Spirometry is a crucial assessment for detecting respiratory obstructive diseases such as chronic obstructive pulmonary disease (COPD). While there are several respiratory trainers and spirometers available on the market, incorrect blowing techniques may render key metrics like forced vital capacity (FVC), forced expiratory volume in first second (FEV1), and Peak Expiratory Flow (PEF) unreliable, necessitating supervised testing by healthcare professionals in hospital settings. Our system integrates the functionalities of a spirometer and respiratory trainer. After conducting pulmonary function tests using the App, users' blowing waveforms are measured with an airflow sensor. The App provides guidance for lung capacity tests and a gaming interface for respiratory training, storing all data in a MySQL database. This study categorizes common blowing waveforms into six types using machine learning models and dynamic time warping (DTW) distance calculations. Based on the classification results, users are directed to appropriate respiratory training sessions to help them master correct testing techniques. Analysis of the results indicates that using Mel Frequency Cepstral Coefficients (MFCC) feature extraction with Random Forest for classification yields optimal outcomes.

This article proposes an interpretable fuzzy estimation system (IFES) for fast estimation of severity of obstructive sleep apnea-hypopnea syndrome (OSAHS) using three easily available physiological variables: neck circumference, waist circumference, and average blood pressure after waking up. The IFES aims to decrease the waiting list of polysomnography so that OSAHS treatment can be performed as soon as possible. The IFES is optimized to achieve the following three objectives: high estimation accuracy, the largest number of applicable subjects, and high model interpretability. The applicable subjects are determined by evaluating the influence of six screening factors, such as smoking, hypertension, and sleep efficiency, on the estimation performance. This article finds solutions of this multiobjective optimization problem using a multiobjective genetic algorithm. A total of 1197 participants are enrolled with the five-fold cross-validation scheme employed to evaluate an estimation performance. Experimental results show that the proposed method successfully finds the influence of different screening factors and the found IFESs outperform different estimation models used for comparison.

Manual titration of positive airway pressure (PAP) is a gold standard to provide an optimal pressure for the treatment of obstructive sleep apnea-hypopnea syndrome (OSAS). Since manual titration studies were costly and time-consuming, many statistical models for predicting effective PAPs were reported. However, the prediction accuracies of the models associated with nocturnal parameters still remain low. This study proposes a fuzzy neural prediction network (FNPN) with input candidate variables, selected among easily available measurements (e.g., body mass index (BMI), waist circumstance (WC), and body composition) and OSAS related questionnaires, to rapidly predict an optimal PAP. The FNPN comprises fuzzy rules and is characterized with the ability of automatic rule growing and pruning from training data. A total of 147 participants from April 2018 to April 2019 were enrolled in Taichung Veterans General Hospital, Taiwan. After two selection processes for feature extraction, WC and BMI were the significant variables for entering the FNPN to predict optimal PAP. Experimental results showed that the average successful prediction rate of the proposed method was 71.8%. This study also found that Epworth sleepiness scales (ESS) and body composition, such as visceral fat area and percent body fat, were excluded in the final prediction model. Compared with existing models, the proposed prediction approach provided a rapid prediction of optimal PAP with higher accuracy.

This paper proposes a neural fuzzy evaluation system (NFES) with significant variables selected from stepwise regression to predict apnea-hypopnea index (AHI) for evaluating obstructive sleep apnea (OSA). The variables considered are the change statuses of blood pressure (BP) before going to sleep and early in the morning as well as other five easily available measurements (age, body mass index (BMI), etc.) so that users can use the system for self-evaluation of OSA. A total of 150 subjects are reviewed retrospectively and categorized as training (120 subjects) and validation (30 subjects) sets by a fivefold cross-validation scheme with stratified sampling based on the OSA severity. Among the eight variables, the stepwise regression shows that BMI, the difference of systolic BP, and Epworth Sleepiness Scale were the significant factors to predict AHI. The three variables are fed as inputs to the NFES with interpretable fuzzy rules automatically generated from the training set. The average accuracy, sensitivity (Sn), specificity (Sp), and Sn+Sp-1 of the NFES were 75.6%, 77.2%, 75.0%, and 0.552, respectively, in distinguishing the OSA level of normal-mild (AHI <15) from moderate-severe (AHI ≱ 15), and outperformed the stepwise regression, back-propagation neural network, and support vector machine models. In addition to personal self-estimation, physicians could differentiate the two OSA levels by means of the fast-screening system for both outpatients and inpatients.

The Electroencephalogram (EEG) signals reflect local potential changes that occur on pyramidal cell. Based on the received signals, epileptic seizure and sleep patterns can be monitored and diagnosed. This paper proposes an asynchronous pervasive sensing method using cooperative communication and networking between a pair of sensors for wireless sleep EEG signal collection. A theoretical analysis and experiments are developed to assess the impact of time synchronization errors and the performance of the proposed approach, which may provide a sensible way for practical system design.

Block Truncation Coding (BTC) is an efficient technology for image compression. An improved BTC algorithm, namely Ordered Dither Block Truncation Coding (ODBTC), is presented in this study. In order to provide better image quality, the void-and-cluster halftoning is combined with the BTC. The ODBTC results show that the image quality is improved when it is operated in high coding gain applications. Another feature of the ODBTC is the dither array Look Up Table (LUT), which significantly reduces the complexity compared to the BTC.