Introduction: Malaysia has one of the highest diabetes prevalence rates in Asia (1 in 5), with >50% fail to achieve optimal glycemic control. Continuous glucose monitoring (CGM) provides detailed insights beyond hemoglobin A1c (HbA1c), capturing daily glucose fluctuations and variability. Realworld data describing CGM patterns and their clinical associations in Malaysian adults with type 2 diabetes mellitus (T2DM) are limited. Methodology: This cross-sectional study analyzed CGM data from 25 Malaysian adults with T2DM, standardized over 14 days. CGM metrics included time in range (TIR), time above range (TAR), time below range (TBR), mean glucose, and coefficient of variation (CV). Weekday–weekend comparisons were performed, and correlations with age, diabetes duration, HbA1c, body mass index (BMI), treatment regimen, and complications were assessed. Results: Participants had a mean age of 56.2 ± 13.2 years (52% male), mean HbA1c 9.4 ± 2.7%, diabetes duration 10.7 ± 9.0 years, and BMI 29.8 ± 9.4 kg/m². Mean TIR, TAR, TBR, mean glucose, and CV were similar between weekdays and weekends (p >0.34 for all). TIR >70% was achieved by 52% of participants on weekdays and 48% on weekends, with no statistically significant difference (p = 0.75). Longer diabetes duration correlated with lower TIR (r = −0.62, p <0.001), higher mean glucose (r = 0.58, p = 0.002), and greater variability (r = 0.54, p = 0.004). Older age was associated with lower TIR (r = −0.48, p = 0.014) and higher mean glucose (r = 0.44, p = 0.025). Higher HbA1c correlated with lower TIR (r = −0.36, p = 0.04), higher TAR (r = 0.35, p = 0.05), greater variability (r = 0.51, p = 0.006), and increased target organ damage (ρ = 0.55, p = 0.004). BMI was not associated with TIR (r = −0.18, p = 0.38). Participants with ≥2 complications had lower TIR (55.8 ± 28.4% vs 76.7 ± 19.2%, p = 0.073), while insulin the Conclusion In Malaysian adults with T2DM, CGM metrics were similar between weekdays and weekends, indicating lifestyle differences had minimal impact on glycemic control. Longer diabetes duration, older age, higher HbA1c, multiple complications, and insulin therapy identified patients at highest risk for poor glycemic control, greater variability, and hypoglycemia. These findings support the use of CGM for risk stratification, individualized monitoring, and therapy optimization to reduce complications and hypoglycemia risk.
Adult ; Blood Glucose ; Blood Glucose Self-Monitoring ; Continuous Glucose Monitoring ; Diabetes Mellitus, Type 2

Introduction: ISPAD guidelines recommend continuous glucose monitoring (CGM) as the standard of care for paediatric type 1 diabetes (T1DM). However, a “real-world” adoption gap persists, particularly in resource-limited settings. The Introductions of the study were to evaluate CGM adoption prevalence, identify documented barriers, and compare glycemic outcomes between active and non-active users. Methodology: This retrospective review analyzed electronic medical records (EMR) of 125 paediatric T1DM patients at Hospital Putrajaya (2025). Data included CGM status, insulin delivery method, and documented barriers. Independent T-tests compared mean hemoglobin A1c (HbA1c) between groups, and multivariable logistic regression identified independent predictors of adoption. Results: Cohort mean age was 11.8 ± 3.8 years. Active CGM users were 32.8% (n = 41), of whom 29.3% (n = 12) utilized predominantly automated insulin delivery (AID) systems. The remaining 67.2% (n = 84) were classified as non-active users, comprising both never-users and ex-users (discontinued use). Active users achieved significantly lower mean HbA1c than non-active users (8.73% vs 9.86%; p <0.001), with no significant difference in rates of DKA (p = 0.564) or severe hypoglycemia (p = 0.250). Among never-users, 42.9% lacked documented technology counselling (p = 0.001). Multivariable analysis identified funding source as the sole independent predictor of CGM adoption (adjusted OR = 7.76, p <0.001). While the primary documented barrier was financial (31.0%), a lack of documented barriers was noted in 61.9% of non-active users. Conclusion A substantial technology gap exists, primarily driven by financial access rather than clinical demographics. The difference of 1.13% in HbA1c between groups underscores the need to address financial setbacks to improve technology access in Malaysia and prevent diabetes complications.
Child ; Blood Glucose ; Blood Glucose Self-Monitoring ; Continuous Glucose Monitoring ; Diabetes Mellitus, Type 1

Sleep-related hypermotor epilepsy （SHE） is a rare type of epilepsy with a prevalence rate of approximately 1.8/100 000. This disease mainly manifests as complex motor behaviors during non-rapid eye movement sleep， such as leg kicking， arm waving， and sitting up. Since such symptoms are similar to non-epileptic disorders such as night terrors and sleepwalking and abnormal discharges may not be observed on electroencephalography， the diagnosis of SHE is quite challenging. Currently， there is still a lack of evidence from large-scale randomized controlled studies to support pharmacological treatment strategies for SHE， and related data in China remain scarce. This article reports a case of SHE， in order to provide a clinical reference for the diagnosis and medication treatment of this disease.
Polysomnography

Objective To investigate the sleep architecture of sleep related bruxism（SB）in adults and the sleep architecture of SB comorbid with obstructive sleep apnea hypopnea syndrome（OSAHS），as well as their correlation with age and other factors. Methods A total of 51 subjects with SB and 67 controls were included in this study to analyze the sleep architecture of SB and compare the sleep architecture of SB comorbid with different severities of OSAHS. Results Compared with the control group，the SB group had a younger age，increases in N1（%TST）and N2（%TST），a reduction in N3（%TST），and an increase in arousal index. The SB group was divided into non-OSAHS group（group 1），mild OSAHS group（group 2），and moderate-to-severe OSAHS group（group 3）. Group 1 had a younger age than group 2 and group 3，and group 3 had increases in body mass index（BMI），N1（%TST），oxygen desaturation index（ODI），and arousal index and a reduction in N3（%TST）. The Spearman's rank correlation analysis showed that BMI，N1（%TST），arousal index，and ODI increased with the increase in apnea-hypopnea index（AHI），while N3（%TST）decreased with the increase in AHI. The binary logistic regression analysis showed that SB was negatively correlated with age and was positively correlated with arousal index. Conclusion SB may affect sleep architecture by increasing light sleep，reducing deep sleep，and increasing the number of awakenings. There are changes in sleep architecture in case of SB comorbid with different severities of OSAHS. SB is negatively correlated with age and is positively correlated with arousal index.
Polysomnography

INTRODUCTION: Obstructive sleep apnoea (OSA) is a serious but underdiagnosed condition. Demand for the gold standard diagnostic polysomnogram (PSG) far exceeds its availability. More efficient diagnostic methods are needed, even in tertiary settings. Machine learning (ML) models have strengths in disease prediction and early diagnosis. We explored the use of ML with oximetry, demographic and anthropometric data to diagnose OSA. METHODS: A total of 2,996 patients were included for modelling and divided into test and training sets. Seven commonly used supervised learning algorithms were trained with the data. Sensitivity (recall), specificity, positive predictive value (PPV) (precision), negative predictive value, area under the receiver operating characteristic curve (AUC) and F1 measure were reported for each model. RESULTS: In the best performing four-class model (neural network model predicting no, mild, moderate or severe OSA), a prediction of moderate and/or severe disease had a combined PPV of 94%; one out of 335 patients had no OSA and 19 had mild OSA. In the best performing two-class model (logistic regression model predicting no-mild vs. moderate-severe OSA), the PPV for moderate-severe OSA was 92%; two out of 350 patients had no OSA and 26 had mild OSA. CONCLUSION Our study showed that the prediction of moderate-severe OSA in a tertiary setting with an ML approach is a viable option to facilitate early identification of OSA. Prospective studies with home-based oximeters and analysis of other oximetry variables are the next steps towards formal implementation.
Humans ; Oximetry/methods* ; Sleep Apnea, Obstructive/diagnosis* ; Male ; Female ; Middle Aged ; Machine Learning ; Polysomnography ; Adult ; Anthropometry ; ROC Curve ; Aged ; Algorithms ; Predictive Value of Tests ; Sensitivity and Specificity ; Neural Networks, Computer ; Demography

INTRODUCTION: Arrhythmias, especially atrial fibrillation (AF) and ventricular arrhythmias, are independent risk factors of mortality in patients with ischaemic heart disease (IHD). While there is a growing body of evidence that suggests an association between obstructive sleep apnoea (OSA) and cardiac arrhythmias, evidence on this relationship in patients with IHD has been scant and inconsistent. We hypothesised that in patients with IHD, severe OSA is associated with an increased risk of nocturnal arrhythmias. METHODS: We studied 103 consecutive patients with IHD who underwent an overnight polysomnography. Exposed subjects were defined as patients who had an apnoea-hypopnoea index (AHI) ≥30/h (severe OSA), and nonexposed subjects were defined as patients who had an AHI <30/h (nonsevere OSA). All electrocardiograms (ECGs) were interpreted by the Somte ECG analysis software and confirmed by a physician blinded to the presence or absence of exposure. Arrhythmias were categorised as supraventricular and ventricular. Arrhythmia subtypes (ventricular, atrial and conduction delay) were analysed as dichotomous outcomes using multiple logistic regression models. RESULTS: Atrial fibrillation and AF/flutter (odds ratio 13.5, 95% confidence interval 1.66-109.83; P = 0.003) were found to be more common in the severe OSA group than in the nonsevere OSA group. This association remained significant after adjustment for potential confounders. There was no significant difference in the prevalence of ventricular and conduction delay arrhythmias between the two groups. CONCLUSION In patients with IHD, there was a significant association between severe OSA and nocturnal AF/flutter. This underscores the need to evaluate for OSA in patients with IHD, as it may have important implications on clinical outcomes.
Humans ; Sleep Apnea, Obstructive/diagnosis* ; Atrial Fibrillation/diagnosis* ; Male ; Female ; Middle Aged ; Polysomnography ; Electrocardiography ; Myocardial Ischemia/complications* ; Aged ; Risk Factors ; Logistic Models

Humans ; Drug Monitoring/methods* ; Child ; Inflammatory Bowel Diseases/drug therapy* ; Immunosuppressive Agents/therapeutic use* ; China ; Biological Products/therapeutic use*

Cardiotocography (CTG) is a non-invasive and important tool for diagnosing fetal distress during pregnancy. To meet the needs of intelligent fetal heart monitoring based on deep learning, this paper proposes a TWD-MOAL deep active learning algorithm based on the three-way decision (TWD) theory and multi-objective optimization Active Learning (MOAL). During the training process of a convolutional neural network (CNN) classification model, the algorithm incorporates the TWD theory to select high-confidence samples as pseudo-labeled samples in a fine-grained batch processing mode, meanwhile low-confidence samples annotated by obstetrics experts were also considered. The TWD-MOAL algorithm proposed in this paper was validated on a dataset of 16 355 prenatal CTG records collected by our group. Experimental results showed that the algorithm proposed in this paper achieved an accuracy of 80.63% using only 40% of the labeled samples, and in terms of various indicators, it performed better than the existing active learning algorithms under other frameworks. The study has shown that the intelligent fetal heart monitoring model based on TWD-MOAL proposed in this paper is reasonable and feasible. The algorithm significantly reduces the time and cost of labeling by obstetric experts and effectively solves the problem of data imbalance in CTG signal data in clinic, which is of great significance for assisting obstetrician in interpretations CTG signals and realizing intelligence fetal monitoring.
Humans ; Pregnancy ; Female ; Cardiotocography/methods* ; Deep Learning ; Neural Networks, Computer ; Algorithms ; Fetal Monitoring/methods* ; Heart Rate, Fetal ; Fetal Distress/diagnosis* ; Fetal Heart/physiology*

Heart rate is a crucial indicator of human health with significant physiological importance. Traditional contact methods for measuring heart rate, such as electrocardiograph or wristbands, may not always meet the need for convenient health monitoring. Remote photoplethysmography (rPPG) provides a non-contact method for measuring heart rate and other physiological indicators by analyzing blood volume pulse signals. This approach is non-invasive, does not require direct contact, and allows for long-term healthcare monitoring. Deep learning has emerged as a powerful tool for processing complex image and video data, and has been increasingly employed to extract heart rate signals remotely. This article reviewed the latest research advancements in rPPG-based heart rate measurement using deep learning, summarized available public datasets, and explored future research directions and potential advancements in non-contact heart rate measurement.
Humans ; Deep Learning ; Heart Rate/physiology* ; Photoplethysmography/methods* ; Video Recording ; Face ; Monitoring, Physiologic/methods* ; Signal Processing, Computer-Assisted

In order to accurately capture the respiratory muscle movement and extract the synchronization signals corresponding to the breathing phases, a comprehensive signal sensing system for sensing the movement of the respiratory muscle was developed with applying the thin-film varistor FSR402 IMS-C07A in this paper. The system integrated a sensor, a signal processing circuit, and an application program to collect, amplify and denoise electronic signals. Based on the respiratory muscle movement sensor and a STM32F107 development board, an experimental platform was designed to conduct experiments. The respiratory muscle movement data and respiratory airflow data were collected from 3 healthy adults for comparative analysis. In this paper, the results demonstrated that the method for determining respiratory phase based on the sensing the respiratory muscle movement exhibited strong real-time performance. Compared to traditional airflow-based respiratory phase detection, the proposed method showed a lead times ranging from 33 to 210 ms [(88.3 ± 47.9) ms] for expiration switched into inspiration and 17 to 222 ms [(92.9 ± 63.8) ms] for inspiration switched into expiration, respectively. When this system is applied to trigger the output of the ventilator, it will effectively improve the patient-ventilator synchrony and facilitate the ventilation treatment for patients with respiratory diseases.
Humans ; Respiratory Muscles/physiology* ; Signal Processing, Computer-Assisted ; Movement/physiology* ; Respiration ; Monitoring, Physiologic/methods* ; Adult