Signal processing and machine learning algorithm to classify anaesthesia depth
Background Poor assessment of anaesthetic depth (AD) has led to overdosing or underdosing of the anaesthetic agent, which requires continuous monitoring to avoid complications. The evaluation of the central nervous system activity and autonomic nervous system could provide additional information on...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2023-06-01
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| Series: | BMJ Health & Care Informatics |
| Online Access: | https://informatics.bmj.com/content/30/1/e100823.full |
| _version_ | 1827571945710813184 |
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| author | Oscar Mosquera Dussan Eduardo Tuta-Quintero Daniel A. Botero-Rosas |
| author_facet | Oscar Mosquera Dussan Eduardo Tuta-Quintero Daniel A. Botero-Rosas |
| author_sort | Oscar Mosquera Dussan |
| collection | DOAJ |
| description | Background Poor assessment of anaesthetic depth (AD) has led to overdosing or underdosing of the anaesthetic agent, which requires continuous monitoring to avoid complications. The evaluation of the central nervous system activity and autonomic nervous system could provide additional information on the monitoring of AD during surgical procedures.Methods Observational analytical single-centre study, information on biological signals was collected during a surgical procedure under general anaesthesia for signal preprocessing, processing and postprocessing to feed a pattern classifier and determine AD status of patients. The development of the electroencephalography index was carried out through data processing and algorithm development using MATLAB V.8.1.Results A total of 25 men and 35 women were included, with a total time of procedure average of 109.62 min. The results show a high Pearson correlation between the Complexity Brainwave Index and the indices of the entropy module. A greater dispersion is observed in the state entropy and response entropy indices, a partial overlap can also be seen in the boxes associated with deep anaesthesia and general anaesthesia in these indices. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states.Conclusion Biological signal filtering and a machine learning algorithm may be used to classify AD during a surgical procedure. Further studies will be needed to confirm these results and improve the decision-making of anaesthesiologists in general anaesthesia. |
| first_indexed | 2024-03-08T16:37:23Z |
| format | Article |
| id | doaj.art-994989c7df2d4adda0de7624593e6cba |
| institution | Directory Open Access Journal |
| issn | 2632-1009 |
| language | English |
| last_indexed | 2025-03-25T13:13:45Z |
| publishDate | 2023-06-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Health & Care Informatics |
| spelling | doaj.art-994989c7df2d4adda0de7624593e6cba2024-08-09T01:25:10ZengBMJ Publishing GroupBMJ Health & Care Informatics2632-10092023-06-0130110.1136/bmjhci-2023-100823Signal processing and machine learning algorithm to classify anaesthesia depthOscar Mosquera Dussan0Eduardo Tuta-Quintero1Daniel A. Botero-Rosas2School of Medicine, Universidad de La Sabana, Chia, ColombiaSchool of Medicine, Universidad de La Sabana, Chia, ColombiaSchool of Medicine, Universidad de La Sabana, Chia, ColombiaBackground Poor assessment of anaesthetic depth (AD) has led to overdosing or underdosing of the anaesthetic agent, which requires continuous monitoring to avoid complications. The evaluation of the central nervous system activity and autonomic nervous system could provide additional information on the monitoring of AD during surgical procedures.Methods Observational analytical single-centre study, information on biological signals was collected during a surgical procedure under general anaesthesia for signal preprocessing, processing and postprocessing to feed a pattern classifier and determine AD status of patients. The development of the electroencephalography index was carried out through data processing and algorithm development using MATLAB V.8.1.Results A total of 25 men and 35 women were included, with a total time of procedure average of 109.62 min. The results show a high Pearson correlation between the Complexity Brainwave Index and the indices of the entropy module. A greater dispersion is observed in the state entropy and response entropy indices, a partial overlap can also be seen in the boxes associated with deep anaesthesia and general anaesthesia in these indices. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states.Conclusion Biological signal filtering and a machine learning algorithm may be used to classify AD during a surgical procedure. Further studies will be needed to confirm these results and improve the decision-making of anaesthesiologists in general anaesthesia.https://informatics.bmj.com/content/30/1/e100823.full |
| spellingShingle | Oscar Mosquera Dussan Eduardo Tuta-Quintero Daniel A. Botero-Rosas Signal processing and machine learning algorithm to classify anaesthesia depth BMJ Health & Care Informatics |
| title | Signal processing and machine learning algorithm to classify anaesthesia depth |
| title_full | Signal processing and machine learning algorithm to classify anaesthesia depth |
| title_fullStr | Signal processing and machine learning algorithm to classify anaesthesia depth |
| title_full_unstemmed | Signal processing and machine learning algorithm to classify anaesthesia depth |
| title_short | Signal processing and machine learning algorithm to classify anaesthesia depth |
| title_sort | signal processing and machine learning algorithm to classify anaesthesia depth |
| url | https://informatics.bmj.com/content/30/1/e100823.full |
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