期刊
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
卷 88, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2023.105036
关键词
Breath analysis; Improved EWF; Improved STFT; LSTM; WBU-HGSO scheme
Analysis of exhaled breath is an increasingly used diagnostic technique in medicine. This study introduces a new NICBGM-based model that utilizes various features and weight optimization for accurate data interpretation and result optimization.
Analysis of exhaled breath is becoming more and more used as an additional diagnostic technique in medicine. Researchers must create unique algorithms for accurate data interpretation due to the sheer quantity of factors that must be considered. Therefore, a new NICBGM-based model from exhaled breath is introduced in this study. This work exploited median filtering (MF) for pre-processing. Then, Improved Empirical Wavelet Functions (IEWF), R-peak detection, QT intervals, PR intervals, Entropy-based feature, improved Discrete wavelet transform (DWT), Continuous Wavelet Transform (CWT), and short-time Fourier transformation (I-STFT) are extracted. Further, optimal features are chosen, which are then put through a hybrid scheme that combines Deep Max out (DMO) and Long Short-Term Memory (LSTM). Then, the mean is taken by DMO and LSTM to attain the fine result. Here, the Wild Beest Updated HGSO (WBU-HGSO) model is used to optimize the LSTM weights. The final step is an analysis that proves the superiority of the WBU-HGSO-based model.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据