Spectro-Temporal Electrocardiogram Analysis for Noise-Robust Heart Rate and Heart Rate Variability Measurement

A Wireless Miniature Injectable Device With Memory-Assisted Backscatter for Multimodal Animal Physiological Monitoring

This paper introduces a wirelessly powered multimodal animal physiological monitoring application-specific integrated circuit (ASIC). Fabricated in the 180 nm process, the ASIC can be integrated into an injectable device to monitor subcutaneous body temperature, electrocardiography (ECG), and photoplethysmography (PPG). To minimize the device size, the ASIC employs a miniature receiver (Rx) coil for wireless power receiving and data communication through a single inductive link operating at 13.56 MHz. We propose a folded L-shape Rx coil with improved coupling to the transmitter (Tx) coil, even in the presence of misalignment, and enhanced quality factor. The ASIC functions alternatively between recording and sleeping modes, consuming 2.55 µW on average. For PPG measurements, a reflection-type PPG sensor illuminates an LED with tunable current pulses. A current-input analog frontend (AFE) amplifies the current of a photodiode (PD) with 30.8 pARMS current input-referred noise (IRN). The ECG AFE captures ECG signals with a configurable gain of 45-80 dB. The temperature AFE achieves 0.02 °C inaccuracy within a sensing range between 27-47 °C. The AFE outputs are sequentially digitized by a 10-bit successive approximation register (SAR) analog-to-digital converter (ADC) with an effective number of bits (ENOB) of 8.79. To improve the reliability of data transmission, we propose a memory-assisted backscatter scheme that stores ADC data in an off-chip memory and transmits it when the coupling condition is stable. This scheme achieves a package loss rate (PLR) lower than 0.2% while allowing 24-hour data storage. The device's functionality has been evaluated by in vivo experiments.

Clinical Evaluation of the ECG Noise Extraction Tool as a Component of ECG Analysis Algorithms Evaluation

OBJECTIVE

The aim of this work is to demonstrate the performance of the ECG noise extraction tool (ECGNExT) which provides estimates of ECG noise that are not significantly different from the inherent noise in an ECG generated by motion artifacts and other sources. In addition, this paper elaborates on use of ECGNExT in an algorithm evaluation context comparing two QRS detection algorithms.

METHODS

140 simultaneous pairs of clean ECGs and ECGs corrupted with motion-induced noise from 29 participants under five different and separate motion conditions were collected and analyzed. Estimates of the noise component of the ECGs recorded with noise were obtained using ECGNExT and were then added to the clean ECGs yielding estimated ECGs with noise. Root mean squared error (RMSE) between the recorded and estimated ECGs with noise was calculated for temporal comparison, and band powers of the signals were calculated for spectral comparison.

RESULTS

A t-test revealed that the mean RMSE < 150-microvolts with p-value < 0.001 and, and equivalence tests showed that the band powers of the two ECGs were statistically equivalent with .

CONCLUSION

ECGNExT can reliably estimate the underlying ECG noise while preserving temporal and spectral features.

SIGNIFICANCE

We previously proposed ECGNExT as a component of ECG analysis algorithm testing during noise conditions and reported its performance based on simulated ECG data. This work provides additional support of the performance and functionality of the ECGNExT algorithm from a study with pairs of simultaneously recorded ECGs with and without noise from human subjects.

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review

Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.

Automated Multi-Wavelength Quality Assessment of Photoplethysmography Signals Using Modulation Spectrum Shape Features

Photoplethysmography (PPG) is used to measure blood volume changes in the microvascular bed of tissue. Information about these changes along time can be used for estimation of various physiological parameters, such as heart rate variability, arterial stiffness, and blood pressure, to name a few. As a result, PPG has become a popular biological modality and is widely used in wearable health devices. However, accurate measurement of various physiological parameters requires good-quality PPG signals. Therefore, various signal quality indexes (SQIs) for PPG signals have been proposed. These metrics have usually been based on statistical, frequency, and/or template analyses. The modulation spectrogram representation, however, captures the second-order periodicities of a signal and has been shown to provide useful quality cues for electrocardiograms and speech signals. In this work, we propose a new PPG quality metric based on properties of the modulation spectrum. The proposed metric is tested using data collected from subjects while they performed various activity tasks contaminating the PPG signals. Experiments on this multi-wavelength PPG dataset show the combination of proposed and benchmark measures significantly outperforming several benchmark SQIs with improvements of 21.3% BACC (balanced accuracy) for green, 21.6% BACC for red, and 19.0% BACC for infrared wavelengths, respectively, for PPG quality detection tasks. The proposed metrics also generalize for cross-wavelength PPG quality detection tasks.

Dynamical behavior analysis of the heart system by the bifurcation structures

The functioning of the heart rhythm can exhibit a wide variety of dynamic behaviours under certain conditions. In the case of rhythm disorders or cardiac arrhythmias, the natural rhythm of the heart is usually involved in the sinoatrial node, the atrioventricular node, the atria of the carotid sinus, etc. The study of heart related disorders requires an important analysis of its rhythm because the regularity of cardiac activity is conditioned by a large number of factors. The cardiac system is made up of a combination of nodes ranging from the sinus node, the atrioventricular node to its Purkinje bundles, which interact with each other via communicative aspects. Due to the nature of their respective dynamics, the above are treated as self-oscillating elements and modelled by nonlinear oscillators. By modelling the cardiac conduction system as a model of three nonlinear oscillators coupled by delayed connections and subjected to external stimuli depicting the behavior of a pacemaker, its dynamic behavior is studied in this paper by nonlinear analysis tools. From an electrocardiogram (ECG) assessment, the heart rhythm reveals normal and pathological rhythms. Three forms of ventricular fibrillation, ventricular flutter, ventricular tachycardia and atrial fibrillation are observed. The results are confirmed by the respective maximum Lyapunov exponents. Considering the cardiac nodes as microchips, using microcontroller simulation technology, the cardiac conduction system was modelled as a network of four ATmega 328P microcontrollers. A similarity with the results obtained numerically can be observed.

Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note

Wearable devices are burgeoning, and applications across numerous verticals are emerging, including human performance monitoring, at-home patient monitoring, and health tracking, to name a few. Off-the-shelf wearables have been developed with focus on portability, usability, and low-cost. As such, when deployed in highly ecological settings, wearable data can be corrupted by artifacts and by missing data, thus severely hampering performance. In this technical note, we overview a signal processing representation called the modulation spectrum. The representation quantifies the rate-of-change of different spectral magnitude components and is shown to separate signal from noise, thus allowing for improved quality measurement, quality enhancement, and noise-robust feature extraction, as well as for disease characterization. We provide an overview of numerous applications developed by the authors over the last decade spanning different wearable modalities and list the results obtained from experimental results alongside comparisons with various state-of-the-art benchmark methods. Open-source software is showcased with the hope that new applications can be developed. We conclude with a discussion on possible future research directions, such as context awareness, signal compression, and improved input representations for deep learning algorithms.

The Movesense Medical Sensor Chest Belt Device as Single Channel ECG for RR Interval Detection and HRV Analysis during Resting State and Incremental Exercise: A Cross-Sectional Validation Study

The value of heart rate variability (HRV) in the fields of health, disease, and exercise science has been established through numerous investigations. The typical mobile-based HRV device simply records interbeat intervals, without differentiation between noise or arrythmia as can be done with an electrocardiogram (ECG). The intent of this report is to validate a new single channel ECG device, the Movesense Medical sensor, against a conventional 12 channel ECG. A heterogeneous group of 21 participants performed an incremental cycling ramp to failure with measurements of HRV, before (PRE), during (EX), and after (POST). Results showed excellent correlations between devices for linear indexes with Pearson's r between 0.98 to 1.0 for meanRR, SDNN, RMSSD, and 0.95 to 0.97 for the non-linear index DFA a1 during PRE, EX, and POST. There was no significant difference in device specific meanRR during PRE and POST. Bland-Altman analysis showed high agreement between devices (PRE and POST: meanRR bias of 0.0 and 0.4 ms, LOA of 1.9 to -1.8 ms and 2.3 to -1.5; EX: meanRR bias of 11.2 to 6.0 ms; LOA of 29.8 to -7.4 ms during low intensity exercise and 8.5 to 3.5 ms during high intensity exercise). The Movesense Medical device can be used in lieu of a reference ECG for the calculation of HRV with the potential to differentiate noise from atrial fibrillation and represents a significant advance in both a HR and HRV recording device in a chest belt form factor for lab-based or remote field-application.

Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering

This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on inertial measurement units to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, inertial measurement units are attached to each individual EEG or ECG electrode to collect local movement data. This data is then used to remove the motion artefact by using normalised least mean square adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However, it is found that the performance varies, necessitating the need for the algorithm to be paired with more sophisticated signal processing to identify scenarios where it is beneficial in terms of improving signal quality. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new data driven approach compared to widely used blind-source separation methods, and helps enable in the wild EEG recordings to be performed.

Recommendations for determining the validity of consumer wearable heart rate devices: expert statement and checklist of the INTERLIVE Network

Assessing vital signs such as heart rate (HR) by wearable devices in a lifestyle-related environment provides widespread opportunities for public health related research and applications. Commonly, consumer wearable devices assessing HR are based on photoplethysmography (PPG), where HR is determined by absorption and reflection of emitted light by the blood. However, methodological differences and shortcomings in the validation process hamper the comparability of the validity of various wearable devices assessing HR. Towards Intelligent Health and Well-Being: Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The consortium was founded in 2019 and strives towards developing best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice validation protocol for consumer wearables assessing HR by PPG. The recommendations were developed through the following multi-stage process: (1) a systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, (2) an unstructured review of the wider literature pertaining to factors that may introduce bias during the validation of these devices and (3) evidence-informed expert opinions of the INTERLIVE Network. A total of 44 articles were deemed eligible and retrieved through our systematic literature review. Based on these studies, a wider literature review and our evidence-informed expert opinions, we propose a validation framework with standardised recommendations using six domains: considerations for the target population, criterion measure, index measure, testing conditions, data processing and the statistical analysis. As such, this paper presents recommendations to standardise the validity testing and reporting of PPG-based HR wearables used by consumers. Moreover, checklists are provided to guide the validation protocol development and reporting. This will ensure that manufacturers, consumers, healthcare providers and researchers use wearables safely and to its full potential.

PASS: A Multimodal Database of Physical Activity and Stress for Mobile Passive Body/ Brain-Computer Interface Research

With the burgeoning of wearable devices and passive body/brain-computer interfaces (B/BCIs), automated stress monitoring in everyday settings has gained significant attention recently, with applications ranging from serious games to clinical monitoring. With mobile users, however, challenges arise due to other overlapping (and potentially confounding) physiological responses (e.g., due to physical activity) that may mask the effects of stress, as well as movement artifacts that can be introduced in the measured signals. For example, the classical increase in heart rate can no longer be attributed solely to stress and could be caused by the activity itself. This makes the development of mobile passive B/BCIs challenging. In this paper, we introduce PASS, a multimodal database of Physical Activity and StresS collected from 48 participants. Participants performed tasks of varying stress levels at three different activity levels and provided quantitative ratings of their perceived stress and fatigue levels. To manipulate stress, two video games (i.e., a calm exploration game and a survival game) were used. Peripheral physical activity (electrocardiography, electrodermal activity, breathing, skin temperature) as well as cerebral activity (electroencephalography) were measured throughout the experiment. A complete description of the experimental protocol is provided and preliminary analyses are performed to investigate the physiological reactions to stress in the presence of physical activity. The PASS database, including raw data and subjective ratings has been made available to the research community at http://musaelab.ca/pass-database/. It is hoped that this database will help advance mobile passive B/BCIs for use in everyday settings.

A method to extract realistic artifacts from electrocardiogram recordings for robust algorithm testing

OBJECTIVE

Recordings of signal noise and artifacts can be added to clean electrocardiogram (ECG) records to assess the performance of ECG and arrhythmia analysis algorithms in the presence of noise. We present a method to estimate device-specific signal noise and artifacts from ECG records. This method can be applied to obtain noise estimates from healthy subjects on any ECG lead, allowing a simple device-specific recording. The proposed approach is assessed using the MIT-BIH Noise Stress Test Database recordings combined with simulated ECGs.

METHODS

The proposed noise-estimation method is based on the subtraction of a time-aligned median beat from a noisy ECG recording. To test our method, electrode motion and muscle artifact noise from MIT-BIH Noise Stress Test database were added to simulated ECG signals at signal-noise ratios (SNR) from -6 to 20 dB. A comparison between noise and estimated noise signal statistical characteristics was made including root-mean squared error and assessment of the power content in three frequency bands (cardiac [0.5-5 Hz], mid [5-25 Hz], and high [25-40 Hz]).

RESULTS

Visual assessment and frequency analysis demonstrate the good quality of noise estimation. Root-mean squared error between noise and estimated noise signals was <0.5 Normalized Units across all SNR levels. Band power error was stable across SNR levels with median percentage error between noise and estimate noise signals of <10% for cardiac and mid frequency bands.

CONCLUSION

Estimating noise from ECG records is a viable approach to generate noise and artifacts-only signals. These signals are device-specific and easy to collect from healthy subjects without requiring special electrode set-ups. Therefore, they may be suitable for use with annotated ECG databases to assess the robustness of ECG analysis algorithms in the presence of noise.