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Feng Fu

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4

JBHI Journal 2024 Journal Article

Classification of Three Anesthesia Stages Based on Near-Infrared Spectroscopy Signals

  • Zhian Liu
  • Lichengxi Si
  • Shaoxian Shi
  • Jing Li
  • Jing Zhu
  • Won Hee Lee
  • Sio-Long Lo
  • Xiangguo Yan

Proper monitoring of anesthesia stages can guarantee the safe performance of clinical surgeries. In this study, different anesthesia stages were classified using near-infrared spectroscopy (NIRS) signals with machine learning. The cerebral hemodynamic variables of right proximal oxyhemoglobin (HbO 2 ) in maintenance (MNT), emergence (EM) and the consciousness (CON) stage were collected and then the differences between the three stages were compared by phase-amplitude coupling (PAC). Then combined with time-domain including linear (mean, standard deviation, max, min and range), nonlinear (sample entropy) and power in frequency-domain signal features, feature selection was performed and finally classification was performed by support vector machine (SVM) classifier. The results show that the PAC of the NIRS signal was gradually enhanced with the deepening of anesthesia level. A good three-classification accuracy of 69. 27% was obtained, which exceeded the result of classification of any single category feature. These results indicate the feasibility of NIRS signals in performing three or even more anesthesia stage classifications, providing insight into the development of new anesthesia monitoring modalities.

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JBHI Journal 2023 Journal Article

Effective Electrical Impedance Tomography Based on Enhanced Encoder–Decoder Using Atrous Spatial Pyramid Pooling Module

  • Xiang Tian
  • Xuechao Liu
  • Tao Zhang
  • Jian'an Ye
  • Weirui Zhang
  • Liangliang Zhang
  • Xuetao Shi
  • Feng Fu

Electrical impedance tomography (EIT) is a noninvasive and radiation-free imaging method. As a “soft-field” imaging technique, in EIT, the target signal in the center of the measured field is frequently swamped by the target signal at the edge, which restricts its further application. To alleviate this problem, this study presents an enhanced encoder–decoder (EED) method with an atrous spatial pyramid pooling (ASPP) module. The proposed method enhances the ability to detect central weak targets by constructing an ASPP module that integrates multiscale information in the encoder. The multilevel semantic features are fused in the decoder to improve the boundary reconstruction accuracy of the center target. The average absolute error of the imaging results by the EED method reduced by 82. 0%, 83. 6%, and 36. 5% in simulation experiments and 83. 0%, 83. 2%, and 36. 1% in physical experiments compared with the errors of the damped least-squares algorithm, Kalman filtering method, and U-Net-based imaging method, respectively. The average structural similarity improved by 37. 3%, 42. 9%, and 3. 6%, and 39. 2%, 45. 2%, and 3. 8% in the simulation and physical experiments, respectively. The proposed method provides a practical and reliable means of extending the application of EIT by solving the problem of weak central target reconstruction under the effect of strong edge targets in EIT.

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