PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring—A Comprehensive Review
Abstract
:1. Introduction
2. Methodology
2.1. Paper Selection Criteria
2.2. PPG, Bioimpedance, Heart Rate, Wearable Device
- Type of signals: papers providing a detailed overview on bioimpedance and PPG measurement as well as features extractable form PPG and bioimpedance signals.
- Signal processing: papers listing relevant signal pre-processing techniques, such as filtering and overall data cleaning were included.
- Algorithms: papers describing heart rate detection and motion artifact removal algorithms.
- Devices: wearable device technologies based on PPG and bioimpedance. Papers dealing with stationary bioimpedance analyzers were excluded from the selection of candidate papers.
- Devices: papers focusing on bioimpedance measurement with stationary analyzers which, by nature, are not wearable devices. Also, papers describing PPG measurement through a finger clip and not an actual wearable device were not included.
- Monitored parameter: papers with an emphasis on monitoring bioparameters other than the ones associated with heart rate were dismissed.
- Experiments on animals: papers dealing with various experiments on animals were not included.
3. Results
3.1. Article Selection Process
3.2. Analyzed Articles
3.3. Temporal Scope and Distribution
4. Biological Signals for Wearable-Based HR
4.1. Electrical Bioimpedance (EBI)
- Systolic phase—the left ventricle of the heart beats to push blood through the arteries and blood volume increases. Initial decrease in impedance.
- Diastolic phase—the heart rests between beats and blood volume leaves the sensing area. Impedance increases.
- The second, smaller impedance decrease due the arrival of the reflection wave.
- The final impedance increase as the reflected blood volume leaves the sensing area.
EBI Waveform Features for Cardiovascular Monitoring
- Amplitude features
- The peak amplitude
- The foot amplitude
- The total EBI amplitude
- Area features
- The area between the diastolic point and the maximum slope point
- The area between the maximum slope point and the systolic point
- The area between the diastolic and the systolic point
- Time features
- Slope transit time is the time difference between the systolic and diastolic points
- Inter-beat interval is the time difference between two consecutive diastolic points
- EBI Signal derivatives
4.2. Photoplethysmography
PPG Waveform Features
- Characteristic PPG waveform points
- Pulse wave transit time (PTT)
- Pulse wave arrival time (PAT)
- PPG Signal derivatives
5. Heart Rate Monitoring
5.1. Signal Pre-Processing
5.1.1. Sources of Noise
5.1.2. Noise Filtering
5.1.3. Motion Artifact Removal
- Without supplementary signals
- Sensitivity to noise in the recorded signals.
- Aliasing [9].
- Trend uncertainty [9].
- The method and its variations are based on empirical and heuristic procedures; thus, interpretability is its weakness [84].
- EMD and especially EEMD have a significant computational cost. A fast version of EEMD (FEEMD) was proposed in [85] lessening the computational burden of the original EEMD.
- With supplementary acceleration signals
- Low computation complexity allows for a real-time signal processing. The drawbacks are as follows [9]:
- The correlation between the reference signal and the motion spectrum has to be sufficiently high, which is impossible in practice.
- Very high computational requirements.
- Limitations
- The methods are too reliant on expertly tuned multiple parameters and heuristic thresholds, which prevents generalization [102].
- The commonly used acceleration signal in MA removal is not a direct MA source but is rather a superposition of movement of a body part and the gravitational acceleration [103]. Therefore, the recorded acceleration signals cannot be directly translated in to the movement of limbs during exercises.
5.2. Heart Rate Estimation
5.3. Algorithms
- TROIKA
- Signal decomposition: This part aims to denoise and sparsify the spectra of PPG signals. SSA helps to remove motion artifacts (MA) frequency components and to sparsify the spectrum coefficients. The time series is decomposed into oscillatory components and noise as shown below:
- Sparse signal reconstruction (SSR): SSR aims at obtaining the sparsest high-resolution spectrum of the PPG signal, which is robust against noise, using FOCUSS algorithms. This process is essential for distinguishing the heart rate component from other dominant frequencies caused by MAs. The basic model of SSR can be written as follows:Here, is a known basis matrix, the vector is the observed signal, is an unknown solution vector and is an unknown noise vector. The goal is to find the sparsest vector by minimizing the square of the norm of (−).
- Spectral peak tracking: The final step involves selecting and verifying the correct spectral peak corresponding to the heart rate. This involves an initialization phase where users are required to minimize motion, followed by the selection of spectral peaks within certain ranges, and a verification process that employs decision mechanisms to ensure the correct peak is chosen despite potential motion artifact interference.
- JOSS
- Joint sparse signal recovery: Using the MMV model (multiple measurement vector), the spectra of the PPG signal and acceleration signals are jointly estimated. The MMV model provides unique solutions and better error performance compared to the SMV model (single measurement vector).
- Spectral subtraction: A simple spectral subtraction method is applied to remove the spectral peaks of MA from the PPG spectrum. The maximum values of spectral coefficients in the acceleration spectra are subtracted from the corresponding values in the PPG spectrum. Thresholding is then performed to zero out coefficients below a certain level.
- Spectral peak tracking: Compared to the TROIKA algorithm, a new phase of peak discovery is introduced. After the initialization of the peak, it is refined and validated based on previous estimates and the expected range of heart rate changes.
6. Wearable Devices for Heart Rate Monitoring
6.1. PPG-Based Devices
6.2. Bioimpedance-Based Devices
- Electrode–skin interface (see Section 5.1.1).
- Characteristics of the alternating current of excitation. The important parameters are the frequency [156] and amplitude of excitation current.
- The measured bioimpedance signal is affected by the type of tissues and the physiological and physiochemical variations that occur within the tissues [156].
- The difficulty of acquiring a signal of high enough quality due to:
- 1.1.
- Patient-related issues—spontaneous movements of the patient, micro-movements that are not controllable, disorders of heart rhythm [156]. Movement with an amplitude above a certain threshold may cause MAs severely degrading reliability of the estimated bioparameters.
- 1.2.
- Inherently low signal-to-noise ratio of the technique [157] thereby establishing a reliable, long-lasting and firm contact interface between the electrodes and the skin is of prime importance [18]. On a hardware/software level, signal pre-processing steps involving noise filtration with filters and algorithms is necessary. One solution of obtaining a signal with strength above a background noise is performing measurements of the systolic and diastolic impedance variations close to the ascending thoracic aorta [157]. This approach, however, is limited by the empirical nature and sensitivity to electrode positioning and calibration inherent in the method [158].
- Changes in the baseline impedance levels when direct comparisons to a healthy state are no longer valid due to physiologic and pathophysiologic conditions, such as pregnancy, obesity, gas or fluid pleural effusion, chronic congestive heart failure with pulmonary edema, or when there is severe aortic valve disease or modified mechanical properties of the arterial tree [157].
7. Discussion and Concluding Remarks
- PPG vs EBI. PPG-based devices are a comparatively mature technology with some commercial brands, while very few attempts have been made to design a wearable device for HR estimation based on EBI. However, compared to the PPG technology, devices based on EBI have the potential to provide a more accurate HR estimation due to the fact that skin tone affects properties of light absorption and reflection diminishing the reliability of PPG-based devices. On the other hand, wearable devices based on EBI face challenges related to power consumption and signal noise originating from a variety of sources including electromagnetic interference from electric circuitry, electrode size and material, levels of skin hydration and quality of contact between skin and surface of electrodes. Advances in flexible, self-adhesive materials have shown potential in enhancing sensor–skin contact, thereby improving signal quality. However, the integration of flexible substrates with rigid electronic components poses significant challenges, often leading to connection failures. Therefore, ongoing research into material science and engineering is vital to overcoming these hurdles. All in all, EBI-based devices have the potential to become mainstream once the electrode properties are carefully tailored to the general user.
- Design of wearable devices. The development of low-power consumption strategies, such as those employing multi-frequency EBI measurements and modifications in hardware design, for example, demodulation and current driver blocks, have been essential in extending the operational life of wearable devices. Despite these advancements, achieving a balance between power efficiency and high signal-to-noise ratio remains a critical area for further research and development. Until a breakthrough in power source development is achieved, the key question concerning wearable device progress is energy efficiency. The energy demand is primarily related to the computation complexity—a trade-off with edge computing, i.e., the speed of data representation and battery duration has to be considered. Solutions exist, in the form of analog edge computing-based machine learning [167], or relying on an analog circuit design [168].
- Motion artifact removal. Accurate HR estimation can easily be sabotaged by motion artifacts (MA). Various noise filtering and MA removal techniques have been employed to mitigate these issues, including band-pass filters and advanced algorithms that utilize supplementary signals for more precise correction. The algorithms most commonly employed in the literature include the ones based on signal decomposition—wavelet transform, empirical mode decomposition and Fourier decomposition. Then the user discards the components that are not related to the signal itself. Another group of methods is based on signal subtraction in the frequency domain (spectral subtraction). This approach, however, requires an estimation of heart rate frequency and detects deviations from it. This may pose a problem, since the assumption of a normal heart rate to be 1 Hz may not be generalized over all cases.
- Heart rate estimation. The mainstream algorithms for HR estimation are TROIKA and JOSS and others directly derived from these two. The reason is that these algorithms have proven to be fairly accurate and effective. Nonetheless, the implementation of TROIKA and JOSS into a wearable device is a challenging task, since these algorithms rely on heavy mathematical computations. Hence, the processing unit and, again, power consumption of the wearable device, pose challenges.
- Potential for a more complete hemodynamic monitoring. Apart from the HR estimation, PPG and EBI techniques have remarkable potential to also provide information on numerous other hemodynamic parameters. Such data, including the hemodynamic feature points, denoting, e.g., left ventricular ejection time (LVET) or pulse transit time (PTT), can be utilized already in performing prognostic and diagnostic tasks. However, for such operations, where the differentiation of the signal is performed, the signal quality is of paramount importance. Indeed, up to four derivatives of the PPG signal [169] and first derivative of EBI signal [170] have been studied in order to evaluate the heart and cardiovascular health.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
ACC | Accelerometer |
ADC | Analog-to-Digital Converter |
ADT | Adaptive Detection Threshold |
ASIC | Application Specific Integrated Circuit |
BioZ | Bioimpedance |
BLE | Bluetooth Low Energy |
BVP | Blood Volume Pulse |
CVD | Cardiovascular disease |
ECG | Electrocardiogram |
EBI | Electrical Bioimpedance |
EDA | Electrodermal Activity Sensor |
EEMD | Extended Empirical Mode Decomposition |
EMD | Empirical Mode Decomposition |
FD | Fourier Decomposition |
FIBF | Fourier Intrinsic Band Functions |
GBP | Gain-Bandwidth Product |
Gyro | Gyroscope |
HR | Heart Rate |
ICA | Independent Component Analysis |
IMU | Inertial Measurement Unit |
IoT | Internet of Things |
IR | Infra Red |
IIR | Infinite Impulse Response |
LCD | Liquid Crystal Display |
LED | Light-Emitting Diode |
LSM | Least Mean Squares |
LVET | Left Ventricular Ejection Time |
MA | Moving Artifacts |
MCU | Microcontrol Unit |
MMV | Multiple Measurement Vector |
NLSM | Normalized Least Mean Squares |
NTC | Negative Temperature Coefficient Sensor |
PCA | Principal Component Analysis |
PAT | Pulse Arrival Time |
PCB | Printed Circuit Board |
PPG | Photoplethysmography |
PTT | Pulse wave Transit Time |
RLS | Recursive Least Squares |
SMV | Single Measurement Vector |
SNR | Signal-to-Noise Ratio |
SoC | System On Chip |
SS | Spectral Subtraction |
SSR | Sparse Signal Reconstruction |
Term | Thermometer |
Tempe | Temperature |
WT | Wavelet Transform |
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Monitoring parameter | Heart rate variability | Blood pressure | Arterial stiffness |
Cardiovascular risk factor | Arrhythmia | Hypertension causing heart attack and stroke | Hypertension, diabetes |
Source | Specifications |
---|---|
[71] | PPG sensor—Reflective, Maxim MAX30101 + photodetector + 525 nm LED, sample rate 125 Hz; MIMU sensor—STMicroelectronics LSM9DS1, sample rate 125 Hz; Microcontroller—MCU: STMicroelectronics STM32L476; Analog-digital conversion—Analog Devices 7606 CE; Case dimensions—4.5 mm × 4.0 mm, 1.5 mm (resin, 3D printed); Circuit system—digital + analog for signal amplification and filtering; Signal transmission—wireless (2.4 GHz, Ashining: AS69-T20) |
[72] | PPG sensor—Reflective, SFH 7070, Osram opto-semiconductors, Regensburg, Germany; IMU sensor—LSM6DSMTR, STMicroelectronics, Geneva, Switzerland; Analog-digital conversion—analog frontend MAX86141 (Maxim integrated, San Jose, CA, USA); Microcontroller—STM32F413CGU6, STMicroelectronics, Geneva, Swiss, clock rate 25 MHz; User interface—switch, 1.3 inch liquid crystal display (LCD, KWH013ST03-F01, FORMIKE, Shenzhen, China) |
Device | Sensors | MCU | Dimensions (mm) | Apps |
---|---|---|---|---|
Apple Watch series 9 [132] | Electric Heat; PPG; Temp; Compass; Altimeter; ACC; Gyro; Ambient light; | S9-SiP-64-bit 4-core | 45 × 38 × 10.7 | ECG; Cycle Tracking with retrospective ovulation estimates; Heart Rate; High and low HR notifications; Irregular rhythm notifications; Medications; Mindfulness; Noise; Sleep, including sleep stages |
Fitbit charge 5 [133] | 3-axis ACC; PPG; vibration motor;relative SpO2; ECG; EDA | Arm 32-bit Cortex M-4 | 36.7 × 22.7 × 11.2 | ECG; HR Monitoring: Continuously tracks HR; including irregular heart rhythm notifications and high and low HR notifications; Stress Detection: EDA-measure stress; Sleep Tracking; SpO2; Skin Temp; Health Metrics Dashboard; Exercise Tracking |
Garmin fenix 6 pro [134] | PPG; BAROMETRIC ALTIMETER; GPS; COMPASS; Gyro; ACC; Term | NXP Kinetis | 47 × 47 × 14.7 | Wrist-based HR and Pulse Ox; Advanced Sleep Monitoring; Stress Tracking; Body Battery Energy Monitoring; Respiration Tracking; Hydration Tracking; Women’s Health Tracking; Performance Metrics; |
Oura Ring gen3 [135] | IR, Green, Red LEDs; ACCs; NTC; Gyro | PSoC 6 BLE | - | Nighttime resting HR; HR variability; skin Temp; respiratory rate; Detailed sleep analysis; 24/7 HR tracking (Daytime, Nighttime, Workout); Integration with third-party health and wellness apps like Strava; Advanced Temp monitoring; SpO2; Cycle Insights; Pregnancy Insights; Daytime Stress; Resilience; Dynamic activity goals with Automatic Activity Detection; Weekly, monthly, quarterly, yearly, and anniversary reports |
Whoop Band 4.0 [136] | ACC and Gyro; PPG; SpO2; Temp | Nordic nRF52840 SoC | 43 × 28 × 10 | Live HR; skin Temp; blood oxygen saturation; resting heart rate; HR variability; respiratory rate |
Polar H10 Strip [137] | ECG; ACC | BLE | 34 × 65 × 10 | Full ECG chart; HR (HR), Average HR, Lowest HR, Peak HR; R - R; Calories |
Firstbeat Sport Sensor [138] | ECG; ACC | BLE | 54 × 38 × 7.7 | HR (HR), Average HR, Lowest HR, Peak HR; Time in Highest Zone; Excess Post-Exercise Oxygen Consumption (EPOC); Training Impulse (TRIMP), TRIMP/min; Training Effect (aerobic, anaerobic) |
Zephyr Bioharness 3 [139] | ECG; ACC; Temp; | BLE | 28 (Diam) × 7 | HR in beats per minute (BPM); Breathing Rate; Posture; Activity Level; Skin Temp; Peak Acceleration; Breathing Wave Amplitude; ECG Amplitude and Noise; HR RR Values |
Source | Measurement Mode | Accuracy | Range () | Power Active | Power Sleep | Size (cm) | Sense Time (s) |
---|---|---|---|---|---|---|---|
[13] | MF | 1.5 % | 100–1000 | 53 mW | 15.7 W | 3 × 1.8 × 0.6 | 0.05 |
[165] | MF | 0.5 | R < 54; 0.7< < 5 | 14.4 mW | 0.9 mW | 4.8 × 3 × 2 | - |
[166] | SF | 0.1 % | 0–120 | 750 A at 2.8 V | - | - | - |
[155] | MF | 4.5 % | 0–10,000 | 3 mA at 1.8 V | 8 A at 1.8 V | 4 × 4.6 × 1.3 | - |
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Lapsa, D.; Janeliukstis, R.; Metshein, M.; Selavo, L. PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring—A Comprehensive Review. Appl. Sci. 2024, 14, 7451. https://doi.org/10.3390/app14177451
Lapsa D, Janeliukstis R, Metshein M, Selavo L. PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring—A Comprehensive Review. Applied Sciences. 2024; 14(17):7451. https://doi.org/10.3390/app14177451
Chicago/Turabian StyleLapsa, Didzis, Rims Janeliukstis, Margus Metshein, and Leo Selavo. 2024. "PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring—A Comprehensive Review" Applied Sciences 14, no. 17: 7451. https://doi.org/10.3390/app14177451