Pain Hybrid Model For Pain Recosnitionusins CNN+BL-LSTM+BL-SRU

D Nithish Kumar; D.Nikhil

Pain Hybrid Model For Pain Recosnitionusins CNN+BL-LSTM+BL-SRU

Authors: D Nithish Kumar, D.Nikhil

Unique Paper ID: 175799
Volume: 11
Issue: 11
PageNo: 4685-4689

Keywords: No Keywords Found

Abstract:
Pain is one of the most frequent symptoms experienced by patients in a clinical environment. Automatic pain recognition systems are developed to support medical personnel in assessing and interpreting a patient's pain level. In this paper, we propose a system for pain recognition using physiological signals such as ECG (Electrocardiogram), EMG (Electromyogram), and SCL (Skin Conductance Level). These signals are recorded from subjects who are subjected to pain-inducing stimuli. The signals are preprocessed and analyzed to extract relevant features. Machine learning algorithms are then applied to classify the pain levels. The proposed system achieves promising results, suggesting that physiological signals can be effectively used for automatic pain detection. This can lead to better pain management and improved patient care. This project addresses the need for automatic pain recognition in healthcare without relying on expert feature extraction from physiological signals. Instead, it introduces a deep learning approach that combines feature extraction and classification. The method incorporates multi-level context information for accurate pain discrimination. Experimental results, based on datasets like BioVid Heat Pain and Emopain 2021, demonstrate its superiority over conventional methods.

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ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 4685-4689

Pain Hybrid Model For Pain Recosnitionusins CNN+BL-LSTM+BL-SRU

Available:https://ijirt.org/Article?manuscript=175799

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