A Modified LSTM Framework for Analyzing COVID-19 Effect on Emotion and Mental Health during Pandemic Using the EEG Signals | Zendy

Aditi Sakalle | Zendy; Pradeep Tomar | Zendy; Harshit Bhardwaj | Zendy; Md. Abdul Alim | Zendy

Open Access

A Modified LSTM Framework for Analyzing COVID-19 Effect on Emotion and Mental Health during Pandemic Using the EEG Signals

Author(s) -

Aditi Sakalle,

Pradeep Tomar,

Harshit Bhardwaj,

Md. Abdul Alim

Publication year - 2022

Publication title -

journal of healthcare engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.509

H-Index - 29

eISSN - 2040-2309

pISSN - 2040-2295

DOI - 10.1155/2022/8412430

Subject(s) - covid-19 , pandemic , electroencephalography , mental health , psychology , emotion recognition , cognitive psychology , computer science , artificial intelligence , applied psychology , psychiatry , medicine , virology , disease , pathology , infectious disease (medical specialty) , outbreak

COVID-19, a WHO-declared public health emergency of worldwide concern, is quickly spreading over the world, posing a physical and mental health hazard. The COVID-19 has resulted in one of the world’s most significant worldwide lockdowns, affecting human mental health. In this research work, a modified Long Short-Term Memory (MLSTM)-based Deep Learning model framework is proposed for analyzing COVID-19 effect on emotion and mental health during the pandemic using electroencephalogram (EEG) signals. The participants of this study were volunteers that recovered from COVID-19. The EEG dataset of 40 people is collected to predict emotion and mental health. The results of the MLSTM model are also compared with the other literature classifiers. With an accuracy of 91.26%, the MLSTM beats existing classifiers when using the 70–30 partitioning technique.

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