Contribution of body movements on the heart rate variability during high intensity running

Abstract We studied the association between the heart rate variability (HRV) and the subject’s movement during high intensity running. HRV is affected by movement, and this phenomena is known as cardiolocomotor coupling (CLC). Characterization of movement related components on the HRV spectrogram is a principal step toward meaningful interpretation of autonomic nervous system (ANS) activity. According to the literature, the aliases of the first and second harmonics of the cadence frequency are the main contributors affecting HRV. Instead, we found out that there is another aliasing component containing significant power in the HRV spectrogram. The source of this component might be the arm swings, torso movement or any other mechanical movement along the horizontal axis, orthogonal to the cadence direction. Our results show that in 13 out of 22 subjects the spectral HRV component arising from the alias of the second harmonic of cadence frequency (vertical acceleration) accommodates significantly less energy than the component related to the alias of the first harmonic of horizontal acceleration. Therefore, neglecting this component and/or considering the second harmonic of the cadence frequency as more dominant one is not always a valid assumption

Characterization and reduction of exercise-based motion influence on heart rate variability using accelerator signals and channel decoding in the time–frequency domain

Abstract Objective: Heart rate variability (HRV) is defined as the variation of the heart’s beat to beat time intervals. Although HRV has been studied for decades, its response to stress tests and off-rest measurements is still under investigation. In this paper, we studied the influence of motion on HRV throughout different exercise tests, including a maximal running of healthy recreational runners, cycling, and walking tests of healthy subjects. Approach: In our proposed method, we utilized the motion trajectory (which is known to exist partially in HRV) measured by a three-channel accelerator (ACC). We then estimated their shares in HRV using a wearable electrocardiogram (ECG) and an error-correcting problem formulation. In this method, we characterized the motion components of three orthogonal directions induced into the HRV signal, and then we suppressed the estimated motion artefact to construct a motion-attenuated spectrogram. Main results and Significance: Our analysis showed that HRV in the exercise context is susceptible to motion artefacts. Furthermore, the interpretation of autonomic nervous system (ANS) activity and HRV indices throughout exercise has a high margin of error depending on the intensity level, type of exercise, and motion trajectory. Our experiment on 84 healthy subjects throughout mid-intensity cycling and walking tests showed 39% and 32% influence on average, respectively. In addition, our proposed method revealed through a maximal running test with 11 runners that motion can describe on average 20%–40% of the HRV high-frequency (HF) energy at different workloads of running

Spectral fusion-based breathing frequency estimation:experiment on activities of daily living

Abstract Background: We study the estimation of breathing frequency (BF) derived from wearable single-channel ECG signal in the context of mobile daily life activities. Although respiration effects on heart rate variability and ECG morphology have been well established, studies on ECG-derived respiration in daily living settings are scarce; possibly due to considerable amount of disturbances in such data. Yet, unobtrusive BF estimation during everyday activities can provide vital information for both disease management and athletic performance optimization. Method and data: For robust ECG-derived BF estimation, we combine the respiratory information derived from R–R interval (RRI) variability and morphological scale variation of QRS complexes (MSV), acquired from ECG signals. Two different fusion techniques are applied on MSV and RRI signals: cross-power spectral density (CPSD) estimation and power spectrum multiplication (PSM). The algorithms were tested on large sets of data collected from 67 participants during office, household and sport activities, simulating daily living activities. We use spirometer reference BF to evaluate and compare our estimations made by different models. Results and conclusion: PSM acquires the least average error of BF estimation, %D²ᵟ=9.86 and %E=9.45, compared to the reference spirometer values. PSM offers approximately 25 and 75% less error in comparison with the CPSD fusion estimation and the estimation by those two exclusive sources, respectively. Our results demonstrate the superiority of both of the fusion approaches, compared to the estimation derived from either of RRI or MSV signals exclusively

Spectral data fusion for robust ECG-derived respiration with experiments in different physical activity levels

Abstract In this paper, we study instant respiratory frequency extraction using single-channel electrocardiography (ECG) during mobile conditions such as high intensity exercise or household activities. Although there are a variety of ECG-derived respiration (EDR) methods available in the literature, their performance during such activities is not very well-studied. We propose a technique to boost the robustness and reliability of widely used and computationally efficient EDR methods, aiming to qualify them for ambulatory and daily monitoring. We fuse two independent sources of respiratory information available in ECG signal, including respiratory sinus arrhythmia (RSA) and morphological change of ECG time series, to enhance the accuracy and reliability of instant breathing rate estimation during ambulatory measurements. Our experimental results show that the fusion method outperforms individual methods in four different protocols, including household and sport activities

Effect of different ECG leads on estimated R–R intervals and heart rate variability parameters

Abstract Heart rate and heart rate variability parameters provide important information on sympathetic and parasympathetic branches of autonomous nervous system. These parameters are usually extracted from electrocardiograms often measured between two electrodes and called an ECG lead. Besides systems intended only for heart rate measurement, ECG measurement devices employ several well-known lead systems including the standard 12-lead system, EASI lead system and Mason-Likar systems. Therefore, the first step is to select the appropriate lead for heart rate variability analysis. The appropriate electrode locations for single-lead measurement systems or the preferred measurement lead in multi-lead measurement are choices that the user needs to make when the heart rate variability is of interest. However, it has not been addressed in the literature, if the lead selection has an effect on the obtained HRV parameters. In this work, we characterized the amount of deviation of heart rate and heart rate variability parameters extracted from nine ECG leads, six from EASI leads and three modified limb leads. The results showed a deviation of 2.04, 2.88, 2.06 and 3.45 ms in SDNN, rMSSD, SD1 and SD2, respectively. A relative difference up to 10% was observed in HRV parameters for single signal frames. Additionally, the discrimination of the R-peaks by amplitudes was evaluated. The A-S lead appeared to have the best performance in all the tests

Heart rate variability and its association with second ventilatory threshold estimation in maximal exercise test

Abstract During incremental exercise, two ventilatory thresholds (VT1, VT2) can normally be identified from gas exchange and ventilatory measurements, such as oxygen uptake, carbon dioxide production and ventilation. In this paper, we attempt to estimate the VT2 using HRV indices derived from a wearable electrocardiogram during a maximal exercise test. The exercise test is conducted on a treadmill that raises its speed by 0.5 km/h every minute. We have 42 measured exercise tests from 24 healthy male volunteers. Three experts determined the VT2 in each exercise test independently and we used principal component subspace reconstruction of their determinations to compute a collective VT2 for our machine learning model. The results demonstrate that the VT2 can be estimated from HRV using the proposed method with a reasonable performance during a maximal exercise test. In 28 out of 42 exercise tests, the HRV-derived threshold (HRVT) is within a minute (one phase) of the collective expert’s determination

How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?

Abstract In collaborative learning situations, monitoring is needed to maintain common progress toward shared goals. The present study aimed to analyze group-level monitoring events, as well as groups’ reactions to these events, to identify instances of adaptive regulation and maladaptive behavior. Three dimensions of monitoring events were qualitatively coded from video data: the monitoring target, valence, and phase, which provided insight into identifying critical moments during the collaborative process when regulation is needed. By looking at what kind of monitoring the groups engaged in, and how the groups progressed after the need for regulation arose, different types of adaptive regulation and maladaptive behavior were distinguished. In addition, group-level physiological state transitions in the heart rate were explored to see whether changes in regulation (adaptive regulation and maladaptive behavior) were reflected in the state transitions. Nine groups of three students each participated in a collaborative exam for an advanced high school physics course, during which video and heart rate data were collected. The results showed that on-track sequences were the most common, followed by adaptive sequences. The temporality of these sequences was examined, and four categories of group progress are described with case examples. A correlation analysis showed that physiological state transitions were positively correlated with on-track sequences. The opportunities and limitations of using three dimensions of monitoring and heart-rate based physiological state transitions to study adaptive regulation are discussed

Validation of printed, skin-mounted multilead electrode for ECG measurements

Abstract An electrocardiography (ECG) monitoring can be used to detect heart‐related abnormalities by recording cardiac activity over a period of time. The conventional 12‐lead ECG measurement system is the standard practice for the evaluation of the heart’s electrical activity. However, a recent trend is to develop patch‐type measurement devices for unobtrusive ECG monitoring by reducing device size and number of electrodes on the skin. This development aims to minimize the discomfort for the user from the wearable recording devices. A printed, bandage‐type hybrid system for continuous ECG monitoring to allow as much comfort as possible while maintaining the signal quality required for medical evaluation is proposed. Movement artifacts in recorded ECG signals are a challenge in long‐term monitoring while the patients are engaged in their everyday activities. The movement artifacts from the printed skin‐conformable electrode are compared to commercial exercise stress‐test ECG electrodes during different physical activities and stationary periods. The results show that the signal quality obtained with the multilead patch ECG electrode, manufactured with printing technologies, is comparable to electrodes currently used in healthcare

Unobtrusive, low‐cost out‐of‐hospital, and in‐hospital measurement and monitoring system

Abstract Continuous monitoring of vital signs can be a life‐saving matter for different patient groups. The development is going toward more intelligent and unobtrusive systems to improve the usability of body‐worn monitoring devices. Body‐worn devices can be skin‐conformable, patch‐type monitoring systems that are comfortable to use even for prolonged periods of time. Herein, an intelligent and wearable, out‐of‐hospital, and in‐hospital four‐electrode electrocardiography (ECG) and respiration measurement and monitoring system is proposed. The system consists of a conformable screen‐printed disposable patch, a measurement unit, gateway unit, and cloud‐based analysis tools with reconfigurable signal processing pipelines. The performance of the ECG patch and the measurement unit was tested with cardiac patients and compared with a Holter monitoring device and discrete, single‐site electrodes

Experiences in digitizing and digitally measuring a paper-based ECG archive

Abstract Background: No established method for digitizing and digital measuring of paper electrocardiograms (ECG) exists. We describe a paper ECG digitizing and digital measuring process, and report comparability to manual measurements. Methods: A paper ECG was recorded from 7203 health survey participants in 1978–1980. With specific software, the ECGs were digitized (ECG Trace Tool), and measured digitally (EASE). A sub-sample of 100 ECGs was selected for manual measurements. Results: The measurement methods showed good agreement. The mean global (EASE)-(manual) differences were 1.4 ms (95% CI 0.5–2.2) for PR interval, −1.0 ms (95% CI −1.5–[−0.5]) for QRS duration, and 11.6 ms (95% CI 10.5–12.7) for QT interval. The mean inter-method amplitude differences of RampV5, RampV6, SampV1, TampII and TampV5 ranged from −0.03 mV to 0.01 mV. Conclusions: The presented paper-to-digital conversion and digital measurement process is an accurate and reliable method, enabling efficient storing and analysis of paper ECGs