Search Results (91)

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

In many applications, recognizing the depth of sleep (e.g., light, deep, REM sleep) while the subject is sleeping enables innovative features. For instance, in SAE Level 4 autonomous driving, a driver may need to takeover the vehicle control in case the autopilot is exiting its operational design domain. Depending on the depth of the sleep, the subject may need time to takeover effectively; hence, it is particularly relevant to know in which sleep stage the subject is (e.g., light sleep, deep sleep, and REM sleep), and possibly initiate actions to prevent the subject to remain in those sleep stages that lead to longer takeover time. Sleep stage classification can be achieved through an on-the-fly algorithm, which generates output in response to each input portion without knowledge of future inputs, unlike an off-Line algorithm that provides output just after receiving the entire input sequence. Various studies have analyzed algorithms or devices that identify sleep stages during the night; however, these typically require electroencephalography (EEG), which is obtrusive, or specialized devices. This study describes the development of an on-the-fly sleep-scoring algorithm using Heart Rate (HR), RR intervals, which is the distance between two consecutive heartbeats, and accelerometer data from a smartwatch, widespread, non-invasive, and affordable but accurate device. The subjects involved in our study wore a commercial off-the-shelf wearable device during a full night’s sleep, and were also monitored using a reference medical device to establish the ground truth by means of a full polysomnography (PSG) analysis. The on-the-fly sleep scoring algorithm based on smartwatch data was tested against PSG-based scoring, achieving 88.46% accuracy, 91.42% precision, and 93.52% sensitivity in sleep–wake identification. Deep sleep was correctly identified 69.38% of times, light sleep in 50.62%, REM sleep 62.02% and wakefulness 73.48% of times. Full article

Background: Periodic limb movements in sleep (PLMS) are characterised by repetitive, involuntary limb movements that occur during sleep and are often associated with autonomic nervous system dysregulation. While it is known that PLMS influence cardiovascular parameters, the exact role of heart rate variability (HRV) and the balance between sympathetic and parasympathetic activity remains unclear. Previous studies have suggested that longer PLMS events may trigger more pronounced autonomic responses, but the relationship between the duration of PLMS and autonomic dynamics has yet to be fully explored. This study aims to investigate the influence of PLMS duration on autonomic co-activation and its potential cardiovascular implications. Methods: A retrospective analysis was conducted on polysomnographic, demographic, and medical data from five patients, encompassing a total of 1348 PLMS events. We measured heart rate (HR), high-frequency HRV (HF-HRV), systolic blood pressure (SBP), and diastolic blood pressure (DBP) for 10 heartbeats before and 10 heartbeats after each PLMS series. A time–frequency approach was used, employing 10 RR interval segments to analyse HF-HRV dynamics. Statistical analysis was performed using IBM SPSS Statistics (v. 28.0.0.0), and the Kruskal–Wallis test was used to assess statistically significant deviations from baseline. Results: HF-HRV increased during PLMS, indicating enhanced parasympathetic activation. No significant changes in mean DBP or SBP were observed with leg movements of <2.1 s. However, with movements of >2.1 s, significant increases in DBP and SBP were noted, suggesting sympathetic activation. Longer PLMS events were associated with greater parasympathetic activity, while the absence of HR changes indicates concurrent sympathetic activation, supporting autonomic co-activation. Conclusions: Our study indicates that PLMS events lasting >2.1 s are linked to increased parasympathetic activity, likely accompanied by sympathetic activation. This simultaneous activation of both branches of the autonomic nervous system, referred to as autonomic co-activation, could lead to autonomic dysregulation and an increased risk of cardiovascular instability, including potentially life-threatening events. Full article

Seismocardiography (SCG) and Gyrocardiography (GCG) use lightweight, miniaturized accelerometers and gyroscopes to record, respectively, cardiac-induced linear accelerations and angular velocities of the chest wall. These inertial sensors are also sensitive to thoracic movements with respiration, which cause baseline wanderings in SCG and GCG signals. Nowadays, accelerometers and gyroscopes are widely integrated into smartphones, thus increasing the potential of SCG and GCG as cardiorespiratory monitoring tools. This study investigates the accuracy of smartphone inertial sensors in simultaneously measuring instantaneous heart rates and breathing rates. Smartphone-derived SCG and GCG signals were acquired from 10 healthy subjects at rest. The performances of heartbeats and respiratory acts detection, as well as of inter-beat intervals (IBIs) and inter-breath intervals (IBrIs) estimation, were evaluated for both SCG and GCG via the comparison with simultaneous electrocardiography and respiration belt signals. Heartbeats were detected with a sensitivity and positive predictive value (PPV) of 89.3% and 93.3% in SCG signals and of 97.3% and 97.9% in GCG signals. Moreover, IBIs measurements reported strong linear relationships (R² > 0.999), non-significant biases, and Bland–Altman limits of agreement (LoA) of ±7.33 ms for SCG and ±5.22 ms for GCG. On the other hand, respiratory acts detection scored a sensitivity and PPV of 95.6% and 94.7% for SCG and of 95.7% and 92.0% for GCG. Furthermore, high R² values (0.976 and 0.968, respectively), non-significant biases, and an LoA of ±0.558 s for SCG and ±0.749 s for GCG were achieved for IBrIs estimates. The results of this study confirm that smartphone inertial sensors can provide accurate measurements of both instantaneous heart rate and breathing rate without the need for additional devices. Full article

Heart rate variability (HRV), which is the variation between consecutive heartbeats, reflects the electrical activity of the heart and provides insight into the autonomic nervous system (ANS) function. This study uses wavelet transform-based HRV feature extraction to investigate cardiac sympatho-vagal balance. Both the continuous wavelet transform (CWT) and discrete wavelet transform (DWT) methods were applied in different steps. DWT was used for R-peak detection and CWT and MODWT were used to generate spectrograms from RR intervals. Frequency components (HF, LF, and VLF) within 0.003–0.4 Hz were extracted, and power estimation was performed. The LF/HF ratio, indicating sympatho-vagal balance, was calculated. ECG data from 42 arrhythmia patients and 18 normal sinus rhythm subjects were analyzed. The results showed a lower LF/HF ratio in arrhythmia patients, with higher HF power in arrhythmia subjects and higher LF power in normal sinus rhythm subjects. The study suggests that the parasympathetic nervous system dominates the ANS in arrhythmia patients, while the sympathetic nervous system dominates in normal sinus rhythm patients. Full article

Background/Objectives: For the rapid, objective characterization of the physiological stress response, there is currently no generally recognized standard. The stress measurement methods used in practice (e.g., for psychological measures of stress) are often subjective, or in the case of biological markers (e.g., cortisol, amylase), they usually require a blood test. For this reason, the use of heart rate variability (HRV) to characterize stress has recently come to the fore. HRV is the variability in the length of heartbeat intervals, which indicates the ability of the heart to respond to various physiological and environmental stimuli. However, the conventional HRV metrics are not corrected for heart rate dependence; hence, they fail to fully account for the complex physiology of stress and relaxation. In order to remedy this problem, here we introduce a novel HRV parameter, the normalized variability derived from an RMSSD “Master Curve”, and we compare it with the conventional metrics. Methods: In Study 1, the relaxation state was induced either by heart rate variability biofeedback training (N = 21) or by habitual relaxation (N = 21), while in Study 2 (N = 9), the Socially Evaluated Cold Pressor Test and the Socially Evaluated Stroop Test were used to induce stress in the subject. For a statistical evaluation of the data, the Kolmogorov–Smirnov test was used to compare the distributions of mean HR, log(RMSSD), log(SDNN), and normalized variability before, during, and after relaxation and stress. Results: The results of this study indicate that while log(RMSSD) and log(SDNN) did not change significantly, the normalized variability did undergo a significant change both in relaxation states and in stress states induced by the Socially Evaluated Cold Pressor Test. Conclusions: Overall, we suggest this novel type of normalized variability ought to be used as a sensitive stress indicator, and in general, for the characterization of the complex processes of the vegetative nervous system. Full article

Heartbeat inter-beat interval (IBI) extraction is a crucial technology for unobtrusive vital sign monitoring, yet its precision and robustness remain challenging. A promising approach is fusing heartbeat signals from different types of unobtrusive sensors. This paper introduces HSF-IBI, a novel and universal framework for unobtrusive IBI extraction using heterogeneous sensor fusion. Specifically, harmonic summation (HarSum) is employed for calculating the average heart rate, which in turn guides the selection of the optimal band selection (OBS), the basic sequential algorithmic scheme (BSAS)-based template group extraction, and the template matching (TM) procedure. The optimal IBIs are determined by evaluating the signal quality index (SQI) for each heartbeat. The algorithm is morphology-independent and can be adapted to different sensors. The proposed algorithm framework is evaluated on a self-collected dataset including 19 healthy participants and an open-source dataset including 34 healthy participants, both containing heterogeneous sensors. The experimental results demonstrate that (1) the proposed framework successfully integrates data from heterogeneous sensors, leading to detection rate enhancements of 6.25 % and 5.21 % on two datasets, and (2) the proposed framework achieves superior accuracy over existing IBI extraction methods, with mean absolute errors (MAEs) of 5.25 ms and 4.56 ms on two datasets. Full article

This paper deals with the non-contact measurement of heartbeat and respiration using a millimeter-wave multiple-input–multiple-output (MIMO) frequency-modulated continuous-wave (FM-CW) radar. Monitoring heartbeat and respiration is useful for detecting cardiac diseases and understanding stress levels. Contact sensors are not suitable for these sorts of long-term measurements due to the discomfort and skin irritation they cause. Therefore, the use of non-contact sensors, such as radars, is desirable. In this study, we obtained heartbeat and respiration information from phase data measured using a millimeter-wave MIMO FM-CW radar. We propose a two-wave model based on a Fourier series expansion and extract respiration and heartbeat information as a minimization problem. This model makes it possible to produce respiration and heartbeat waveforms. The produced heartbeat waveform can be used for estimating the interbeat interval (IBI). Experiments were conducted to confirm the usefulness of the proposed method. Moreover, the estimated results were compared with the contact sensor’s results. The results for both types of sensors were in good agreement. Full article

Background: The modulation of the autonomic nervous system’s activity, particularly increasing its parasympathetic tone, is of significant interest in clinical physiotherapy due to its potential benefits for stress-related conditions and recovery processes. This study evaluated the effectiveness of the addition of neurodynamics in enhancing parasympathetic activation in subjects with chronic stress. Methods: A clinical trial randomly assigned participants to a group with neurodynamics (6 bpm breathing protocol + manual therapy + neurodynamic technique) or a group without neurodynamics (6 bpm breathing protocol + manual therapy only). Metrics of heart rate variability (HRV), including the Mean Heart Rate (Mean HR), standard deviation of intervals between consecutive heartbeats (SDNN), Heart Rate Difference (Diff. HR), Root Mean Square of Successive Differences (RMSSD), number of intervals differing by more than 50 ms (NN50), percentage of consecutive NN intervals that differed by more than 50 ms (pNN50), and the high-frequency component measured in standardized units (HF), were assessed before, during, and after the intervention. Results: During the intervention, the group with neurodynamics showed significant changes in all variables except in the pNN50 and HF while the group without neurodynamics only showed improvements in the Mean HR, SDNN, and RMSSD. In the post-intervention phase, the group with neurodynamics maintained an increase in HRV while the group without neurodynamics experienced a decrease, suggesting an increase in sympathetic activity. Conclusions: Vagal nerve neurodynamics appear to represent an effective method for enhancing parasympathetic activation in patients with chronic stress. The results highlight the importance of a more comprehensive analysis of HRV variables in order to obtain a correct picture of the impact of interventions on the complex and multifaceted functioning of the autonomic nervous system. Full article

Periodic limb movements in sleep (PLMS) are a prevalent disorder characterized by rhythmic, involuntary movements of the lower limbs, such as dorsiflexion of the ankle and extension of the big toe, occurring in periodic intervals during sleep. These movements are often linked to disrupted autonomic nervous system (ANS) activity and altered interoception. Interoception involves perceiving internal bodily states, like heartbeat, breathing, hunger, and temperature, and plays a crucial role in maintaining homeostasis and the mind–body connection. This review explores the complex relationships between PLMS, heart rate variability (HRV), ANS dysregulation, and their impact on psychiatric disorders. By synthesizing the existing literature, it provides insights into how ANS dysregulation and altered interoceptive processes, alongside PLMS, contribute to psychiatric conditions. The review highlights the potential for integrated diagnostic and therapeutic approaches and presents a cause-and-effect model illustrating the mutual influence of psychiatric disorders, ANS dysregulation, PLMS, and interoception. Full article

Background: Heart rate variability (HRV) is the fluctuation in the time intervals between adjacent heartbeats. HRV is a measure of neurocardiac function that is produced by dynamic autonomic nervous system (ANS) processes and is a simple measure that estimates cardiac autonomic modulation. Methods: The study included 108 patients admitted to the Coronary Intensive Care Unit with acute myocardial infarction (AMI) who did not undergo primary percutaneous transluminal coronary angioplasty (PTCA) or systemic thrombolysis and followed conservative management. All patients underwent detailed clinical, biological, and paraclinical assessments, including evaluation of HRV parameters and echocardiographic measurements. The analysis of RR variability in both time and frequency domains indicates that the negative prognosis of patients with AMI is associated with an overall imbalance in the neuro-vegetative system. The HRV parameters were acquired using continuous 24 h electrocardiogram (ECG) monitoring at a baseline, after 1 month, and 6 months. Results: Our analysis reveals correlations between alterations in HRV parameters and the increased risk of adverse events and mortality after AMI. The study found a significant improvement in HRV parameters over time, indicating better autonomic regulation post-AMI. The standard deviation of all RR intervals (SDNN) increased significantly from baseline (median 75.3 ms, IQR 48.2–100) to 1 month (median 87 ms, IQR 55.7–111) and further to 6 months (median 94.2 ms, IQR 67.6–118) (p < 0.001 for both comparisons). The root mean square of successive difference of RR (RMSSD) also showed significant increases at each time point, from baseline (median 27 ms, IQR 22–33) to 1 month (median 30.5 ms, IQR 27–38) and from 1 month to 6 months (median 35 ms, IQR 30–42) (p < 0.001 for all comparisons), indicating enhanced parasympathetic activity. Moreover, changes in HRV parameters have been associated with impaired left ventricle ejection fraction (LVEF) and global longitudinal strain (GLS), indicating a relationship between autonomic dysfunction and myocardial deformation. GLS values improved from a baseline median of −11% (IQR 5%) to −13% (IQR 4%) at 6 months (p < 0.001), reflecting better myocardial function. Conclusions: HRV parameters and cardiac performance analysis, especially using GLS, offer a solid framework for evaluating recovery and predicting adverse outcomes post-MI. Full article

Early diagnosis of paroxysmal atrial fibrillation (PAF) could prompt patients to receive timely interventions in clinical practice. Various PAF onset prediction algorithms might benefit from accurate heart rate variability (HRV) analysis and machine learning classification but are challenged by real-time monitoring scenarios. The aim of this study is to present an end-to-end deep learning-based PAFNet model that integrates a sliding window technique on raw R-R intervals of electrocardiogram (ECG) segments to achieve a real-time prediction of PAF onset. This integration enables the deep convolutional neural network (CNN) to be customized as a light-weight architecture that accommodates the size of sliding windows simply by altering the input layer, and specifically its effectiveness in making a new prediction with each new heartbeat. Catering to the potential influence of input sizes, three CNN models were trained using 50, 100, and 200 R-R intervals, respectively. For each model, the performance of the automated algorithms was evaluated for PAF prediction using a ten-fold cross-validation. As a results, a total of 56,381 PAFN-type and 56,900 N-type R-R interval segments were collected from publicly accessible ECG databases, and a promising prediction performance of the automated algorithm with 100 R-R intervals was achieved, with a sensitivity of 97.12%, a specificity of 97.77%, and an accuracy of 97.45%, respectively. Importantly, the automated algorithm with a sliding window step of 1 could process one sample in only 23.1 milliseconds and identify the onset of PAF at least 45 min in advance. The present results suggest that the sliding window technique on raw R-R interval sequences, along with deep learning-based algorithms, may offer the possibility of providing an accurate, real-time, end-to-end clinical tool for mass monitoring of PAF. Full article

Background: Esketamine has received approval as a nasal spray (ESK-NS) for treatment-resistant depression (TRD) and evidence from real-world investigations has confirmed the effectiveness of ESK-NS, albeit with interindividual differences in response. Heart rate variability (HRV), defined as the fluctuation in time interval between consecutive heartbeats, can be used to measure autonomic dysfunction in psychiatric disorders and its role has been investigated in diagnosis and prognosis of depression. Methods: This preliminary report aims to evaluate HRV parameters and their association with treatment outcome in 18 patients (55.6% males, 55.6 ± 9.39 years old) with TRD treated with a target dose of ESK-NS for one month (mean dose: 80.9 ± 9.05 mg). The Beck Depression Inventory (BDI) and a 3 min resting electrocardiogram were used to assess changes in depressive symptoms and HRV measurements before and after treatment. Results: Responders (n = 8, 44.5%; based on ≥30% BDI scores reduction) displayed lower HRV values than non-responders at baseline (p = 0.019), which increased at one month (p = 0.038). Receiver–Operating Characteristic (ROC) curves obtained from a logistic regression displayed a discriminative potential for baseline HRV in our sample (AUC = 0.844). Conclusions: These preliminary observations suggest a mutual interaction between esketamine and HRV, especially in relation to treatment response. Further studies are required to investigate electrophysiological profiles among predictors of response to ESK-NS and allow for personalized intervention strategies in TRD that still represent a public health concern. Full article

Background and Objectives: Exercise enhances cardiovascular health through various mechanisms, including the modulation of autonomic nervous system activity. This study aimed to systematically examine the impact of exercise on heart rate variability (HRV) in adults during and within one hour after exercise (WHAE). Materials and Methods: A comprehensive literature review was conducted using the MEDLINE, Embase, Cochrane Library, Scopus, and PubMed databases to identify published studies that reported the impact of exercise on autonomic nervous system activity in adults. The studies measured the absolute power of the low-frequency band (0.04–0.15 Hz) to the absolute power of the high-frequency band (0.015–0.4 Hz) (LF/HF ratio) to assess sympathetic activity and the root mean square of successive differences between normal heartbeats (RMSSD) to assess parasympathetic activity. Results: A total of 3329 studies were screened for relevance, and finally, 10 articles that utilized methods for measuring autonomic nervous system activity, such as the LF/HF ratio and RMSSD, covering 292 adult patients, were included for meta-analysis. In the current meta-analysis, we observed a significant decrease in parasympathetic activity during and after exercise, as indicated by RMSSD, compared to pre-exercise levels (mean difference [MD] = −4.96, 95% confidence interval [CI]: −8.00 to −1.91, p = 0.003). However, sympathetic activity after exercise, represented by the LF/HF ratio, showed a borderline significant increase compared to pre-exercise levels (MD = 1.06, 95% CI: −0.01 to 2.12, p = 0.052). The meta-regression model found that factors associated with RMSSD included mean age, male gender, and duration post-exercise. Additionally, the factor associated with the LF/HF ratio was the healthy condition of participants. The trial sequential analysis provided robust evidence of a decrease in RMSSD and an increase in the LF/HF ratio during and WHAE. Conclusions: Given the limitations of the current study, the findings suggest that a significant decrease in parasympathetic activity and a borderline significant increase in sympathetic activity in adults during and WHAE, as confirmed by trial sequential analysis. Meta-regression analysis indicated that parasympathetic activity was negatively associated with participant age and male gender, but positively associated with duration post-exercise. Additionally, increased sympathetic activity was linked to the healthy conditions of participants. This study suggests that exercise might differentially affect autonomic balance in individuals with chronic conditions compared to healthy individuals. This highlights the potential need for tailored exercise interventions to improve autonomic function across different populations. Full article

Thyroid hormones have a pivotal role in controlling metabolic processes, cardiovascular function, and autonomic nervous system activity. Hypothyroidism, a prevalent endocrine illness marked by inadequate production of thyroid hormone, has been linked to different cardiovascular abnormalities, including alterations in heart rate variability (HRV). The study included 110 patients with hypothyroid disorder. Participants underwent clinical assessments, including thyroid function tests and HRV analysis. HRV, a measure of the variation in time intervals between heartbeats, serves as an indicator of autonomic nervous system activity and cardiovascular health. The HRV values were acquired using continuous 24-h electrocardiogram (ECG) monitoring in individuals with hypothyroidism, as well as after a treatment period of 3 months. All patients exhibited cardiovascular symptoms like palpitations or fatigue but showed no discernible cardiac pathology or other conditions associated with cardiac disease. The findings of our study demonstrate associations between hypothyroidism and alterations in heart rate variability (HRV) parameters. These results illustrate the possible influence of thyroid dysfunction on the regulation of cardiac autonomic function. Full article