A Review on Machine Learning Models for Breathing Pattern Analysis of Soldiers DOI

P. Kaleeswari,

R. Ramalakshmi,

Arunprasath Thiyagarajan

et al.

Published: Dec. 14, 2023

Since 2001, the U.S. military has sent 2.7 million people to support missions in Afghanistan and Asia. The experience of land-based employees is increased by exposure additional inhalational exposures particulate matter from a variety sources. For purpose preventing significant loss nation individual soldier, post-traumatic stress disorder (PTSD) must be identified. Breathing pattern analysis key method for detecting PTSD, various studies have used machine learning techniques this purpose. This survey examines multiple ML models determine soldiers' breathing patterns distinct works. overview discusses several strategies over past few decades conducting extensive research. Military personnel' are analyzed using datasets, statistical factors, methodologies. effectiveness algorithms compared qualitative as well quantitative approaches. potential future study areas with major challenges discussed reach conclusion.

Language: Английский

Multi-perspective characterization of seizure prediction based on microstate analysis DOI Creative Commons
Wei Shi, Yina Cao, Fangni Chen

et al.

Frontiers in Neuroscience, Journal Year: 2024, Volume and Issue: 18

Published: Nov. 19, 2024

Epilepsy is an irregular and recurrent cerebral dysfunction that significantly impacts the affected individual's social functionality quality of life. This study aims to integrate cognitive dynamic attributes brain into seizure prediction, evaluating effectiveness various characterization perspectives for while delving impact varying fragment lengths on performance each characterization. We adopted microstate analysis extract properties states, calculated EEG-based microstate-based features characterize nonlinear attributes, assessed power values across different frequency bands represent spectral information EEG. Based aforementioned characteristics, predictor achieved a sensitivity 93.82% private FH-ZJU dataset 93.22% Siena Scalp EEG dataset. The outperforms state-of-the-art works in terms metrics indicating it crucial incorporate prediction.

Language: Английский

Citations

0
Altered EEG patterns in individuals with disorganized attachment: an EEG microstates study DOI Creative Commons
Giuseppe Alessio Carbone, Christoph M. Michel, Benedetto Farina

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: June 6, 2023

Abstract Background: Over the past years, different studies provided preliminary evidence that Disorganized Attachment (DA) may have dysregulatory and disintegrative effects on both autonomic arousal regulation brain connectivity. However, despite clinical relevance of this construct, few investigated specific alterations underlying DA using electroencephalography (EEG). Thus, main aim current study was to extend scientific literature EEG microstates correlates in a non-clinical sample (N= 50) before after administration Adult Interview (AAI). Methods: Two Resting State (RS) recordings were performed AAI. Microstates indices then calculated Cartool software. Results: Disorganized/Unrevolved (D/U) group showed lower mean duration map E higher occurrence F than organized individuals. Then, an effect time also emerged for indices. Finally, positive significant correlation between post-AAI coherence mind found as well negative with segmentation density post-AAI. Conclusion: our results differences dynamic patterns groups, reflecting disintegration mechanisms retrieval attachment memories.

Language: Английский

Citations

1
Valence-specific EEG microstate modulations during self-generated affective states DOI Open Access
Anakarina Nazare, Miralena I. Tomescu

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 23, 2023

Abstract We spend a significant part of our lives navigating emotionally charged mind-wandering states by spontaneously imagining the past or future, which predicts general well-being. investigated brain self-generated affective using EEG microstate analysis to identify temporal dynamics underlying networks that sustain endogenous state activity. With this aim, we compared five distinct microstates between baseline resting-state, positive (e.g., awe, contentment), and negative anger, fear) states. found affect-related modulations B, C, D dynamics. Microstates B were increased, while C was decreased during valence In addition, valence-specific mechanisms spontaneous regulation. Negative specifically modulate increased presence occurrence E The are characterized more prevalent les present A both These findings provide valuable insights into neurodynamic patterns regulation implications for developing biomarkers therapeutic interventions in mood anxiety disorders.

Language: Английский

Citations

1
A Review on Machine Learning Models for Breathing Pattern Analysis of Soldiers DOI

P. Kaleeswari,

R. Ramalakshmi,

Arunprasath Thiyagarajan

et al.

Published: Dec. 14, 2023

Since 2001, the U.S. military has sent 2.7 million people to support missions in Afghanistan and Asia. The experience of land-based employees is increased by exposure additional inhalational exposures particulate matter from a variety sources. For purpose preventing significant loss nation individual soldier, post-traumatic stress disorder (PTSD) must be identified. Breathing pattern analysis key method for detecting PTSD, various studies have used machine learning techniques this purpose. This survey examines multiple ML models determine soldiers' breathing patterns distinct works. overview discusses several strategies over past few decades conducting extensive research. Military personnel' are analyzed using datasets, statistical factors, methodologies. effectiveness algorithms compared qualitative as well quantitative approaches. potential future study areas with major challenges discussed reach conclusion.

Language: Английский

Citations

0