Single-channel airflow monitors developed for screening of sleep-disordered breathing (SDB) have conflicting results for accuracy. It was hypothesised that the analytical algorithm is crucial for the performance and the present authors tried to develop a novel computer algorithm.A total of 399 polysomnography (PSG) records were employed, including a thermal sensor signal. The first 100 records were used in the development of the algorithm and the remainder for validation. In addition, 119 PSG records, including a thermocouple signal and a nasal pressure signal, were used for the validation. The algorithm was designed to obtain a time series (flowpower) using power spectral analysis, which expresses fluctuation in the airflow signal amplitude. From the time series the algorithm detects transient falls of the flow-power and calculates flowrespiratory disturbance index (RDI), defined as the number of falls per hour.In the validation group, the areas under receiver operating characteristic curves for diagnosis of SDB (apnoea/hypopnoea index o5) were 0.96, 0.95 and 0.95, for the records of the thermal sensor, thermocouple and nasal pressure system, respectively. The diagnostic sensitivity/ specificity ratios of the flow-RDI were 96/76, 88/80 and 97%/77%, respectively.The present results suggest that a single-channel airflow monitor can be used to detect sleepdisordered breathing automatically if the analytic algorithm is optimised.

Objective: To investigate predictors of dropout from a group cognitive behavioral therapy (CBT) intervention for overweight or obese women. Methods: 119 overweight and obese Japanese women aged 25-65 years who attended an outpatient weight loss intervention were followed throughout the 7-month weight loss phase. Somatic characteristics, socioeconomic status, obesity-related diseases, diet and exercise habits, and psychological variables (depression, anxiety, self-esteem, alexithymia, parenting style, perfectionism, and eating attitude) were assessed at baseline. Significant variables, extracted by univariate statistical analysis, were then used as independent variables in a stepwise multiple logistic regression analysis with dropout as the dependent variable. Results: 90 participants completed the weight loss phase, giving a dropout rate of 24.4%. The multiple logistic regression analysis demonstrated that compared to completers the dropouts had significantly stronger body shape concern, tended to not have jobs, perceived their mothers to be less caring, and were more disorganized in temperament. Of all these factors, the best predictor of dropout was shape concern. Conclusion: Shape concern, job condition, parenting care, and organization predicted dropout from the group CBT weight loss intervention for overweight or obese Japanese women.

A simple screening method for sleep-disordered breathing (SDB) is desirable for primary care practices. In the present study, a simple monitor, which utilises a new type of flow sensor and a novel algorithm, was prospectively validated.Home recording for 2 nights with the monitor only, followed by in-laboratory recording with the monitor together with polysomnography, were carried out in consecutive patients (n5100) suspected of SDB. A subjective sleep log was also recorded. The signal was analysed using power spectral analysis, which yielded the flow respiratory disturbance index (flow-RDI).There was no recording failure at home. The reproducibility of the flow-RDI between the 2 nights at home was high (intraclass correlation coefficient50.92). The sensitivity and specificity of the in-laboratory flow-RDI to diagnose SDB were 0.96 and 0.82, 0.91 and 0.82, and 0.89 and 0.96, for apnoea/hypopnoea index (AHI) o5, o15 and o30 events?h -1 , respectively. The diagnostic ability in low-severity subgroups (female, normal weight, AHI ,15 events?h -1 ) was almost comparable to that in the entire group. Excluding subjective waking time on the sleep log from the recording time had no significant effect on the flow-RDI. The single-channel monitor is considered feasible for ambulatory sleep disordered breathing monitoring because of its easy applicability, high reproducibility and relatively high agreement with polysomnography results.

Study Objectives: Portable devices for home sleep apnea testing are often limited by their inability to discriminate sleep/wake status, possibly resulting in underestimations. Tracheal sound (TS), which can be visualized as a spectrogram, carries information about apnea/hypopnea and sleep/wake status. We hypothesized that image analysis of all-night TS recordings by a deep neural network (DNN) would be capable of detecting breathing events and classifying sleep/wake status. The aim of this study is to develop a DNN-based system for sleep apnea testing and validate it using a large sampling of polysomnography (PSG) data. Methods: PSG examinations for the evaluation of sleep-disordered breathing (SDB) were performed for 1,852 patients: 1,548 PSG records were used to develop the system, and the remaining 304 records were used for validation. TS spectrogram images were obtained every 60 seconds and labeled with the PSG scoring results (breathing event and sleep/wake status), then introduced to DNN learning. Two different DNNs were trained for breathing status and sleep/wake status, respectively. Results: A DNN with convolutional layers showed the best performance for discriminating breathing status. The same DNN structure was trained for sleep/wake discrimination. In the validation study, the DNN analysis was capable of discriminating the sleep/wake status with reasonable accuracy. The diagnostic sensitivity, specificity, and area under the receiver operating characteristic curves for diagnosis of SDB with apnea-hypopnea index of > 5, 15, and 30 were 0.98, 0.76, and 0.99; 0.97, 0.90, and 0.99; and 0.92, 0.94, and 0.98, respectively. Conclusions: The developed system using the TS DNN analysis has a good performance for SDB testing.