Search Results (43)

Syncope of unclear cause (SUC) presents a significant diagnostic challenge, with a considerable proportion of patients remaining without a definitive diagnosis despite comprehensive clinical evaluation. This study aims to explore the potential of unsupervised machine learning (ML), specifically clustering algorithms, to identify clinically meaningful subgroups within a cohort of 123 patients with SUC. Patients were prospectively recruited from the cardiology, neurology, and emergency departments, and clustering was performed using the k-prototypes algorithm, which is suitable for mixed-type data. The number of clusters was determined through cost function analysis and silhouette index, and visual validation was performed using UMAP. Five distinct patient clusters were identified, each exhibiting unique profiles in terms of age, comorbidities, and symptomatology. After clustering, nocturnal cardiorespiratory polygraphy and heart rate variability (HRV) parameters were analyzed across groups to uncover potential physiological differences. The results suggest distinct autonomic and respiratory patterns in specific clusters, pointing toward possible links among sympathetic dysregulation, sleep-related disturbances, and syncope. While the sample size imposes limitations on generalizability, this pilot study demonstrates the feasibility of applying unsupervised ML to complex clinical syndromes. The integration of clinical, autonomic, and sleep-related data may provide a foundation for future, larger-scale studies aiming to improve diagnostic precision and guide personalized management strategies in patients with SUC. Full article

Background: Sleep-disordered breathing (SDB), particularly Cheyne–Stokes respiration (CSR), is highly prevalent among patients hospitalized with acute decompensated heart failure (ADHF) and is associated with worse clinical outcomes. Sodium-glucose cotransporter-2 inhibitors (SGLT2i) have demonstrated cardiorenal benefits in heart failure, but their effects on nocturnal respiratory parameters remain underexplored. Objectives: This study aims to evaluate the impact of SGLT2i therapy on key respiratory and cardiac indices including CSR burden, oxygenation, and right heart function in patients with ADHF and reduced left ventricular ejection fraction. Methods: In this single-center prospective cohort study, 60 patients with ADHF, LVEF < 40%, and a baseline apnea–hypopnea index (AHI) > 5 were assessed before and three months after the initiation of SGLT2i therapy. Sleep respiratory parameters were measured using home polygraphy (ApneaLink^TM), while cardiac and renal indices were evaluated by echocardiography, NT-proBNP, and the estimated glomerular filtration rate (eGFR). Structural and functional echocardiographic changes were analyzed both at baseline and following the 3-month treatment period. Patient-reported outcomes were assessed using the Epworth Sleepiness Scale (ESS) and Kansas City Cardiomyopathy Questionnaire (KCCQ). Results: After 3 months of SGLT2i therapy, significant improvements were observed in daytime sleepiness (ESS: −2.68 points; p < 0.001), CSR index (−5.63 events/h; p < 0.001), AHI (−3.07 events/h; p < 0.001), ODI (−6.11 events/h; p < 0.001), and mean nocturnal SpO₂ (+1.95%; p < 0.001). KCCQ scores increased by 9.16 points (p < 0.001), indicating improved quality of life. Cardiac assessments revealed reductions in NT-proBNP (−329.6 pg/mL; p < 0.001) and E/e′ ratio (−1.08; p < 0.001), with no significant change in LVEF or chamber dimensions. Right ventricular function improved, as evidenced by the increased TAPSE/sPAP ratio (+0.018; p < 0.001). Renal function remained stable, with a non-significant upward trend in eGFR. Conclusions: This exploratory study suggests that SGLT2 inhibitors may be associated with the attenuation of Cheyne–Stokes respiration and an improvement in right heart function in patients with ADHF, warranting further investigation in controlled trials. These findings highlight the potential of SGLT2is to address overlapping cardio-respiratory dysfunction in this high-risk population. Full article

The heart rate variability (HRV), influenced by female sex hormone fluctuations, is an indicator of athletes’ adaptation. This case study explores HRV responses over 18 months across a natural menstrual cycle (MC) and during oral contraceptive (OC) use in two Olympic female swimmers. HRV measurements—including mean heart rate (HR); root mean square of successive differences (RMSSD); and frequency-domain indices—were collected at rest in supine (SU) and standing (ST) positions across two competitive seasons. Nocturnal HR and RMSSD were assessed using the Ōura^® ring. MC and OC phases were identified through specific tracking, and training load was controlled. In both athletes, resting HR was lower during bleeding phases, increasing from menstruation to the luteal phase (MC) and from withdrawal to active pill phases (OC). In the ST position, RMSSD was higher but decreased throughout the phases. Nocturnal measurements confirmed these trends. Overall, findings suggest a phase-related parasympathetic overactivity shift. This study provides novel insights into HRV responses across hormonal cycles in elite female athletes, which present unique characteristics. Such monitoring tools may support a data-informed approach to guide and periodize training more effectively. Full article

Background and Objectives: The association between syncope and sleep apnea (SA) has been scarcely investigated. Dysfunction of the autonomic nervous system (ANS) may represent a shared pathophysiological mechanism. This study aimed to determine the prevalence of SA in patients with syncope of unclear cause (SUC), identify potential associated factors, and evaluate nocturnal heart rate variability (HRV) as a marker of ANS function. Materials and Methods: A prospective cohort study was conducted in adult patients diagnosed with SUC. Nocturnal cardiorespiratory polygraphy was performed to detect the presence of SA. A range of variables potentially associated with SA was collected. Both SA diagnosis and HRV parameters were assessed using the Embletta^® MPR polygraph system. Results: A total of 156 patients were enrolled (57% male), with a mean age of 64 years and a mean body mass index of 27.5 kg/m² (range: 24.8–32.2). Hypertension was present in 46% of the cohort. The overall prevalence of SA was 78.2% (95% CI: 71.7–84.4%), with 28.7% classified as severe. Age (OR = 1.04; 95% CI: 1.01–1.07) and BMI (OR = 1.17; 95% CI: 1.06–1.28) were independent predictors of SA. Mean RR interval was significantly lower in patients with SA compared to those without (942 ms vs. 995 ms; p = 0.04). No significant differences in HRV parameters were observed between the two groups. Conclusions: This study found a high prevalence (nearly 78%) of SA among adult patients with SUC, particularly in individuals over 50 years of age and those who were overweight. However, this association could not be predicted based on clinical variables alone. No significant differences in nocturnal HRV were detected between patients with SUC with and without SA. Full article

Background/Objectives: Alcohol consumption is known to influence cardiovascular regulation and sleep quality; however, real-world data on its acute effects—particularly during nocturnal rest—are limited. This study aimed to investigate the impact of moderate alcohol intake on nocturnal resting heart rate (HR) and sleep parameters using continuous smartwatch-based monitoring in healthy individuals. Methods: In this prospective observational study, 40 healthy adults (63% female, mean age of 30.5 years) underwent a structured 9-day smartwatch monitoring period. The protocol included three alcohol-free baseline days, three consecutive evenings with moderate alcohol consumption (40 g/day for women, 60 g/day for men), and three post-exposure days. Continuous data on HR, sleep stages, nocturnal awakenings, and physical activity were recorded. Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) at baseline. The primary endpoint was the change in the average nocturnal resting HR. Secondary outcomes included sleep parameters and activity levels. Results: Alcohol consumption led to a statistically significant increase in nocturnal resting HR from 63.6 ± 9.2 bpm at baseline to 66.6 ± 9.0 bpm during exposure (p < 0.001), with rapid normalization during the post-exposure phase (64.9 ± 9.3 bpm). No significant changes were observed in objective sleep architecture or daytime activity. Despite stable sleep structure, participants reported reduced subjective sleep quality under alcohol exposure, suggesting a potential link to the elevated HR. Conclusions: Even moderate alcohol intake transiently elevates nocturnal resting HR without affecting sleep architecture, likely impairing physiological recovery. These findings underscore the underestimated cardiovascular impact of alcohol and warrant further research in larger and more diverse populations. Full article

This study aimed to explore the sleep conditions and obstructive sleep apnea (OSA) risk in athletes with poor sleep quality. Athletes with poor sleep quality before competition were recruited. Cardiopulmonary coupling analysis, the Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, and Insomnia Severity Index were used to assess and compare athletes at risk of OSA (apnea-hypopnea index (AHI) ≥ 5 events per hour) with those not at risk (AHI < 5 events per hour). Comparisons were made between the non-OSA group (n = 23) and the OSA risk group (n = 19, AHI = 10.79 ± 4.47 events per hour). The OSA risk group exhibited a significantly higher percentage of Stage 1 (S1) and Stage 2 (S2) sleep and greater heart rate variability (HRV) (p < 0.05). Positive correlations were found between AHI and the percentage of S1 and S2 sleep, low-frequency (LF), and the LF/HF ratio (p < 0.05). Conversely, significant negative correlations were observed between AHI and the percentage of Stage 3 (S3) and Stage 4 (S4) sleep, as well as HRV (p < 0.05). Athletes with poor sleep quality and high OSA risk demonstrated reduced parasympathetic activity, increased sympathetic activity, and affected sympathovagal balance during nocturnal HRV. Full article

Previous studies on the effects of intensified training on sleep quality/quantity have been somewhat contradictory. Moreover, recreational athletes often track various sleep metrics, and those metrics’ actual connections to training adaptations are unknown. This study explored the effects of intensified training on sleep and nightly recovery along with their associations with training adaptations. A total of 24 participants (10 females) performed a 3-week baseline training period (BL), a 2-week overload period (OL), and a 1-week recovery period (REC), which were followed by test days (T1–T3). The endurance performance was assessed with a 3000 m running test. Throughout all of the periods, the nightly recovery information was monitored with a wrist-worn wearable, including sleep quantity and quality, heart rate (HR) and HR variability (HRV), and proprietary parameters combining several parameters and scaling the results individually. In addition, the perceived strain and muscle soreness were evaluated daily. The 3000 m running performance improved from T1 to T2 (−1.2 ± 1.7%, p = 0.006) and from T1 to T3 (−1.7 ± 1.2%, p = 0.002). The perceived strain and muscle soreness increased (p < 0.001) from the final week of the BL to the final week of the OL, but the subjective sleep quality and nightly recovery metrics remained unchanged. The OL average of the proprietary parameter, autonomic nervous system charge (“ANS charge”, combining the HR, HRV, and breathing rate), as well as the change in the sleep HR and HRV from the BL to the OL, were associated (p < 0.05) with a change in the 3000 m running time. In conclusion, the subjective recovery metrics were impaired by intensified training, while the sleep and nightly recovery metrics showed no consistent changes. However, there were substantial interindividual differences in nightly recovery, which were also associated with the training adaptations. Therefore, monitoring nightly recovery can help in recognizing individual responses to training and assist in optimizing training prescriptions. Full article

Cardiovascular diseases are a major cause of mortality worldwide. Long-term monitoring of nighttime heart rate (HR) and heart rate variability (HRV) may be useful in identifying latent cardiovascular risk. The Oura Ring has shown excellent correlation only with ECG-derived HR, but not HRV. We thus assessed if stringent data quality filters can improve the accuracy of time-domain and frequency-domain HRV measures. 92 younger (<45 years) and 22 older (≥45 years) participants from two in-lab sleep studies with concurrent overnight Oura and ECG data acquisition were analyzed. For each 5 min segment during time-in-bed, the validity proportion (percentage of interbeat intervals rated as valid) was calculated. We evaluated the accuracy of Oura-derived HR and HRV measures against ECG at different validity proportion thresholds: 80%, 50%, and 30%; and aggregated over different durations: 5 min, 30 min, and Night-level. Strong correlation and agreements were obtained for both age groups across all HR and HRV metrics and window sizes. More stringent validity proportion thresholds and averaging over longer time windows (i.e., 30 min and night) improved accuracy. Higher discrepancies were found for HRV measures, with more than half of older participants exceeding 10% Median Absolute Percentage Error. Accurate HRV measures can be obtained from Oura’s PPG-derived signals with a stringent validity proportion threshold of around 80% for each 5 min segment and aggregating over time windows of at least 30 min. Full article

Nocturnal sympathetic overdrive is an early indicator of cardiovascular (CV) disease, emphasizing the importance of reliable remote patient monitoring (RPM) for autonomic function during sleep. To be effective, RPM systems must be accurate, non-intrusive, and cost-effective. This review evaluates non-invasive technologies, metrics, and algorithms for tracking nocturnal autonomic nervous system (ANS) activity, assessing their CV relevance and feasibility for integration into RPM systems. A systematic search identified 18 relevant studies from an initial pool of 169 publications, with data extracted on study design, population characteristics, technology types, and CV implications. Modalities reviewed include electrodes (e.g., electroencephalography (EEG), electrocardiography (ECG), polysomnography (PSG)), optical sensors (e.g., photoplethysmography (PPG), peripheral arterial tone (PAT)), ballistocardiography (BCG), cameras, radars, and accelerometers. Heart rate variability (HRV) and blood pressure (BP) emerged as the most promising metrics for RPM, offering a comprehensive view of ANS function and vascular health during sleep. While electrodes provide precise HRV data, they remain intrusive, whereas optical sensors such as PPG demonstrate potential for multimodal monitoring, including HRV, SpO2, and estimates of arterial stiffness and BP. Non-intrusive methods like BCG and cameras are promising for heart and respiratory rate estimation, but less suitable for continuous HRV monitoring. In conclusion, HRV and BP are the most viable metrics for RPM, with PPG-based systems offering significant promise for non-intrusive, continuous monitoring of multiple modalities. Further research is needed to enhance accuracy, feasibility, and validation against direct measures of autonomic function, such as microneurography. Full article

Sleep apnea (SA) is a prevalent disorder characterized by recurrent events of nocturnal apnea. Polysomnography (PSG) represents the gold standard for SA diagnosis. This laboratory-based procedure is complex and costly, and less cumbersome wearable devices have been proposed for SA detection and monitoring. A novel textile multi-sensor monitoring belt recording electrocardiogram (ECG) and breathing frequency (BF) measured by thorax excursion was developed and tested in a sleep laboratory for validation purposes. The aim of the current study was to evaluate the diagnostic performance of ECG-derived heart rate variability and BF-derived breathing rate variability and their combination for the detection of sleep apnea in a population of patients with a suspicion of SA. Fifty-one patients with a suspicion of SA were recruited in the sleep laboratory of the Cantonal Hospital St. Gallen. Patients were equipped with the monitoring belt and underwent a single overnight laboratory-based PSG. In addition, some patients further tested the monitoring belt at home. The ECG and BF signals from the belt were compared to PSG signals using the Bland-Altman methodology. Heart rate and breathing rate variability analyses were performed. Features derived from these analyses were used to build a support vector machine (SVM) classifier for the prediction of SA severity. Model performance was assessed using receiver operating characteristics (ROC) curves. Patients included 35 males and 16 females with a median age of 49 years (range: 21 to 65) and a median apnea-hypopnea index (AHI) of 33 (IQR: 16 to 58). Belt-derived data provided ECG and BF signals with a low bias and in good agreement with PSG-derived signals. The combined ECG and BF signals improved the classification accuracy for SA (area under the ROC curve: 0.98; sensitivity and specificity greater than 90%) compared to single parameter classification based on either ECG or BF alone. This novel wearable device combining ECG and BF provided accurate signals in good agreement with the gold standard PSG. Due to its unobtrusive nature, it is potentially interesting for multi-night assessments and home-based patient follow-up. Full article

Nocturnal enuresis (NE) is involuntary bedwetting during sleep, typically appearing in young children. Despite the potential benefits of the long-term home monitoring of NE patients for research and treatment enhancement, this area remains underexplored. To address this, we propose NEcare, an in-home monitoring system that utilizes wearable devices and machine learning techniques. NEcare collects sensor data from an electrocardiogram, body impedance (BI), a three-axis accelerometer, and a three-axis gyroscope to examine bladder volume (BV), heart rate (HR), and periodic limb movements in sleep (PLMS). Additionally, it analyzes the collected NE patient data and supports NE moment estimation using heuristic rules and deep learning techniques. To demonstrate the feasibility of in-home monitoring for NE patients using our wearable system, we used our datasets from 30 in-hospital patients and 4 in-home patients. The results show that NEcare captures expected trends associated with NE occurrences, including BV increase, HR increase, and PLMS appearance. In addition, we studied the machine learning-based NE moment estimation, which could help relieve the burdens of NE patients and their families. Finally, we address the limitations and outline future research directions for the development of wearable systems for NE patients Full article

Obstructive sleep apnea (OSA) is characterized by repeated episodes of upper airway obstruction during sleep, and it is closely linked to several cardiovascular issues due to intermittent hypoxia, nocturnal hypoxemia, and disrupted sleep patterns. Pulmonary hypertension (PH), identified by elevated pulmonary arterial pressure, shares a complex interplay with OSA, contributing to cardiovascular complications and morbidity. The prevalence of OSA is alarmingly high, with studies indicating rates of 20–30% in males and 10–15% in females, escalating significantly with age and obesity. OSA’s impact on cardiovascular health is profound, particularly in exacerbating conditions like systemic hypertension and heart failure. The pivotal role of hypoxemia increases intrathoracic pressure, inflammation, and autonomic nervous system dysregulation in this interplay, which all contribute to PH’s pathogenesis. The prevalence of PH among OSA patients varies widely, with studies reporting rates from 15% to 80%, highlighting the variability in diagnostic criteria and methodologies. Conversely, OSA prevalence among PH patients also remains high, often exceeding 25%, stressing the need for careful screening and diagnosis. Treatment strategies like continuous positive airway pressure (CPAP) therapy show promise in mitigating PH progression in OSA patients. However, this review underscores the need for further research into long-term outcomes and the efficacy of these treatments. This review provides comprehensive insights into the epidemiology, pathophysiology, and treatment of the intricate interplay between OSA and PH, calling for integrated, personalized approaches in diagnosis and management. The future landscape of OSA and PH management hinges on continued research, technological advancements, and a holistic approach to improving patient outcomes. Full article

Obstructive sleep apnea (OSA), a prevalent sleep disorder, is intimately associated with various other diseases, particularly cardiovascular conditions. The conventional diagnostic method, nocturnal polysomnography (PSG), despite its widespread use, faces challenges due to its high cost and prolonged duration. Recent developments in electrocardiogram-based diagnostic techniques have opened new avenues for addressing these challenges, although they often require a deep understanding of feature engineering. In this study, we introduce an innovative method for OSA classification that combines a composite deep convolutional neural network model with a multimodal strategy for automatic feature extraction. This approach involves transforming the original dataset into scalogram images that reflect heart rate variability attributes and Gramian angular field matrix images that reveal temporal characteristics, aiming to enhance the diversity and richness of data features. The model comprises automatic feature extraction and feature enhancement components and has been trained and validated on the PhysioNet Apnea-ECG database. The experimental results demonstrate the model’s exceptional performance in diagnosing OSA, achieving an accuracy of 96.37%, a sensitivity of 94.67%, a specificity of 97.44%, and an AUC of 0.96. These outcomes underscore the potential of our proposed model as an efficient, accurate, and convenient tool for OSA diagnosis. Full article

Changes in the activity of the renin–angiotensin–aldosterone system are responsible for a stable shift in the regulation of the cardiovascular system in essential hypertension (EH). They can be characterized as hemodynamic allostasis. The purpose of our study was to determine the role of hemodynamic parameters in allostatic load in patients with EH without metabolic syndrome. Twenty-four hours of ambulatory blood pressure monitoring was performed, followed by linear and non-linear rhythm analysis. Based on the daily index, patients with EH were divided into two groups: group 1—patients with no significant nighttime decrease in blood pressure (BP); group 2—patients who had a nocturnal decrease in BP. The control group included healthy persons aged 25 to 69 years. A linear analysis was used to determine the mean values of systolic and diastolic BP, heart rate (HR), time load of BP, circadian index, and structural point of BP. Non-linear analysis was applied to determine the mesor, amplitude, range of oscillations and % rhythm of BP and HR. The allostatic load index (ALI) was also calculated on the basis of the corresponding biomarkers. It was found that ALI was significantly higher in groups 1 and 2 in comparison with the control group. The hemodynamic mechanisms of this increase were different. Full article

Bruxism is a parafunctional activity represented by the gnashing and clenching of one’s teeth. The aim of this study was to determine the utility of screening and monitoring with a Bruxoff device during nocturnal bruxism in 51 children and adolescents (36 with bruxism and 15 without bruxism) by assessing the variations in the intensity and duration of parafunctional activity in each patient. Bruxoff measurements were recorded for at least 60 min for three consecutive nights for each subject. All the parameters recorded using Bruxoff in the control and the study groups showed a statistically significant difference (p < 0.05). The differences found by comparing the values recorded in the male and female study groups are significant for heart rate, the number of masseter muscle contractions during one night, and mixed contractions. The Bruxoff device proved to be important in diagnosing patients with bruxism in our practice. Full article