Document Type : Original Research

Authors

1 MSc, Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

2 PhD, Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

3 PhD, Occupational Sleep Research Center, Tehran University of Medical Sciences, Tehran, Iran

Abstract

Background: Sleep apnea is one of the most common sleep disorders that facilitating and accelerating its diagnosis will have positive results on its future trend.
Objective: This study aimed to diagnosis the sleep apnea types using the optimized neural network.
Material and Methods: This descriptive-analytical study was done on 50 cases of patients referred to the sleep clinic of Imam Khomeini Hospital in Tehran, including 11 normal, 13 mild, 17 moderate and 9 severe cases. At the first, the data were pre-processed in three stages, then The Electrocardiogram (ECG) signal was decomposed to 8 levels using wavelet transform convert and 6 nonlinear features for the coefficients of this level and 10 features were calculated for RR Intervals. For apnea categorizing classes, the multilayer perceptron neural network was used with the backpropagation algorithm. For optimizing Multi-layered Perceptron (MLP) weights, the Particle Swarm Optimization (PSO) evolutionary optimization algorithm was used.
Results: The simulation results show that the accuracy criterion in the MLP network is allied with the Backpropagation (BP) training algorithm for different types of apnea. By optimizing the weights in the MLP network structure, the accuracy criterion for modes normal, obstructive, central, mixed was obtained %96.86, %97.48, %96.23, and % 96.44, respectively. These values indicate the strength of the evolutionary algorithm in improving the evaluation criteria and network accuracy.
Conclusion: Due to the growth of knowledge and the complexity of medical decisions in the diagnosis of the disease, the use of artificial neural network algorithms can be useful to support this decision.

Keywords