Smartphone.

Heart rate (HR) and respiratory rate (RR) are very important physiological variables useful to evaluate the cardiorespiratory system. At present, there is a great interest by the general population in knowing their health status, quickly and easily. Accordingly, several approaches have been proposed to achieve that goal. In this study, the simultaneous estimation of the instantaneous HR and RR values was achieved by the image photoplethysmography (iPPG) technique, in the contact mode directly implemented in a smartphone. iPPG results were compared with those obtained using specialized biomedical sensors such as the electrocardiogram and the respiratory effort band. Performance evaluation included three different respiratory maneuvers in five healthy volunteers. The absolute mean error for instantaneous HR and RR estimations reached 0.94 ± 0.28 beats per minute and 0.40 ± 0.11 breaths per minute, respectively. The mean correlation index was 0.69 ± 0.14 between the iPPG-derived respiratory signal and the respiratory effort reference signal.Clinical Relevance- These results appear to indicate that the contact iPPG method implemented directly on the smartphone is a good option, accessible to the common population to estimate the instantaneous HR and RR values outside specialized clinical environments, e.g., in the point-of-contact office.

Contactless video-based photoplethysmography technique comparison investigating pulse transit time estimation of arterial blood pressure.

Non-contact measurement of physiological vital signs, such as blood pressure (BP), by video-based photoplethysmography (vPPG) is a potential means for remote health monitoring. However, the signal-to-noise ratio of cardiovascular signals within the vPPG is very low. This study investigates the potential of BP estimation from vPPG. In 10 healthy volunteers (4 females, 28 ± 7 years), continuous electrocardiogram, finger BP and video of the face and palm of the hand were recorded. BP was varied by isometric hand grip exercise and leg ischemia. Four vPPG methods were compared: (i) averages of the green (GREEN) color intensity; (ii) the best linear combination of color channels using independent component analysis (ICA); (iii) a linear combination of chrominance-based (CHROM) signal by standardizing the skin color profile; (iv) plane orthogonal to the skin tone (POS) as vPPG signal. These were applied to 14 regions of interest (ROIs) on the face and 5 ROIs on the palm. Pulse transit time (PTT) between ROIs, for all permutations, were calculated and the correlation with BP quantified. A significant, negative PTT-BP correlation was defined as success. A maximum success rate of 80% was achieved, occurring for the GREEN, POS and ICA methods only for specific ROIs within the face, but not for any permutation using the hand. These results indicate that the use of vPPG for estimation of BP will be challenging. A combination of different vPPG methods and within-face ROIs may yield useful information.

Heart rate variability responses determined by photoplethysmography in people with spinal cord injury

This study aims to evaluate whether the photoplethysmography technique (PPG) can be used as an instrument to assess heart rate variability (HRV) parameters in individuals with spinal cord injury (SCI). Twenty-one subjects (2 tetraplegic, 18 paraplegic and 1 diplegic) participated in the study (37.5 ± 12.3 years, 58.8 ± 15.7 kg and 160.0 ± 21.4 cm). HRV parameters were obtained simultaneously for 5 min with PPG (HRV-4 trainingTM mobile app) and Polar H7TM. Although significant correlations between PPG and Polar H7TM have been found for SDNN (r = 0.508, p = 0.019), RMSSD (r = 0.456, p = 0.038), PNN50 (r = 0.620, p = 0.003) and LF (r = 0.456, p = 0.038) parameters, Bland-Altman analyses of agreement showed that PPG overestimates all HRV parameters (SDNN, Bias = 47.61 ms; RMSSD, Bias = 17.91 ms; PNN50, Bias = 11.52%; LF, Bias = 0.22 Hz; and HF, Bias = 0.18 Hz). These results indicate that the HRV parameters have a correlation between the methods evaluated by PPG and PolarH7TM. However, caution is recommended when using PPG for HRV analysis in people with spinal cord injury, as the results indicate an overestimation of HRV parameters.

Heart Rate Measurement

In recent studies on non-invasive techniques for heart rate measurements, various Computer Vision algorithms based on Ballistocardiography (BCG) have been employed. This method captures minimal head motions from facial videos, that result from the pumping of blood to the head through the carotid arteries, at each cardiac cycle. We move towards BCG because the conventional technique of Photoplethysmography (PPG) fails to yield accurate results from facial video in case of skin color variations. This paper proposes an improved system for accurately measuring the heart rate and heart rate variability to infer important information about the subject’s health. It incorporates functions from the Dlib toolkit, which provide robust face detection along with facial landmark tracking. Relevant data in terms of facial video and ground truth was acquired from 5 test subjects, in 3 states - sitting, standing and post exercise. The system exhibits promising results when validated using a wearable smart watch with inbuilt heart rate sensor.

Cardiac events monitoring

Cardiac events recorders have been developed in order to record the heart rhythm during symptoms such as palpitations or presyncope, to first make a diagnosis, and subsequently drive the treatment strategy. In other circumstances, they can be also used in asymptomatic patients (to record silent atrial fibrillation for instance). Because they are non-invasive, potentially cost-saving and relatively easy to use, the external rhythm recording devices have shown some great advances in the last years, spreading from photoplethysmographic technique to real ECG reconstruction. Technological advances in the field of microelectronics, as well as in the field of data transmission have contributed to their widespread use in cardiology. The trend for miniaturization was also expanded to the implantable recorders. This paper will review will review advantages and limitations of the different existing available well-established recording devices, as well as the last technological developments in terms of ECG recordings.