Search Results (79)

Ring-type wearable sensors are increasingly used for continuous monitoring of heart rate (HR) and heart rate variability (HRV); however, evidence regarding their measurement validity and potential lateral differences remains limited. This study aimed to evaluate (1) the validity of HR and HRV obtained from ring-type photoplethysmography (PPG) sensors under multiple activity conditions (Experiment 1), and (2) the presence of lateral differences in autonomic indices when worn on the left versus right hand (Experiment 2). In Experiment 1, HR and HRV indices from the ring sensor were compared with those from a Holter electrocardiogram (ECG) and a wrist device during rest, low activity, and moderate activity. Mixed-model analysis revealed significant differences in very low frequency (VLF) power between the left- and right-hand rings (p = 0.001). Additionally, significant interactions between device side and measurement condition were observed for HR and low-frequency (LF) components, indicating that lateral differences were condition-dependent. In Experiment 2, participants wore two ring sensors simultaneously to assess left–right discrepancies under rest and exercise conditions. The SDPP index showed a significant difference (p = 0.017), with mean values differing between Rest and Exercise, demonstrating condition-related variability but limited systematic lateral bias. Overall, ring-type wearable sensors demonstrated high validity for HR and acceptable performance for selected HRV metrics during rest and low-activity states. While some condition-dependent lateral differences emerged for specific HRV parameters, the practical impact on overall measurement performance remained modest. These findings support the utility of ring-type wearable devices for autonomic monitoring while highlighting the importance of considering activity level and side–condition interactions. Full article

Background: Realistic simulation of ECG signals is essential for validating signal-processing algorithms and training artificial intelligence models in cardiology. Many existing approaches model either waveform morphology or heart rate variability (HRV), but few achieve both with high accuracy. This study proposes a hybrid method that combines morphological accuracy with physiological variability. Methods: We developed a mathematical model that integrates Gaussian mesa functions (GMF) for waveform generation and a chaotic Rössler attractor to simulate RR-interval variability. The GMF approach allows fine control over the amplitude, width, and slope of each ECG component (P, Q, R, S, T), while the Rössler system introduces dynamic modulation through the use of seven parameters. Spectral and statistical analyses were applied, including power spectral density (PSD) computed via the Lomb–Scargle, STFT, CWT, and histogram analyses. Results: The synthesized signals demonstrated physiological realism in both the time and frequency domains. The LF/HF ratio was 1.5–2.0 when simulating a normal rhythm and outside these limits in a simulated stress rhythm, consistent with typical HRV patterns. PSD analysis captured clear VLF (0.003–0.04 Hz), LF (0.04–0.15 Hz), and HF (0.15–0.4 Hz) bands. Histogram distributions showed amplitude ranges consistent with real ECGs. Conclusions: The hybrid GMF–Rössler approach enables large-scale ECG synthesis with controllable morphology and realistic HRV. It is computationally efficient and suitable for artificial intelligence training, diagnostic testing, and digital twin modeling in cardiovascular applications. Full article

A series of multi-parameter, multi-layer observations was conducted to study possible electromagnetic precursors associated with the M 6.7 earthquake that struck Iburi, Hokkaido, Japan, at 18:07:59 UT on 5 September 2018. The most significant observation is seismogenic lower-ionospheric perturbations in the propagation anomalies of sub-ionospheric VLF/LF signals recorded in Japan and Russia. Other substantial observations include the GIM-TEC irregularities, the intensification of stratospheric atmospheric gravity waves (AGWs), and the satellite and ground monitoring of air temperature (T), relative humidity (RH), atmospheric chemical potential (ACP), and surface latent heat flux (SLHF). We have found that there were very remarkable VLF/LF anomalies indicative of lower-ionospheric perturbations observed on 4 and 5 September just before the EQ date and even after it from the observations in Japan and Russia. In particular, the anomaly was detected for a particular propagation path from the JJY transmitter (Fukushima) to a VLF station at Wakkanai one day before the EQ, i.e., on 4 September, and is objectively confirmed by machine/deep learning analysis. An anomaly in TEC occurred only on 5 September, but it is unclear whether it is related to a pre-EQ effect or a minor geomagnetic storm. We attempted to determine whether any seismo-related atmospheric gravity wave (AGW) activity occurred in the stratosphere. Although numerous anomalies were detected, they are most likely associated with convective weather phenomena, including a typhoon. Finally, the Earth’s surface parameters based on satellite monitoring seem to indicate some anomalies from 29 August to 3, 4, and 5 September, a few days prior to EQ data, but the ground-based observation close to the EQ epicenter has indicated a clear T/RH and ACP on 2 September with fair weather, but no significant data on subsequent days because of severe meteorological activities. By integrating multi-layer observations, the LAIC (lithosphere–atmosphere–ionosphere coupling) process for the Hokkaido earthquake appears to follow a slow diffusion-type channel, where ionospheric perturbations arise a few days after ground thermal anomalies. This study also provides integrated evidence linking concurrent lower-ionospheric, atmospheric, and surface thermal anomalies, emphasizing the diagnostic value of such multi-parameter observations in understanding EQ-associated precursor signatures. Full article

It has recently been recognized that the ionosphere is highly sensitive to pre-seismic effects, and the detection of ionospheric perturbations associated with earthquakes (EQs) is one of the most promising candidates for short-term EQ prediction. In this review, we focus on a possible use of VLF/LF (very low frequency (3–30 kHz)/low frequency (30–300 kHz)) radio sounding of seismo-ionospheric perturbations to study seismogenic effects. Because an understanding of the early history in any area will provide a lot of crucial insights to the readers (especially to young scientists) working in the field of seismo-electromagnetics, we provide a brief history (mainly results reported by a Russian group of scientists) of the initial application of subionospheric VLF/LF propagation for the study of ionospheric perturbations associated with EQs, and then we present our first convincing evidence on the ionospheric perturbation for the disastrous Kobe EQ in 1995, with a new analysis method based on the shifts in terminator times in VLF/LF diurnal variations (minima in the diurnal variations in amplitude and phase). We then summarize our latest results on further evidence of seismo-ionospheric perturbations. Firstly, we present a few statistical studies on the correlation between VLF/LF propagation anomalies and EQs based on long-term data. Secondly, we showcase studies for a few large, recent EQs (including the 2011 Tohoku EQ). Building on those EQ precursor studies, we demonstrate scientific topics and the underlying physics that can be studied using VLF/LF data, highlighting recent achievements including the revolutionary perspective of lithosphere–atmosphere–ionosphere coupling (LAIC) (or how the ionosphere is perturbed due to the lithospheric pre-EQ activity), modulation in VLF/LF data by atmospheric gravity waves (AGWs), Doppler-shift observation, satellite observation of VLF/LF transmitter signals, etc., together with the recommendation of the application of new technologies such as artificial intelligence and critical analysis to VLF/LF analysis. Finally, we want to emphasize again the essential significance of the information on lower-ionospheric perturbations within LAIC studies. Full article

Photoplethysmography (PPG)-based heart rate variability (HRV) offers a cost-effective alternative to electrocardiography (ECG) for autonomic monitoring in wearable devices. We optimized signal processing on a 16-bit microcontroller by comparing 4th-order equivalent Butterworth and Elliptic IIR bandpass filters (0.8–20 Hz, zero-phase) at 1000, 500, and 250 Hz. Paired PPG–ECG recordings from 10 healthy adults were analyzed for ln HF, ln LF, and ln VLF using Lin’s concordance correlation coefficient (CCC), ±5% equivalence testing (TOST), and Passing–Bablok regression (PBR). Butterworth at 500 Hz preserved near-identity with ECG standard (CCC ≥0.94; TOST met equivalence; PBR slopes/intercepts: ln HF = 0.97x + 0.10, ln LF = 1.02x − 0.07, ln VLF = 1.01x − 0.03), while halving computational load. In contrast, Elliptic at 250 Hz degraded concordance (CCC ≈ 0.64) and failed equivalence, with greater bias from nonlinear phase and ripple-induced distortion. Elliptic performance improved at higher sampling but offered no benefit over Butterworth. These results support zero-phase Butterworth filtering at ≥500 Hz as the optimal balance of fidelity, robustness, and efficiency, enabling reliable PPG-HRV monitoring on low-power devices. As a pilot investigation (n = 10), this study establishes preliminary design parameters and optimal configurations to guide subsequent large-scale clinical validation. Full article

Background/Objectives: Heart rate variability (HRV) reflects autonomic nervous system modulation and may be altered in both unexplained syncope and obstructive sleep apnea (OSA). However, the nocturnal autonomic patterns underlying these conditions and their coexistence remain poorly understood. This study aimed to characterize nocturnal autonomic modulation in patients with unexplained syncope, OSA, or both, compared with individuals without these conditions. Methods: In this multicenter, cross-sectional, comparative study, 304 adults were assigned to four groups: controls (no syncope or OSA), OSA without syncope, syncope without OSA, and syncope with OSA. Time- and frequency-domain HRV parameters were derived from overnight respiratory polygraphy and compared across groups. Results: OSA was associated with increased root mean square of successive differences (RMSSD) and reduced low-frequency (LF) power, indicating enhanced vagal activity and lower nocturnal sympathetic tone. Syncope was characterized by further reductions in sympathetic indices (LF and very low frequency, VLF) with increased RMSSD, suggesting blunted sympathetic reserve. Patients with both conditions exhibited a mixed autonomic profile—elevated overall HRV with concurrent reductions in both sympathetic and parasympathetic components—indicating more profound dysautonomia despite milder OSA severity. Conclusions: OSA and syncope show distinct nocturnal autonomic patterns, and their coexistence leads to deeper autonomic imbalance. Incorporating nocturnal HRV analysis into routine polygraphy may improve pathophysiological stratification of unexplained syncope and identify clinically significant OSA. Full article

Background/Objectives: Obstructive sleep apnea (OSA) is strongly associated with cardiovascular morbidity, and depressive symptoms are common in affected individuals. Both OSA and depression have been linked to autonomic dysfunction, but the independent contribution of depressive symptoms to autonomic dysfunction in OSA remains unclear. We investigated whether depressive symptom severity is associated with autonomic function, indexed by heart-rate variability (HRV), in patients with OSA. Methods: We retrospectively analyzed 1713 adults with OSA at a university-affiliated sleep center from 2011 to 2024. HRV was derived from electrocardiography during polysomnography, and frequency-domain indices (natural log-transformed LF, HF, VLF, TP, and LF/HF) were computed. Depressive symptoms were assessed using the Beck Depression Inventory-II (BDI-II). Associations between BDI-II and HRV indices were evaluated using univariable and multivariable linear regressions. Results: In univariable regression analyses, higher BDI-II scores were significantly associated with lower HRV indices (ln LF, ln HF, ln VLF, ln TP; all p < 0.01). In multivariable analyses, higher BDI-II scores were independently associated with lower ln LF, ln HF, and ln TP (all p < 0.05), adjusting for age, sex, body mass index, hypertension, diabetes, apnea–hypopnea index, arousal index, and sleep quality. Conclusions: Greater depressive symptom burden is independently associated with reductions in multiple HRV indices, suggesting attenuated parasympathetic activity and autonomic dysregulation in patients with OSA. These findings support integrated management strategies that address both physiological and psychological domains in OSA and motivate longitudinal studies to test whether effective depression treatment improves HRV and mitigates long-term cardiovascular risk. Full article

The binary anomaly detection (classification) of ionospheric data related to Very Low Frequency (VLF) signal amplitude in prior research demonstrated the potential for development and further advancement. Further data quality improvement is integral for advancing the development of machine learning (ML)-based ionospheric data (VLF signal amplitude) anomaly detection. This paper presents the transition from binary to multi-class classification of ionospheric signal amplitude datasets. The dataset comprises 19 transmitter–receiver pairs and 383,041 manually labeled amplitude instances. The target variable was reclassified from a binary classification (normal and anomalous data points) to a six-class classification that distinguishes between daytime undisturbed signals, nighttime signals, solar flare effects, instrument errors, instrumental noise, and outlier data points. Furthermore, in addition to the dataset, we developed a freely accessible web-based tool designed to facilitate the conversion of MATLAB data files to TRAINSET-compatible formats, thereby establishing a completely free and open data pipeline from the WALDO world data repository to data labeling software. This novel dataset facilitates further research in ionospheric signal amplitude anomaly detection, concentrating on effective and efficient anomaly detection in ionospheric signal amplitude data. The potential outcomes of employing anomaly detection techniques on ionospheric signal amplitude data may be extended to other space weather parameters in the future, such as ELF/LF datasets and other relevant datasets. Full article

Sleep posture has received limited attention in studies of autonomic nervous system (ANS) activity during sleep, particularly in clinical populations. We analyzed data from 130,885 individuals (56.1% female) in the Allostatic State Mapping by Ambulatory ECG Repository (ALLSTAR), a nationwide Japanese database of 24 h Holter ECG recordings obtained for clinical purposes. Sleep posture was classified as supine, right lateral, left lateral, or prone using triaxial accelerometer data. Heart rate variability (HRV) indices—including heart rate (HR), standard deviation of RR intervals (SDRR), high-frequency (HF), low-frequency (LF), very low-frequency (VLF) components, cyclic variation in heart rate (CVHR), and HF spectral power concentration index (Hsi)—were calculated for each posture and stratified by age and sex. HR was consistently lowest in the left lateral posture and highest in the right lateral posture across most age groups. Other HRV indices also showed consistent laterality, although the effect sizes were generally small. Posture distribution differed slightly by estimated sleep apnea severity, but the effect size was negligible (η² = 0.0013). These findings highlight sleep posture as a statistically significant and independent factor influencing ANS activity during sleep, though the magnitude of differences should be interpreted in the context of their clinical relevance. Full article

The classification of very-low-frequency and low-frequency (VLF/LF) lightning-radiation electric-field waveforms is of paramount importance for lightning-disaster prevention and mitigation. However, traditional waveform classification methods suffer from the complex characteristics of lightning waveforms, such as non-stationarity, strong noise interference, and feature coupling, limiting classification accuracy and generalization. To address this problem, a novel framework is proposed for VLF/LF lightning-radiated electric-field waveform classification. Firstly, an improved Kalman filter (IKF) is meticulously designed to eliminate possible high-frequency interferences (such as atmospheric noise, electromagnetic radiation from power systems, and electronic noise from measurement equipment) embedded within the waveforms based on the maximum entropy criterion. Subsequently, an attention-based multi-fusion convolutional neural network (AMCNN) is developed for waveform classification. In the AMCNN architecture, waveform information is comprehensively extracted and enhanced through an optimized feature fusion structure, which allows for a more thorough consideration of feature diversity, thereby significantly improving the classification accuracy. An actual dataset from Anhui province in China is used to validate the proposed classification framework. Experimental results demonstrate that our framework achieves a classification accuracy of 98.9% within a processing time of no more than 5.3 ms, proving its superior classification performance for lightning-radiation electric-field waveforms. Full article

Background: Autonomic nervous system (ANS) and vascular factors are associated with glaucoma. However, the association between systemic comorbidity burden and ANS and hemodynamic function in patients with glaucoma remains unclear. This study aimed to examine the association between heart rate variability (HRV) and acceleration plethysmography (APG) parameters and the age-adjusted Charlson Comorbidity Index (ACCI) in patients with glaucoma. Methods: A total of 260 subjects (260 eyes), including 186 with primary open-angle glaucoma (PG) and 74 with exfoliation glaucoma (EG), were enrolled at Shimane University Hospital from June 2023 to July 2024. HRV and APG were assessed using a sphygmograph (TAS9 Pulse Analyzer Plus View). HRV parameters included time-domain measures (SDNN, RMSSD, CVRR) and frequency-domain measures (TP, VLF, LF, HF, LF/HF). APG parameters included the a, b, c, d, and e components of the accelerated pulse wave, and the following vascular types: Type A, Type B, and Type C. The association between ACCI and HRV and APG parameters was evaluated using Spearman’s rank correlation and multivariate regression adjusted for sex, body mass index, pulse rate, systolic and diastolic blood pressure, intraocular pressure, medication score, mean deviation, and glaucoma type. Results: By univariate analysis, against ACCI, significant inverse correlations were observed for several parameters: LnLF (R = −0.17, p = 0.0062); LnLF/LnHF (R = −0.24, p = 0.00012); b peak (R = −0.14, p = 0.031); d peak (R = −0.17, p = 0.0072); and e peak (R = −0.15, p = 0.015). Regarding HRV parameters, multivariate linear regression models showed that ACCI was significantly positively associated with RMSSD (coefficient: 2.861; 95% CI: 0.447 to 5.274) and significantly negatively associated with the frequency-domain parameters LnLF (coefficient: −0.127; 95% CI: −0.245 to −0.009) and LnLF/LnHF (coefficient: −0.038; 95% CI: −0.062 to −0.014). In APG parameters, the c peak was significant associated with ACCI (coefficient: −12.6; 95% CI: −22.5 to −2.69). ACCI was significantly associated with Type B (coefficient: 0.305; 95% CI: 0.057 to 0.552). Conclusions: Greater systemic comorbidity burden may be related to impaired ANS regulation and increased vascular stiffness in glaucoma patients. Full article

Heart rate (HR) is strongly affected by the autonomic nervous system (ANS), while its spontaneous fluctuations, called heart rate variability (HRV), report about the dynamics of the complex, vegetative regulation of the heart rhythm. Hence, HRV is widely considered an important marker of the ANS effects on the cardiac system, and as such, a crucial diagnostic tool in cardiology. In order to obtain nontrivial results from HRV analysis, it would be desirable to establish exact, universal interrelations between the typical HRV parameters and HR itself. That, however, has not yet been fully accomplished. Hence, our aim was to perform a comparative statistical analysis of ECG recordings from a public database, with a focus on the HR dependence of typical HRV parameters. We revealed their fundamental connections, which were substantiated by basic mathematical considerations, and were experimentally demonstrated via the analysis of 24 h of ECG recordings of more than 200 healthy individuals. The large database allowed us to perform unique age-cohort analyses. We confirmed the HR dependence of typical time-domain parameters, such as RMSSD and SDNN, frequency-domain parameters such as the VLF, LF, and HF components, and nonlinear indices such as sample entropy and DFA exponents. In addition to shedding light on their relationship, we are the first, to our knowledge, to identify a new, diffuse structure in the VHF regime as an important indicator of SNS activity. In addition, the demonstrated age dependence of the HRV parameters gives important new insight into the long-term changes in the ANS regulation of the cardiac system. As a possible molecular physiological mechanism underlying our new findings, we suggest that they are associated with Piezo2 channel function and its age-related degradation. We expect our results to be utilized in HRV analysis related to both medical research and practice. Full article

Background/Objectives: Heart rate variability (HRV) is a marker of autonomic nervous system function, based on fluctuations in heartbeat intervals. Although several studies have investigated the association between frequency-domain HRV parameters and glaucoma, evidence based on large sample sizes remains limited. Therefore, the present study aimed to examine the relationship between frequency-domain HRV parameters and glaucoma subtypes, including primary open-angle glaucoma (PG) and exfoliation glaucoma (EG), using a larger sample size. Methods: Participants with primary open-angle glaucoma (PG), exfoliation glaucoma (EG), or no ocular disease other than cataract (controls) were recruited at Shimane University between June 2023 and July 2024. Frequency-domain HRV parameters (total power [TP], very-low-frequency [VLF], low-frequency [LF], high-frequency [HF], and LF/HF) were measured using a sphygmograph (TAS9 Pulse Analyzer Plus View). Group comparisons were conducted using unpaired t-tests, Fisher’s exact tests, and Tukey’s HSD test. Multivariate analyses were performed to identify factors associated with each HRV parameter. Results: A total of 809 participants were analyzed, including 522 with PG, 191 with EG, and 96 controls. The EG group showed significantly lower values across all frequency-domain HRV parameters compared to the PG group, and significantly lower LnLF values than the control group (p = 0.012). Multivariate analyses revealed that no significant associations were found between HRV measures and the presence of glaucoma or pseudoexfoliation material (PEM) deposition. Older age was significantly associated with lower values across all HRV parameters. Conclusions: In elderly glaucoma patients, age-related alterations in frequency-domain HRV parameters have been observed. Full article

Introduction: The monitoring of autonomic nervous balance during childhood remains underexplored. However, heart rate variability (HRV) is widely recognized as a biomarker of health risk across the lifespan. Juvenile idiopathic arthritis (JIA), a group of chronic inflammatory joint disorders, is associated with persistent inflammation and pain, both of which contribute to increased cardiovascular risk, commonly linked to reduced HRV. Among HRV parameters, very-low frequency (VLF) components have been associated with physiological recovery processes. This study aimed to assess HRV during sleep in patients with JIA. Methods: We studied 10 patients with JIA and 10 age-, gender-, and Tanner stage-matched healthy controls. All participants underwent polysomnographic monitoring following an adaptation night in the sleep laboratory. HRV was analyzed using standard time and frequency domain measures over 5 min epochs across all sleep stages. Frequency components were classified into low- and high-frequency bands, and time domain measures included the standard deviation of the beat-to-beat intervals. Group differences in HRV parameters were assessed using nonparametric tests for independent samples, with a significance level set at p < 0.05. Results: JIA exhibited greater sleep disruption than controls, including reduced NREM sleep, longer total sleep time, and increased wake time after sleep onset. HRV analyses in both time and frequency domains revealed significant differences between groups across all stages of sleep. In JIA patients, the standard deviation of the normal-to-normal interval during slow wave sleep (SWS) and total power across all sleep stages (p < 0.05) was reduced. In JIA patients, the standard deviation of the normal-to-normal interval during slow wave sleep and total power across all sleep stages were significantly reduced (p < 0.05). VLF power was also significantly lower in JIA patients across all sleep stages (p = 0.002), with pronounced reductions during N2 and SWS (p = 0.03 and p = 0.02, respectively). A group effect was observed for total power across all stages, mirroring the VLF findings. Additionally, group differences were detected in LF/HF ratio analyses, although values during N2, SWS, and REM sleep did not differ significantly between groups. Notably, the number of affected joints showed a moderate positive correlation with the parasympathetic HRV parameter. Conclusions: Patients with JIA exhibited sleep disruption and alterations in cardiovascular autonomic functioning during sleep. Reduced HRV across all sleep stages in these patients suggests underlying autonomic nervous dysfunction. Addressing sleep disturbances in patients with chronic pain may serve as an effective strategy for managing their cardiovascular risk. Full article

This study investigates the impact on the Earth’s ionosphere of a severe geomagnetic storm (Dst $\sim -212$ nT) that began on 23 April 2023 at around 17:37 UT according to very low-frequency (VLF, 3–30 kHz) or low-frequency (LF, 30–300 kHz) radio signals and ionosonde data. We analyze VLF/LF signals received by SuperSID monitors located in mid-latitude (Europe) and low-latitude (South America, Colombia) areas across nine different propagation paths in the Northern Hemisphere. Mid-latitude regions exhibited a daytime amplitude perturbation, mostly an increase, by ∼3–5 dB during the storm period, with a subsequent recovery after 7–8 days post April 23. In contrast, signals received in low-latitude regions (UTP, Colombia) did not show significant variation during the storm-disturbed days. We also observe that the 3-hour average of foF2 data declined by up to 3 MHz on April 23 and April 24 at the European Digisonde stations. However, no significant variation in foF2 was observed at the low-latitude Digisonde stations in Brazil. Both the VLF and ionosonde data exhibited anomalies during the storm period in the European regions, confirming that both D- and F-region ionospheric perturbation was caused by the severe geomagnetic storm. Full article