Clinical investigation of sensing ailments in brain movement through physiological activity using ML technique

T. Ragunthar; K. Kishore Anthuvan Sahayaraj; Angel Latha Mary; G. K. Jakir Hussain; T. R. Kalai Lakshmi; R. Thiagarajan

doi:10.53730/ijhs.v6nS6.11761

Authors

Ragunthar T. Assistant Professor, Department of Computing Technologies, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathuru
K. Kishore Anthuvan Sahayaraj Assistant Professor, Department of Computing Technologies, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathuru
Angel Latha Mary Professor and Head Department of Computer Science and Business Systems, Sri Eshwar College of Engineering Coimbatore
G. K. Jakir Hussain Assistant Professor (Selection Grade), Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore
T. R. Kalai Lakshmi Associate Professor, School of Management Studies, Sathyabama Institute of Science and Technology, Chennai
R. Thiagarajan Associate Professor/IT, Prathyusha Engineering College, Chennai

Keywords:

machine learning, clinical research, neuroimaging, pattern recognition, brain wave abnormality detection, physiological activity

Abstract

The main goal was to demonstrate that EEG and its derivatives may be utilised to recreate brain function using EEG data. This is vital to determine the application's source activities in order to assess various strategies that address the reverse issue, hence accessibility to a standardized EEG dataset is essential. Physiological and psychological tests could be used to determine alertness or activity levels in particular. Furthermore, changes of psychological measurements can be influenced by a variety of cognitive notions. Heartbeat, skin temperature, and brainwaves activities, in example, were susceptible to several psychological categories such as sleepiness, tension, and so on. EEG, on the other hand, delivers a robust resolving power and continuous recording the cerebral activity. An EEG records either periodic and irregular brainwaves. ML approaches are used to classify the physical movement of the heart brain per its condition. The main purpose is using categorization to improve the effectiveness of testing condition segmentation. Multimodal modeling, which is built upon localised machine learning, is a rather appealing option to bipolar neuroimaging, particularly in terms of increased sensibility to alterations in experiment settings.

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