Sleep apnea syndrome is one of the most serious sleep disorders. Sleep apnea detection systems have been proposed which estimate whether the measured signal contains an apnea event. Hence, we proposed apnea state detection at each discrete time by utilizing frequency distribution. The objective of this study was to improve the detection accuracy through the consideration of the temporal association of the frequency distribution. A pneumatic bio-instrumental system was adopted as the sensing system. The output signal contained biomedical information such as respiration, heartbeat, and body movement. Wavelet transform was applied to the output signal to obtain the temporal alteration of the frequency distribution. Wavelet coefficients in the range of 0.15Hz to 0.45Hz were used as the respiratory distribution, which was used as an input in the recurrent neural network. A long short-term memory layer in the recurrent neural network utilized the last state as an update which enabled the network to take into account the temporal association. The output from the long short-term memory layer was interpreted using the probabilities that passed through a fully connected layer and a softmax layer. The apnea state at each discrete time was detected by comparing the probabilities. To validate the method, an experiment in which 8 subjects participated was conducted. The results indicate the detection performance of the proposed method is better than the previous method in all three indices. It is inferred that the temporal alteration of respiratory distribution is effective for apnea detection.
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Sleep Apnea Detection by a Recurrent Neural Network based on Long Short-Term Memory
Semantic Scholar · Medicine · 2020
Abstract
Sleep apnea syndrome is one of the most serious sleep disorders. Sleep apnea detection systems have been proposed which estimate whether the measured signal contains an apnea event. Hence, we proposed apnea state detection at each discrete time by utilizing frequency distribution. The objective of this study was to improve the detection accuracy through the consideration of the temporal association of the frequency distribution. A pneumatic bio-instrumental system was adopted as the sensing system. The output signal contained biomedical information such as respiration, heartbeat, and body movement. Wavelet transform was applied to the output signal to obtain the temporal alteration of the frequency distribution. Wavelet coefficients in the range of 0.15Hz to 0.45Hz were used as the respiratory distribution, which was used as an input in the recurrent neural network. A long short-term memory layer in the recurrent neural network utilized the last state as an update which enabled the network to take into account the temporal association. The output from the long short-term memory layer was interpreted using the probabilities that passed through a fully connected layer and a softmax layer. The apnea state at each discrete time was detected by comparing the probabilities. To validate the method, an experiment in which 8 subjects participated was conducted. The results indicate the detection performance of the proposed method is better than the previous method in all three indices. It is inferred that the temporal alteration of respiratory distribution is effective for apnea detection.