Screening of sleep apnea based on heart rate variability and long short-term memory

Purpose: Sleep apnea syndrome (SAS) is a prevalent sleep disorder in which apnea and hypopnea occur frequently during sleep and result in increase of the risk of lifestyle-related disease development as well as daytime sleepiness. Although SAS is a common sleep disorder, most patients remain undiagnosed because the gold standard test polysomnography (PSG), is high-cost and unavailable in many hospitals. Thus, an SAS screening system that can be used easily at home is needed. Methods: Apnea during sleep affects changes in the autonomic nervous function, which causes fluctuation of the heart rate. In this study, we propose a new SAS screening method that combines heart rate measurement and long short-term memory (LSTM) which is a type of recurrent neural network (RNN). We analyzed the data of intervals between adjacent R waves (R-R interval; RRI) on the electrocardiogram (ECG) records, and used an LSTM model whose inputs are the RRI data is trained to discriminate the respiratory condition during sleep. Results: The application of the proposed method to clinical data showed that it distinguished between patients with moderate-to-severe SAS with a sensitivity of 100% and specificity of 100%, results which are superior to any other existing SAS screening methods. Conclusion: Since the RRI data can be easily measured by means of wearable heart rate sensors, our method may prove to be useful as an SAS screening system at home

Prevalence and cumulative incidence of autism spectrum disorders and the patterns of co-occurring neurodevelopmental disorders in a total population sample of 5-year-old children.

BackgroundsWhether there is a true increase in autism spectrum disorder (ASD) frequency or not remains unclear. Additionally, the rates of co-existing neurodevelopmental disorders (NDD) in a total population sample has not been fully examined before. Therefore, using a total population sample in Japan, we aimed to estimate the prevalence and cumulative incidence of autism spectrum disorder (ASD) annually, to determine whether there is a true increase in ASD prevalence by estimating the cumulative incidence of ASD annually, and to examine the rates of co-existing neurodevelopmental disorders (NDD).MethodIn this cross-sectional sequential design study, all 5-year-old children in the catchment area underwent the screening annually from the year 2013-2016. Screen-positive children were invited to participate in a comprehensive assessment, including child and parent interview, behavioral observation, and cognitive and motor function testing. All cases were reviewed by a multidisciplinary research team.ResultsCaregivers of 3954 children returned the screening, among which 559 children underwent the assessment with 87 children receiving an ASD diagnosis. Adjusted ASD prevalence was 3.22% (95% confidence interval (CI) 2.66-3.76%). The male to female ratio of the crude prevalence was 2.2:1. The cumulative incidence of ASD up to 5 years of age for the total study years was 1.31% (95% CI 1.00-1.62%). A generalized linear model revealed no significant linear trends in 5-year cumulative incidence over the study years. Only 11.5% of children had ASD alone; the remaining 88.5% were found to have at least one co-existing NDD.LimitationsModest sample size for a total population study.ConclusionsOur findings demonstrate the stability of the 5-year cumulative incidence of ASD, implying no true rise in ASD incident cases over the 4-year study period in the study catchment area. High rates of co-existing NDDs reflect the importance of investigating broad developmental challenges in children with ASD

Time series analysis of daily reported number of new positive cases of COVID-19 in Japan from January 2020 to February 2023.

This study investigated temporal variations of the COVID-19 pandemic in Japan using a time series analysis incorporating maximum entropy method (MEM) spectral analysis, which produces power spectral densities (PSDs). This method was applied to daily data of COVID-19 cases in Japan from January 2020 to February 2023. The analyses confirmed that the PSDs for data in both the pre- and post-Tokyo Olympics periods show exponential characteristics, which are universally observed in PSDs for time series generated from nonlinear dynamical systems, including the so-called susceptible/exposed/infectious/recovered (SEIR) model, well-established as a mathematical model of temporal variations of infectious disease outbreaks. The magnitude of the gradient of exponential PSD for the pre-Olympics period was smaller than that of the post-Olympics period, because of the relatively high complex variations of the data in the pre-Olympics period caused by a deterministic, nonlinear dynamical system and/or undeterministic noise. A 3-dimensional spectral array obtained by segment time series analysis indicates that temporal changes in the periodic structures of the COVID-19 data are already observable before the commencement of the Tokyo Olympics and immediately after the introduction of mass and workplace vaccination programs. Additionally, the possibility of applying theoretical studies for measles control programs to COVID-19 is discussed