Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment

Schmierer, Thomas; Li, Tianning; Li, Yan

doi:10.1016/j.artmed.2024.102869

Artificial Intelligence in Medicine

2024

DOI: 10.1016/j.artmed.2024.102869

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Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment

Thomas Schmierer,

Tianning Li,

Yan Li

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2024

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Cited by 3 publications

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Measure of the prediction capability of EEG features for depth of anesthesia in pigs

Caillet,

Maître,

Mirra

et al. 2024

Front. Med. Eng.

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Introduction: In the medical and veterinary fields, understanding the significance of physiological signals for assessing patient state, diagnosis, and treatment outcomes is paramount. There are, in the domain of machine learning (ML), very many methods capable of performing automatic feature selection. We here explore how such methods can be applied to select features from electroencephalogram (EEG) signals to allow the prediction of depth of anesthesia (DoA) in pigs receiving propofol.Methods: We evaluated numerous ML methods and observed that these algorithms can be classified into groups based on similarities in selected feature sets explainable by the mathematical bases behind those approaches. We limit our discussion to the group of methods that have at their core the computation of variances, such as Pearson’s and Spearman’s correlations, principal component analysis (PCA), and ReliefF algorithms.Results: Our analysis has shown that from an extensive list of time and frequency domain EEG features, the best predictors of DoA were spectral power (SP), and its density ratio applied specifically to high-frequency intervals (beta and gamma ranges), as well as burst suppression ratio, spectral edge frequency and entropy applied to the whole spectrum of frequencies.Discussion: We have also observed that data resolution plays an essential role not only in feature importance but may impact prediction stability. Therefore, when selecting the SP features, one might prioritize SP features over spectral bands larger than 1 Hz, especially for frequencies above 14 Hz.

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Measure of the prediction capability of EEG features for depth of anesthesia in pigs

Caillet,

Maître,

Mirra

et al. 2024

Front. Med. Eng.

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Artificial Intelligence in Medicine: from Diagnosis to Treatment

Bashkirova,

Kit,

Havryshchuk

et al. 2024

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In recent years, medicine has faced the serious challenge of the covid pandemic, due to which representatives of the health care sector had to mobilize forces and resources to jointly overcome these problems. The rapid development of artificial intelligence, its learning capabilities, and in recent years the creation of a neural network opens up wide possibilities for the use of AI in medicine. Aims: To analyze the modern literature on the use of AI for diagnosis and treatment and to analyze what problems may arise with the uncontrolled introduction of artificial intelligence Methodology: When conducting a literature review, an analysis and generalization of data on the research topic from 2019 to 2024 was carried out. The literature search was carried out by keywords using the PubMed search engine. Results: The literature review demonstrated the use of artificial intelligence in medicine, which has grown significantly in recent years and continues its rapid development, which is associated with the improvement of innovative technologies. The use of artificial intelligence in diagnostics is associated with the use of a neural network, which makes it possible to identify digitized images for rapid diagnosis. The use of artificial intelligence in surgery is reflected in the application of da Vinci. Artificial intelligence has been widely used in anesthesiology. Scientific Novelty: The literature search established that the implementation of artificial intelligence in medicine creates certain challenges related to the protection of personal data, and the possibility of error is not excluded when using AI. Conclusion: The use of AI is promising for diagnosis and treatment and helps doctors quickly make a diagnosis and prescribe treatment, but certain challenges created by artificial intelligence must be solved by implementing more reliable personal data protection systems, as well as control over the information reproduced by artificial intelligence.

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Future Trends in Genetic Research and Their Implications for Public Health: A Literature Review

Orlova,

Abramchuk,

Babik

et al. 2024

Futur. Med.

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The development of medical genetics, which took place in the last decade, allows specialists to apply an individual approach to treatment considering the genetic characteristics of the patient Aims: to conduct an analysis of the literature on the development of medical genetics and the use of its directions in diagnosis and treatment, as well as to clarify the ethical aspects of the introduction of genetics tools in medicine Methodology: During the literature review, data analysis and summarization was carried out on the topic of the study using the PubMed medical search engine. The search for literary sources was limited to keywords on the research topic and covered the period 2019-2024. Results: As a result of studying the literature, it was established that today the use of medical genetics methods is a promising direction that opens opportunities for the introduction of personalized medicine. Areas such as sequencing and genomics make it possible to study the structural and functional features of the patient's gene and help predict the development of genetic diseases, while pharmacogenomics and bioinformatics make it possible to model the effect of drugs. Scientific Novelty: During the literature search, it was found that the implementation of medical genetics in treatment and diagnostics creates several issues related to the protection of personal data, as well as imperfect sequencing methods can cause diagnostic errors. It is important to ensure access to medical genetics methods for all population groups. Conclusion: today society is on the threshold of a discovery in medicine, which relates to the introduction of medical genetics tools in diagnosis and treatment. The methods make it possible to treat a specific patient considering genetic characteristics, which improves diagnosis, reduces the side effects of drugs and, in general, improves the results of treatment.

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Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment

Cited by 3 publications

References 93 publications

Measure of the prediction capability of EEG features for depth of anesthesia in pigs

Measure of the prediction capability of EEG features for depth of anesthesia in pigs

Artificial Intelligence in Medicine: from Diagnosis to Treatment

Future Trends in Genetic Research and Their Implications for Public Health: A Literature Review

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