A Review of Methods for Non-Invasive Heart Rate Measurement on Wrist

The Investigation of Bio-impedance Analysis at a Wrist Phantom with Two Pulsatile Arteries

PURPOSE

Bio-impedance analysis (BIA) has been widely investigated for hemodynamic monitoring. However, previous works rarely modelled two synchronously pulsatile arteries (representing the radial and ulnar arteries) in the wrist/forearm model. This work aims to clarify and quantify the influences of two pulsatile arteries on BIA.

METHODS

First, two blood-filled arteries were structured in a 3D wrist segment using the finite element method (FEM). Afterwards, an easy-to-produce two-arteries artificial wrist was fabricated with two components: gelatine-based surrounding tissue phantom and saline blood phantom. A syringe driver was utilised to constrict the arteries, and the impedance signals were measured using a Multi-frequency Impedance Analyser (MFIA).

RESULTS

Both simulation and experimental results demonstrated the non-negligible influences of the ulnar artery on the overall BIA, inducing unwanted resistance changes to the acquired signals from the radial artery. The phantom experiments revealed the summation of the individual resistance changes caused by a single pulsatile artery was approximately equal to the measured resistance change caused by two synchronously pulsatile arteries, confirming the measured impedance signal at the wrist contains the pulsatile information from both arteries.

CONCLUSION

This work is the first simulation and phantom investigation into two synchronously pulsatile arteries under BIA in the distal forearm, providing a better insight and understanding in the morphology of measured impedance signals. Future research can accordingly select either a small spacing 4-spot electrode configuration for a single artery sensing or a band electrode configuration for overall pulsatile arteries sensing. A more accurate estimation of blood volume change and pulse wave analysis (PWA) could help to develop cuffless blood pressure measurement (BPM).

Heartbeat Detection in Gyrocardiography Signals without Concurrent ECG Tracings

A heartbeat generates tiny mechanical vibrations, mainly due to the opening and closing of heart valves. These vibrations can be recorded by accelerometers and gyroscopes applied on a subject's chest. In particular, the local 3D linear accelerations and 3D angular velocities of the chest wall are referred to as seismocardiograms (SCG) and gyrocardiograms (GCG), respectively. These signals usually exhibit a low signal-to-noise ratio, as well as non-negligible amplitude and morphological changes due to changes in posture and the sensors' location, respiratory activity, as well as other sources of intra-subject and inter-subject variability. These factors make heartbeat detection a complex task; therefore, a reference electrocardiogram (ECG) lead is usually acquired in SCG and GCG studies to ensure correct localization of heartbeats. Recently, a template matching technique based on cross correlation has proven to be particularly effective in recognizing individual heartbeats in SCG signals. This study aims to verify the performance of this technique when applied on GCG signals. Tests were conducted on a public database consisting of SCG, GCG, and ECG signals recorded synchronously on 100 patients with valvular heart diseases. The results show that the template matching technique identified heartbeats in GCG signals with a sensitivity and positive predictive value (PPV) of 87% and 92%, respectively. Regression, correlation, and Bland-Altman analyses carried out on inter-beat intervals obtained from GCG and ECG (assumed as reference) reported a slope of 0.995, an intercept of 4.06 ms (R² > 0.99), a Pearson's correlation coefficient of 0.9993, and limits of agreement of about ±13 ms with a negligible bias. A comparison with the results of a previous study obtained on SCG signals from the same database revealed that GCG enabled effective cardiac monitoring in significantly more patients than SCG (95 vs. 77). This result suggests that GCG could ensure more robust and reliable cardiac monitoring in patients with heart diseases with respect to SCG.

Channel Intensity and Edge-Based Estimation of Heart Rate via Smartphone Recordings

Smartphones, today, come equipped with a wide variety of sensors and high-speed processors that can capture, process, store, and communicate different types of data. Coupled with their ubiquity in recent years, these devices show potential as practical and portable healthcare monitors that are both cost-effective and accessible. To this end, this study focuses on examining the feasibility of smartphones in estimating the heart rate (HR), using video recordings of the users’ fingerprints. The proposed methodology involves two-stage processing that combines channel-intensity-based approaches (Channel-Intensity mode/Counter method) and a novel technique that relies on the spatial and temporal position of the recorded fingerprint edges (Edge-Detection mode). The dataset used here included 32 fingerprint video recordings taken from 6 subjects, using the rear camera of 2 smartphone models. Each video clip was first validated to determine whether it was suitable for Channel-Intensity mode or Edge-Detection mode, followed by further processing and heart rate estimation in the selected mode. The relative accuracy for recordings via the Edge-Detection mode was 93.04%, with a standard error of estimates (SEE) of 6.55 and Pearson’s correlation r > 0.91, while the Channel-Intensity mode showed a relative accuracy of 92.75%, with an SEE of 5.95 and a Pearson’s correlation r > 0.95. Further statistical analysis was also carried out using Pearson’s correlation test and the Bland–Altman method to verify the statistical significance of the results. The results thus show that the proposed methodology, through smartphones, is a potential alternative to existing technologies for monitoring a person’s heart rate.

Development of Real-Time Cuffless Blood Pressure Measurement Systems with ECG Electrodes and a Microphone Using Pulse Transit Time (PTT)

Research has shown that pulse transit time (PTT), which is the time delay between the electrocardiogram (ECG) signal and the signal from a photoplethysmogram (PPG) sensor, can be used to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) without the need for a cuff. However, the LED of the PPG sensor requires the precise adjustment of both light intensity and light absorption rates according to the contact status of the light-receiving element. This results in the need for regular calibration. In this study, we propose a cuffless blood pressure monitor that measures real-time blood pressure using a microphone instead of a PPG sensor. The blood pulse wave is measured in the radial artery of the wrist using a microphone that can directly measure the sound generated by a body rather than sending energy inside the body and receiving a returning signal. Our blood pressure monitor uses the PTT between the R-peak of the ECG signal and two feature points of the blood pulse wave in the radial artery of the wrist. ECG electrodes and circuits were fabricated, and a commercial microelectromechanical system (MEMS) microphone was used as the microphone to measure blood pulses. The peak points of the blood pulse from the microphone were clear, so the estimated SBP and DBP could be obtained from each ECG pulse in real time, and the resulting estimations were similar to those made by a commercial cuff blood pressure monitor. Since neither the ECG electrodes nor the microphone requires calibration over time, the real-time cuffless blood pressure monitor does not require calibration. Using the developed device, blood pressure was measured three times daily for five days, and the mean absolute error (MAE) and standard deviation (SD) of the SBP and DBP were found to be 2.72 ± 3.42 mmHg and 2.29 ± 3.53 mmHg, respectively. As a preliminary study for proof-of-concept, these results were obtained from one subject. The next step will be a pilot study on a large number of subjects.

Linear and Non-Linear Heart Rate Variability Indexes from Heart-Induced Mechanical Signals Recorded with a Skin-Interfaced IMU

Heart rate variability (HRV) indexes are becoming useful in various applications, from better diagnosis and prevention of diseases to predicting stress levels. Typically, HRV indexes are retrieved from the heart's electrical activity collected with an electrocardiographic signal (ECG). Heart-induced mechanical signals recorded from the body's surface can be utilized to record the mechanical activity of the heart and, in turn, extract HRV indexes from interbeat intervals (IBIs). Among others, accelerometers and gyroscopes can be used to register IBIs from precordial accelerations and chest wall angular velocities. However, unlike electrical signals, the morphology of mechanical ones is strongly affected by body posture. In this paper, we investigated the feasibility of estimating the most common linear and non-linear HRV indexes from accelerometer and gyroscope data collected with a wearable skin-interfaced Inertial Measurement Unit (IMU) positioned at the xiphoid level. Data were collected from 21 healthy volunteers assuming two common postures (i.e., seated and lying). Results show that using the gyroscope signal in the lying posture allows accurate results in estimating IBIs, thus allowing extracting of linear and non-linear HRV parameters that are not statistically significantly different from those extracted from reference ECG.

Multi-systemic evaluation of biological and emotional responses to the Trier Social Stress Test: A meta-analysis and systematic review

Humans experience multiple biological and emotional changes under acute stress. Adopting a multi-systemic approach, we summarized 61 studies on healthy people's endocrinological, physiological, immunological and emotional responses to the Trier Social Stress Test. We found salivary cortisol and negative mood states were the most sensitive markers to acute stress and recovery. Biomarkers such as heart rate and salivary alpha-amylase also showed sensitivity to acute stress, but the numbers of studies were small. Other endocrinological (e.g., dehydroepiandrosterone), inflammatory (C-Reactive Protein, Interleukin-6) and physiological (e.g., skin conductance level) measures received modest support as acute stress markers. Salivary cortisol showed some associations with mood measures (e.g., state anxiety) during acute stress and recovery, and heart rate showed preliminary positive relationship with calmness ratings during response to TSST, but the overall evidence was mixed. While further research is needed, these findings provide updated and comprehensive knowledge on the integrated psychobiological response profiles to TSST.

Derivation of Bioimpedance Model Data Utilizing a Compact Analyzer and Two Capacitive Electrodes: A Forearm Example

The paper investigates the impacts of the selected electrical equivalent circuit model, measurement setup, and surrounding environment on the trustworthiness of electrical bioimpedance measurement and obtained model data in the human body. The influence of these constitutive components of the system on finding the model parameters is analyzed and illustrated with examples. The results based on experimental measurements on a forearm near the wrist are provided by employing the model, measurement setup, and novel 16-bit compact wireless impedance analyzer (CIA) according to the outcome of the analysis. The area near the wrist is of interest because of attempts to get cardiac-activity-related impedance changes. It is concluded that a two-electrode system with voltage excitation suits better for determining bioimpedance model parameters in the β dispersion area. The results obtained with the CIA and two capacitive bracelet electrodes on a left forearm were used for the fitting model parameters. Despite the small dimensions of 60 × 60 × 25 mm of the CIA reducing stray capacitance to 8 pF, it provides relative impedance magnitude measurement error below 0.3% and phase error below 0.2 ° in the 10 MHz range. Analysis of the model parameters allowed separation of the electrodes, skin, and internal tissue spectra and revealed the relative significance of model components at different frequencies.

Multi-ROI Spectral Approach for the Continuous Remote Cardio-Respiratory Monitoring from Mobile Device Built-In Cameras

Heart rate (HR) and respiratory rate (f_R) can be estimated by processing videos framing the upper body and face regions without any physical contact with the subject. This paper proposed a technique for continuously monitoring HR and f_R via a multi-ROI approach based on the spectral analysis of RGB video frames recorded with a mobile device (i.e., a smartphone's camera). The respiratory signal was estimated by the motion of the chest, whereas the cardiac signal was retrieved from the pulsatile activity at the level of right and left cheeks and forehead. Videos were recorded from 18 healthy volunteers in four sessions with different user-camera distances (i.e., 0.5 m and 1.0 m) and illumination conditions (i.e., natural and artificial light). For HR estimation, three approaches were investigated based on single or multi-ROI approaches. A commercially available multiparametric device was used to record reference respiratory signals and electrocardiogram (ECG). The results demonstrated that the multi-ROI approach outperforms the single-ROI approach providing temporal trends of both the vital parameters comparable to those provided by the reference, with a mean absolute error (MAE) consistently below 1 breaths·min^-1 for f_R in all the scenarios, and a MAE between 0.7 bpm and 6 bpm for HR estimation, whose values increase at higher distances.

Real-Time PPG Signal Conditioning with Long Short-Term Memory (LSTM) Network for Wearable Devices

This paper presents an algorithm for real-time detection of the heart rate measured on a person’s wrist using a wearable device with a photoplethysmographic (PPG) sensor and accelerometer. The proposed algorithm consists of an appropriately trained LSTM network and the Time-Domain Heart Rate (TDHR) algorithm for peak detection in the PPG waveform. The Long Short-Term Memory (LSTM) network uses the signals from the accelerometer to improve the shape of the PPG input signal in a time domain that is distorted by body movements. Multiple variants of the LSTM network have been evaluated, including taking their complexity and computational cost into consideration. Adding the LSTM network caused additional computational effort, but the performance results of the whole algorithm are much better, outperforming the other algorithms from the literature.

Non-Invasive Physiological Monitoring for Physical Exertion and Fatigue Assessment in Military Personnel: A Systematic Review

During operational activities, military personnel face extremely demanding circumstances, which when combined lead to severe fatigue, influencing both their well-being and performance. Physical exertion is the main condition leading to fatigue, and its continuous tracking would help prevent its effects. This review aimed to investigate the up-to-date progress on non-invasive physiological monitoring to evaluate situations of physical exertion as a pre-condition to fatigue in military populations, and determine the potential associations between physiological responses and fatigue, which can later result in decision-making indicators to prevent health-related consequences. Adhering to the PRISMA Statement, four databases (Scopus, Science Direct, Web of Science and PubMed) were used for a literature search based on combinations of keywords. The eligibility criteria focused on studies monitoring physiological variables through non-invasive objective measurements, with these measurements being developed in military field, combat, or training conditions. The review process led to the inclusion of 20 studies. The findings established the importance of multivariable assessments in a real-life context to accurately characterise the effects of military practices. A tendency for examining heart rate variables, thermal responses, and actigraphy measurements was also identified. The objectives and experimental protocols were diverse, but the effectiveness of non-invasive measurements in identifying the most fatigue-inducing periods was demonstrated. Nevertheless, no assessment system for standardised application was presented. Future work may include the development of assessment methods to translate physiological recordings into actionable information in real-time and mitigate the effects of fatigue on soldiers’ performance accurately.