Deep Learning Approaches For Brain State Detection Under Anesthesia: A Cnn-lstm Framework

• Author(s):

  Ankitha D D | Harshitha M G | Manjula C | Meghana V Mathad | Rummana Firdaus

• Keywords:

  Brain-Computer Interface (BCI), Convolutional Neural Network(CNN), Electroencephalography(EEG),Depth Of Anesthesia (DoA), Long Short-Term Memory (LSTM)

• Abstract:

  This Research Presents An Automated Approach To Analyzing Brain States During Anesthesia Using Convolutional Neural Networks (CNNs) And Long Short-Term Memory (LSTM) Networks. By Leveraging The Spatial Feature Extraction Power Of CNNs And The Temporal Sequence Processing Capabilities Of LSTMs, The Model Effectively Classifies Brain States From EEG Signals. The System Identifies Key States Such As Consciousness, Light Anesthesia, Deep Anesthesia, And Emergence. Extensive Experiments On EEG Datasets Show That The Proposed CNN-LSTM Hybrid Architecture Outperforms Traditional Machine Learning Methods In Accuracy. This Method Offers Real-time, Objective, And Precise Monitoring Of Brain States, Aiding Anesthesiologists In Clinical Decision-making. The Research Paves The Way For Safer Anesthesia Practices By Integrating Advanced Deep Learning Technologies For Reliable Brain State Classification.

---

Other Details

• Paper id:

  IJSARTV11I5103457

• Published in:

  Volume: 11 Issue: 5 May 2025

• Publication Date:

  2025-05-04

Download Article