Anesthesia Depth Monitoring Based on Anesthesia Monitor with the Help of Artificial Intelligence
OBJECTIVE: To use the low-cost anesthesia monitor for realizing anesthesia depth monitoring, effectively assist anesthesiologists in diagnosis and reduce the cost of anesthesia operation.
METHODS: Propose a monitoring method of anesthesia depth based on artificial intelligence. The monitoring method is designed based on convolutional neural network (CNN) and long and short-term memory (LSTM) network. The input data of the model include electrocardiogram (ECG) and pulse wave photoplethysmography (PPG) recorded in the anesthesia monitor, as well as heart rate variability (HRV) calculated from ECG, The output of the model is in three states of anesthesia induction, anesthesia maintenance and anesthesia awakening.
RESULTS: The accuracy of anesthesia depth monitoring model under transfer learning is 94.1%, which is better than all comparison methods.
CONCLUSIONS: The accuracy of this study meets the needs of perioperative anesthesia depth monitoring and the study reduces the operation cost.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:47 |
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| Enthalten in: |
Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation - 47(2023), 1 vom: 30. Jan., Seite 43-46 |
| Sprache: |
Chinesisch |
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| Beteiligte Personen: |
Guo, Yi [VerfasserIn] |
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| Links: |
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| Themen: |
Anesthesia depth |
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| Anmerkungen: |
Date Completed 09.02.2023 Date Revised 09.02.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.3969/j.issn.1671-7104.2023.01.007 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 500 | |a Date Completed 09.02.2023 | ||
| 500 | |a Date Revised 09.02.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a OBJECTIVE: To use the low-cost anesthesia monitor for realizing anesthesia depth monitoring, effectively assist anesthesiologists in diagnosis and reduce the cost of anesthesia operation | ||
| 520 | |a METHODS: Propose a monitoring method of anesthesia depth based on artificial intelligence. The monitoring method is designed based on convolutional neural network (CNN) and long and short-term memory (LSTM) network. The input data of the model include electrocardiogram (ECG) and pulse wave photoplethysmography (PPG) recorded in the anesthesia monitor, as well as heart rate variability (HRV) calculated from ECG, The output of the model is in three states of anesthesia induction, anesthesia maintenance and anesthesia awakening | ||
| 520 | |a RESULTS: The accuracy of anesthesia depth monitoring model under transfer learning is 94.1%, which is better than all comparison methods | ||
| 520 | |a CONCLUSIONS: The accuracy of this study meets the needs of perioperative anesthesia depth monitoring and the study reduces the operation cost | ||
| 650 | 4 | |a English Abstract | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a anesthesia depth | |
| 650 | 4 | |a anesthesia monitor | |
| 650 | 4 | |a artificial intelligence | |
| 700 | 1 | |a Du, Qiuchen |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Mengmeng |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Guanhua |e verfasserin |4 aut | |
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