Application of Machine Intelligence in IoT-Enabled Healthcare Monitoring Systems: A Case Study-Based Approach

Chakraborty, Amartya; Adhikary, Subhrangshu; Ghosh, Arindam; Paul, Partha Sarathi

doi:10.1201/9781003239895-4

Cited by 2 publications

(1 citation statement)

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“…However classical machine learning algorithms like K-Nearest Neighbour, and Support Vector Machine performs doesn't perform optimally for large datasets because of their higher order time complexities. Therefore a specialized form of machine learning called a Neural Network can be implemented for processing data at a very large scale with almost linear time complexity [6]. A neural network is inspired by the decision-making process of the human brain where data from one neuron is fed to other neurons by altering the electrical potential based on the properties of that neuron.…”

Section: Introductionmentioning

confidence: 99%

Optimized EEG based mood detection with signal processing and deep neural networks for brain-computer interface

Adhikary¹,

Jain

Saha³

et al. 2023

Biomed. Phys. Eng. Express

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Electroencephalogram (EEG) is a very promising and widely implemented procedure to study brain signals and activities by amplifying and measuring the post-synaptical potential arising from electrical impulses produced by neurons and detected by specialized electrodes attached to specific points in the scalp. It can be studied for detecting brain abnormalities, headaches, and other conditions. However, there are limited studies performed to establish a smart decision-making model to identify EEG's relation with the mood of the subject. In this experiment, EEG signals of 28 healthy human subjects have been observed with consent and attempts have been made to study and recognise moods. Savitzky-Golay band-pass filtering and Independent Component Analysis have been used for data filtration. Different neural network algorithms have been implemented to analyze and classify the EEG data based on the mood of the subject. The model is further optimised by the usage of Blackman window-based Fourier Transformation and extracting the most significant frequencies for each electrode. Using these techniques, up to 96.01% detection accuracy has been obtained.

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Section: Introductionmentioning

confidence: 99%