Comparison of initial learning algorithms for long short-term memory method on real-time respiratory signal prediction - Sun, Wenzheng; Dang, Jun; Zhang, Lei; Wei, Qichun (Frontiers in Oncology, 2023)
Aim: To examine the effect of the weight initializers on the respiratory signal prediction performance using the long short-term memory (LSTM) model. Methods: Respiratory signals collected with the CyberKnife Synchrony device during 304 breathing motion traces were used in this study. The effectiveness of four weight initializers (Glorot, He, Orthogonal and Narrow-normal) on the prediction performance of the LSTM model was investigated. The prediction performance was evaluated by the normalized root-mean-square-error (NRMSE) between the ground truth and predicted respiratory signal. Results: Among the four initializers, the He initializer showed the best performance. The mean NRMSE with 385-ms ahead time using the He initializer was superior by 7.5%, 8.3% and 11.3% as compared to that using the Glorot, Orthogonal and Narrow-normal initializer, respectively. The confidence interval of NRMSE using Glorot, He, Orthogonal and Narrow-normal initializer were [0.099, 0.175], [0.097, 0.147], [0.101, 0.176] and [0.107, 0.178], respectively. Conclusions: The experiment results in this study indicated that the He could be a valuable initializer in the LSTM model for the respiratory signal prediction.