A Fuzzy Neural Network Model for Rapid Prediction of Optimal Positive Airway Pressures in OSAS Patients

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.

Explainable fuzzy neural network with easy-to-obtain physiological features for screening obstructive sleep apnea-hypopnea syndrome

OBJECTIVE/BACKGROUND

Recently, several tools for screening obstructive sleep apnea-hypopnea syndrome (OSAHS) have been devised with varied shortcomings. To overcome these drawbacks, we aimed to propose a self-estimation method using an explainable prediction model with easy-to-obtain variables and evaluate its performance for predicting OSAHS.

PATIENTS/METHODS

This retrospective, cross-sectional study selected significant easy-to-obtain variables from patients, suspected of having OSAHS by regression analysis, and fed these variables into the proposed explainable fuzzy neural network (EFNN), a back propagation neural network (BPNN) and a stepwise regression model to compare the screening performance for OSAHS.

RESULTS

Of the 300 participants, three easily available features, such as waist circumference, mean blood pressure (BP) at the end of polysomnography and the difference in systolic BP between the end and start of polysomnography, were obtained from regression analysis with a five-fold cross-validation scheme. Feeding these three variables into the prediction models showed that the average prediction differences for apnea-hypopnea index (AHI) when using the EFNN, BPNN, and regression model were respectively 1.5 ± 18.2, 3.5 ± 19.1 and 0.1 ± 19.3, indicating none of the tested methods had good efficacy to predict the AHI values. The performance as determined by the sensitivity + specificity-1 value for screening moderate-to-severe OSAHS of the EFNN, BPNN and regression model were respectively 0.440, 0.414 and 0.380.

CONCLUSIONS

When fed with easy-to-obtain physiological features, the understandable EFNN should be the preferred method to predict moderate-to-severe OSAHS.

Personalized Rehabilitation Recognition for Ubiquitous Healthcare Measurements

The physical therapeutic application needs personalized rehabilitation recognition (PRR) for ubiquitous healthcare measurements (UHMs). This study employed the adaptive neuro-fuzzy inference system (ANFIS) to generate a PRR model for a self-development system of UHM. The subjects wore a sensor-enabled wristband during physiotherapy exercises to measure the scheduled motions of their limbs. In the model, the sampling data collected from the scheduled motions are labeled by an arbitrary number within a defined range. The sample datasets are referred as the design of an initial fuzzy inference system (FIS) with data preprocessing, feature visualizing, fuzzification, and fuzzy logic rules. The ANFIS then processes data training to adjust the FIS for optimization. The trained FIS then can infer the motion labels via defuzzification to recognize the features in the test data. The average recognition rate was higher than 90% for the testing motions if the subject followed the sampling schedule. With model implementation, the middle section of motion datasets in each second is recommended for recognition in the UHM system which also includes a mobile App to retrieve the personalized FIS in order to trace the exercise. This approach contributes a PRR model with trackable diagrams for the physicians to explore the rehabilitation motions in details.

FPGA Implementation of a Functional Neuro-Fuzzy Network for Nonlinear System Control

This study used Xilinx Field Programmable Gate Arrays (FPGAs) to implement a functional neuro-fuzzy network (FNFN) for solving nonlinear control problems. A functional link neural network (FLNN) was used as the consequent part of the proposed FNFN model. This study adopted the linear independent functions and the orthogonal polynomials in a functional expansion of the FLNN. Thus, the design of the FNFN model could improve the control accuracy. The learning algorithm of the FNFN model was divided into structure learning and parameter learning. The entropy measurement was adopted in the structure learning to determine the generated new fuzzy rule, whereas the gradient descent method in the parameter learning was used to adjust the parameters of the membership functions and the weights of the FLNN. In order to obtain high speed operation and real-time application, a very high speed integrated circuit hardware description language (VHDL) was used to design the FNFN controller and was implemented on FPGA. Finally, the experimental results demonstrated that the proposed hardware implementation of the FNFN model confirmed the viability in the temperature control of a water bath and the backing control of a car.