CN118000677A - Anesthesia management system and method based on novel or multi-mode electroencephalogram monitoring - Google Patents
Anesthesia management system and method based on novel or multi-mode electroencephalogram monitoring Download PDFInfo
- Publication number
- CN118000677A CN118000677A CN202410284046.4A CN202410284046A CN118000677A CN 118000677 A CN118000677 A CN 118000677A CN 202410284046 A CN202410284046 A CN 202410284046A CN 118000677 A CN118000677 A CN 118000677A
- Authority
- CN
- China
- Prior art keywords
- anesthesia
- brain
- analysis
- index
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 206010002091 Anaesthesia Diseases 0.000 title claims abstract description 212
- 230000037005 anaesthesia Effects 0.000 title claims abstract description 209
- 238000012544 monitoring process Methods 0.000 title claims abstract description 77
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000004458 analytical method Methods 0.000 claims abstract description 92
- 230000003925 brain function Effects 0.000 claims abstract description 46
- 238000007405 data analysis Methods 0.000 claims abstract description 25
- 210000004556 brain Anatomy 0.000 claims description 116
- 239000008280 blood Substances 0.000 claims description 64
- 238000007726 management method Methods 0.000 claims description 56
- 210000004369 blood Anatomy 0.000 claims description 53
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 46
- 229910052760 oxygen Inorganic materials 0.000 claims description 46
- 239000001301 oxygen Substances 0.000 claims description 46
- 230000004089 microcirculation Effects 0.000 claims description 44
- 230000006870 function Effects 0.000 claims description 38
- 230000003727 cerebral blood flow Effects 0.000 claims description 37
- 238000005259 measurement Methods 0.000 claims description 29
- 238000005516 engineering process Methods 0.000 claims description 26
- 230000008344 brain blood flow Effects 0.000 claims description 20
- 230000007246 mechanism Effects 0.000 claims description 17
- 238000001356 surgical procedure Methods 0.000 claims description 17
- 238000001994 activation Methods 0.000 claims description 16
- 230000004872 arterial blood pressure Effects 0.000 claims description 16
- 210000003710 cerebral cortex Anatomy 0.000 claims description 16
- JVTAAEKCZFNVCJ-UHFFFAOYSA-N lactic acid Chemical compound CC(O)C(O)=O JVTAAEKCZFNVCJ-UHFFFAOYSA-N 0.000 claims description 16
- 230000004044 response Effects 0.000 claims description 16
- 230000001360 synchronised effect Effects 0.000 claims description 14
- 230000005611 electricity Effects 0.000 claims description 13
- 102000001554 Hemoglobins Human genes 0.000 claims description 12
- 108010054147 Hemoglobins Proteins 0.000 claims description 12
- 230000008878 coupling Effects 0.000 claims description 11
- 238000010168 coupling process Methods 0.000 claims description 11
- 238000005859 coupling reaction Methods 0.000 claims description 11
- 238000010219 correlation analysis Methods 0.000 claims description 10
- 230000036760 body temperature Effects 0.000 claims description 9
- 230000003920 cognitive function Effects 0.000 claims description 9
- 230000035945 sensitivity Effects 0.000 claims description 9
- 238000000540 analysis of variance Methods 0.000 claims description 8
- 230000037396 body weight Effects 0.000 claims description 8
- 238000005534 hematocrit Methods 0.000 claims description 8
- 238000001802 infusion Methods 0.000 claims description 8
- 230000004410 intraocular pressure Effects 0.000 claims description 8
- 235000014655 lactic acid Nutrition 0.000 claims description 8
- 239000004310 lactic acid Substances 0.000 claims description 8
- 230000002980 postoperative effect Effects 0.000 claims description 8
- 210000003478 temporal lobe Anatomy 0.000 claims description 8
- 210000002700 urine Anatomy 0.000 claims description 8
- 238000000605 extraction Methods 0.000 claims description 7
- 210000001652 frontal lobe Anatomy 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 7
- 239000000835 fiber Substances 0.000 claims description 6
- 239000000523 sample Substances 0.000 claims description 6
- 238000000692 Student's t-test Methods 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 5
- 238000012706 support-vector machine Methods 0.000 claims description 5
- 210000002321 radial artery Anatomy 0.000 claims description 4
- 230000008336 microcirculatory blood flow Effects 0.000 claims description 3
- 239000004020 conductor Substances 0.000 claims description 2
- 238000012417 linear regression Methods 0.000 claims description 2
- 238000011156 evaluation Methods 0.000 abstract description 9
- 238000003745 diagnosis Methods 0.000 abstract description 4
- 230000017531 blood circulation Effects 0.000 description 34
- 238000002695 general anesthesia Methods 0.000 description 21
- 230000002490 cerebral effect Effects 0.000 description 19
- 210000001519 tissue Anatomy 0.000 description 19
- 238000005070 sampling Methods 0.000 description 13
- 210000003128 head Anatomy 0.000 description 12
- 230000000694 effects Effects 0.000 description 10
- 230000006698 induction Effects 0.000 description 10
- 230000033228 biological regulation Effects 0.000 description 9
- 239000003814 drug Substances 0.000 description 9
- 238000001990 intravenous administration Methods 0.000 description 9
- 238000001228 spectrum Methods 0.000 description 9
- 238000002604 ultrasonography Methods 0.000 description 9
- 230000005856 abnormality Effects 0.000 description 8
- 229940079593 drug Drugs 0.000 description 8
- 238000009792 diffusion process Methods 0.000 description 7
- 210000002569 neuron Anatomy 0.000 description 7
- 238000011160 research Methods 0.000 description 7
- 230000002159 abnormal effect Effects 0.000 description 6
- 230000004913 activation Effects 0.000 description 6
- 230000000004 hemodynamic effect Effects 0.000 description 6
- 238000007917 intracranial administration Methods 0.000 description 6
- 230000003702 neurovascular coupling effect Effects 0.000 description 6
- 238000001949 anaesthesia Methods 0.000 description 5
- 230000010455 autoregulation Effects 0.000 description 5
- 230000036772 blood pressure Effects 0.000 description 5
- 230000008859 change Effects 0.000 description 5
- 238000010801 machine learning Methods 0.000 description 5
- 239000013307 optical fiber Substances 0.000 description 5
- 208000006011 Stroke Diseases 0.000 description 4
- 238000013473 artificial intelligence Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 4
- 230000004087 circulation Effects 0.000 description 4
- 230000006378 damage Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
- 206010012601 diabetes mellitus Diseases 0.000 description 4
- 230000005484 gravity Effects 0.000 description 4
- NOESYZHRGYRDHS-UHFFFAOYSA-N insulin Chemical compound N1C(=O)C(NC(=O)C(CCC(N)=O)NC(=O)C(CCC(O)=O)NC(=O)C(C(C)C)NC(=O)C(NC(=O)CN)C(C)CC)CSSCC(C(NC(CO)C(=O)NC(CC(C)C)C(=O)NC(CC=2C=CC(O)=CC=2)C(=O)NC(CCC(N)=O)C(=O)NC(CC(C)C)C(=O)NC(CCC(O)=O)C(=O)NC(CC(N)=O)C(=O)NC(CC=2C=CC(O)=CC=2)C(=O)NC(CSSCC(NC(=O)C(C(C)C)NC(=O)C(CC(C)C)NC(=O)C(CC=2C=CC(O)=CC=2)NC(=O)C(CC(C)C)NC(=O)C(C)NC(=O)C(CCC(O)=O)NC(=O)C(C(C)C)NC(=O)C(CC(C)C)NC(=O)C(CC=2NC=NC=2)NC(=O)C(CO)NC(=O)CNC2=O)C(=O)NCC(=O)NC(CCC(O)=O)C(=O)NC(CCCNC(N)=N)C(=O)NCC(=O)NC(CC=3C=CC=CC=3)C(=O)NC(CC=3C=CC=CC=3)C(=O)NC(CC=3C=CC(O)=CC=3)C(=O)NC(C(C)O)C(=O)N3C(CCC3)C(=O)NC(CCCCN)C(=O)NC(C)C(O)=O)C(=O)NC(CC(N)=O)C(O)=O)=O)NC(=O)C(C(C)CC)NC(=O)C(CO)NC(=O)C(C(C)O)NC(=O)C1CSSCC2NC(=O)C(CC(C)C)NC(=O)C(NC(=O)C(CCC(N)=O)NC(=O)C(CC(N)=O)NC(=O)C(NC(=O)C(N)CC=1C=CC=CC=1)C(C)C)CC1=CN=CN1 NOESYZHRGYRDHS-UHFFFAOYSA-N 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 210000000056 organ Anatomy 0.000 description 4
- 238000011084 recovery Methods 0.000 description 4
- 230000001105 regulatory effect Effects 0.000 description 4
- 238000007619 statistical method Methods 0.000 description 4
- 238000004659 sterilization and disinfection Methods 0.000 description 4
- 230000024883 vasodilation Effects 0.000 description 4
- 210000003462 vein Anatomy 0.000 description 4
- 206010021143 Hypoxia Diseases 0.000 description 3
- 230000003444 anaesthetic effect Effects 0.000 description 3
- 238000005311 autocorrelation function Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 3
- 210000004204 blood vessel Anatomy 0.000 description 3
- 210000004027 cell Anatomy 0.000 description 3
- 238000005100 correlation spectroscopy Methods 0.000 description 3
- 239000003193 general anesthetic agent Substances 0.000 description 3
- 230000007954 hypoxia Effects 0.000 description 3
- 230000001965 increasing effect Effects 0.000 description 3
- 238000006213 oxygenation reaction Methods 0.000 description 3
- 230000010412 perfusion Effects 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 description 2
- 206010019196 Head injury Diseases 0.000 description 2
- 206010020772 Hypertension Diseases 0.000 description 2
- 102000004877 Insulin Human genes 0.000 description 2
- 108090001061 Insulin Proteins 0.000 description 2
- 208000019693 Lung disease Diseases 0.000 description 2
- 241001465754 Metazoa Species 0.000 description 2
- 238000004497 NIR spectroscopy Methods 0.000 description 2
- 206010039897 Sedation Diseases 0.000 description 2
- 208000030886 Traumatic Brain injury Diseases 0.000 description 2
- 230000003187 abdominal effect Effects 0.000 description 2
- 230000036592 analgesia Effects 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 2
- 230000008499 blood brain barrier function Effects 0.000 description 2
- 210000001218 blood-brain barrier Anatomy 0.000 description 2
- 208000029028 brain injury Diseases 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000002612 cardiopulmonary effect Effects 0.000 description 2
- 238000009407 construction method and process Methods 0.000 description 2
- 230000001276 controlling effect Effects 0.000 description 2
- 238000013075 data extraction Methods 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 210000003743 erythrocyte Anatomy 0.000 description 2
- 230000007717 exclusion Effects 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 239000008103 glucose Substances 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000002329 infrared spectrum Methods 0.000 description 2
- 229940125396 insulin Drugs 0.000 description 2
- 230000033001 locomotion Effects 0.000 description 2
- 230000004060 metabolic process Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 230000035479 physiological effects, processes and functions Effects 0.000 description 2
- 230000035790 physiological processes and functions Effects 0.000 description 2
- 238000004393 prognosis Methods 0.000 description 2
- 238000000611 regression analysis Methods 0.000 description 2
- 230000036280 sedation Effects 0.000 description 2
- 230000006641 stabilisation Effects 0.000 description 2
- 238000011105 stabilization Methods 0.000 description 2
- 230000035488 systolic blood pressure Effects 0.000 description 2
- 238000002560 therapeutic procedure Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 208000000044 Amnesia Diseases 0.000 description 1
- 201000006474 Brain Ischemia Diseases 0.000 description 1
- 206010007687 Carotid artery stenosis Diseases 0.000 description 1
- 206010008120 Cerebral ischaemia Diseases 0.000 description 1
- 206010008190 Cerebrovascular accident Diseases 0.000 description 1
- 208000034657 Convalescence Diseases 0.000 description 1
- 241000282414 Homo sapiens Species 0.000 description 1
- 206010021118 Hypotonia Diseases 0.000 description 1
- 208000032358 Intraoperative Awareness Diseases 0.000 description 1
- 208000032382 Ischaemic stroke Diseases 0.000 description 1
- 208000035965 Postoperative Complications Diseases 0.000 description 1
- 241000700159 Rattus Species 0.000 description 1
- 208000003443 Unconsciousness Diseases 0.000 description 1
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 229940124326 anaesthetic agent Drugs 0.000 description 1
- 229940035674 anesthetics Drugs 0.000 description 1
- 210000000133 brain stem Anatomy 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 208000006170 carotid stenosis Diseases 0.000 description 1
- 206010008118 cerebral infarction Diseases 0.000 description 1
- 230000003788 cerebral perfusion Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 210000002889 endothelial cell Anatomy 0.000 description 1
- 230000000763 evoking effect Effects 0.000 description 1
- 210000001061 forehead Anatomy 0.000 description 1
- 238000002599 functional magnetic resonance imaging Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000001727 in vivo Methods 0.000 description 1
- 230000005764 inhibitory process Effects 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 231100000863 loss of memory Toxicity 0.000 description 1
- 238000010197 meta-analysis Methods 0.000 description 1
- 230000002503 metabolic effect Effects 0.000 description 1
- 238000001690 micro-dialysis Methods 0.000 description 1
- 230000036640 muscle relaxation Effects 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 238000002610 neuroimaging Methods 0.000 description 1
- 230000005311 nuclear magnetism Effects 0.000 description 1
- 238000007500 overflow downdraw method Methods 0.000 description 1
- 230000007310 pathophysiology Effects 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 210000003668 pericyte Anatomy 0.000 description 1
- 210000000578 peripheral nerve Anatomy 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 230000006461 physiological response Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- OLBCVFGFOZPWHH-UHFFFAOYSA-N propofol Chemical compound CC(C)C1=CC=CC(C(C)C)=C1O OLBCVFGFOZPWHH-UHFFFAOYSA-N 0.000 description 1
- 229960004134 propofol Drugs 0.000 description 1
- 230000002685 pulmonary effect Effects 0.000 description 1
- 230000029058 respiratory gaseous exchange Effects 0.000 description 1
- 230000033764 rhythmic process Effects 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 229960002078 sevoflurane Drugs 0.000 description 1
- DFEYYRMXOJXZRJ-UHFFFAOYSA-N sevoflurane Chemical compound FCOC(C(F)(F)F)C(F)(F)F DFEYYRMXOJXZRJ-UHFFFAOYSA-N 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 210000000278 spinal cord Anatomy 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000001225 therapeutic effect Effects 0.000 description 1
- 230000000287 tissue oxygenation Effects 0.000 description 1
- 230000009529 traumatic brain injury Effects 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
- 208000019553 vascular disease Diseases 0.000 description 1
- 230000001457 vasomotor Effects 0.000 description 1
- 238000009423 ventilation Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Landscapes
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The invention relates to the technical field of electroencephalogram analysis and anesthesia monitoring, in particular to an anesthesia management system and method based on novel or multi-mode electroencephalogram monitoring, wherein the system comprises the following components: the device comprises a data acquisition module, a data analysis module and an anesthesia management module; the data acquisition module is used for acquiring operation data and brain function monitoring data of a patient in operation; the data analysis module is used for analyzing the operation data and the brain function monitoring data to obtain an analysis result; and the anesthesia management module is used for performing anesthesia management based on the analysis result. The invention establishes a dynamic balance mode between the multi-mode brain function data and the anesthesia depth monitoring and management mode, and can improve the accurate diagnosis of anesthesia management and the capability of real-time evaluation management first aid.
Description
Technical Field
The invention relates to the technical field of electroencephalogram analysis and anesthesia monitoring, in particular to an anesthesia management system and method based on novel or multi-mode electroencephalogram monitoring.
Background
Anesthesia results from a number of inhibition processes that interact, resulting in loss of consciousness, loss of memory, immobility, and analgesia. Optimal clinical anesthesia involves three factors, sedation, analgesia, and muscle relaxation. The dynamic continuous monitoring and management of patients during anesthesia is a key principle of anesthesiology, including heart rate, heart rhythm, blood pressure, body temperature, oxygen saturation, pulmonary ventilation, etc. In 2018, the american society of anesthesiologists has established guidelines and minimum practice standards for monitoring cardiopulmonary function and blood flow perfusion, including: moderate sedation, pre-drug assessment, convalescence management, and the like. In anesthesia management, anesthesiologists monitor the inhalation and intravenous administration indexes, and observe the reaction of the patient through brain evaluation technology, respiration and circulation monitoring to judge the anesthesia depth of the patient, namely, the relation between the monitoring indexes and the anesthesia target points. The complexity of the anesthesia depth brings difficulty to anesthesia management monitoring, and at present, no single objective index is used for overall quantitative anesthesia monitoring, so that the anesthesia management mode and depth cannot be simplified and unified.
Since the 21 st century, human beings have paid more attention to the study of brain functions, because the complexity of the functional structure, neural network and information transfer of the brain is far beyond the imagination of people, and the study in recent years explores the mystery of brain functions by deep association and system integration of different directions and dimensions of genetics, molecular cytology, neuroinformatics, cognitive function and the like. The brain neural networks are dynamically connected with each other, and are important for regulating the anatomical functions of the brain, blood flow and blood oxygen and maintaining the dynamic balance of the brain. Although the brain is the most important organ, monitoring of the brain is relatively inadequate compared to other body systems, such as the cardiopulmonary system. In addition, the adverse consequences of the brain remain a persistent problem in patients undergoing various surgical procedures. The purpose of electroencephalogram monitoring is to assess and preserve the functional integrity of the brain, brainstem, spinal cord and/or peripheral nerves during surgery, and anesthesiologists and surgeons can be alerted to modify surgical and therapeutic strategies. Comprehensive brain monitoring can optimize perioperative prognosis, and early detection of clinical problems at risk to the brain by providing information on a range of physiological parameters, and timely intervention to mitigate injury. And the device can also be used for guiding the individual anesthesia requirement of a patient and the brain hemodynamic environment, so as to realize the management between the brain hemodynamic measurement and the anesthesia target.
Nowadays, more and more monitors can more specifically evaluate the effect of general anesthesia on the brain. However, these indicators generally reflect indirect responses caused by reduced consciousness levels. Current practice recommendations of the american society of anesthesiologists regarding brain function monitoring indicate that: for patients receiving general anesthesia, the use of brain function monitors is essential, both to reduce the frequency of intraoperative awareness and to monitor the depth of anesthesia. These recommendations are also well accepted by other international professionals.
In recent years, a number of anesthesia depth and brain function monitoring technologies have evolved dramatically, brain electrical monitoring (BIS), brain entropy index (entopy index), motor and sensory evoked potentials, jugular saturation, direct oxygen tissue monitors, and Narcotrend anesthesia/brain electrical depth of consciousness index (NI), enabling clinicians to increasingly use multi-modal brain monitoring. Multi-modal brain monitoring can be divided into three categories: (1) Direct signals monitored invasively (e.g., intracranial pressure (ICP), tissue oxygenation, microdialysis, parenchymal blood flow, etc.); (2) Variables that can be monitored non-invasively (e.g., transcranial Doppler (TCD) or near infrared spectroscopy (NIRS)); (3) The variables describing the pathophysiology of the brain are calculated. Among them, brain electrical signal monitoring (BIS) is increasingly used in anesthesia management, and visual analysis of brain electrical signals is a form of anesthesia depth monitoring analysis.
Brain monitoring is an evolving field of clinical medicine, providing anesthesiologists with increasing options during surgery, aimed at improving brain prognosis during surgery. However, even in the case of brain electrical monitors (BIS) which are widely used in clinic, they are only of monitoring interest under the action of propofol, sevoflurane and several other anaesthetics. Because BIS is subjected to comprehensive, large-sample and multi-center clinical verification, the BIS is a first measuring means of brain effect by the anesthetic drug authenticated by the FDA in the United states, has lower measuring cost and is most widely applied; but reflects only the electrical activity of neurons and does not directly reflect the microcirculation and metabolic state of the brain coupled with neurovascular. Moreover, the same BIS values under different anesthetics do not represent the same depth of anesthesia, and the effect of the drug may also cause considerable differences. BIS index is the energy and phase information of the integrated brain electricity, and reflects the electrical activity of the cerebral cortex; therefore, the BIS value is obtained by performing mathematical treatment on the original electroencephalogram for a certain time, so that the method has certain hysteresis and cannot timely respond to the anesthesia stress level. Modern anesthesia procedures generally include a combination of various modes of anesthesia and drugs, and at present, no clinical index is available that can fully and accurately reflect brain function changes in different states of general anesthesia.
The nuclear magnetism and brain ultrasound lay a good research foundation for measuring and evaluating large blood vessels. But research and reports on microvascular and microneural changes are relatively rare. Although there are considerable studies on brain microcirculation functions using animal experiments such as rats, an exciting result has been obtained. However, these animal experiments using invasive means such as surgery and tissue microtome analysis are not suitable for clinical application, and many studies (Brassard, etc.) show that the response of microcirculation perfusion to the increase and decrease of arterial blood pressure is asymmetric nonlinear, and that the changes of cerebral blood flow and cerebral blood oxygen are more effective in regulating arterial blood pressure, oxygen saturation and blood oxygen index, and that the microcirculation perfusion seems to be more important and the functions of cell level are more represented by the regulation of the microcirculation system in analyzing and evaluating the automatic regulation characteristics of neurovascular coupling, except for the clinical guidance significance thereof. And the structural images such as conventional CT, magnetic resonance and the like cannot be observed. The brain microcirculation system regulation and control and neurovascular coupling mechanism have more definite targets in accurate diagnosis treatment evaluation, and clinical patients benefit more.
In recent years, a data-driven Near-infrared brain blood flow automatic regulation method, called "Near-infrared diffuse light correlation spectrum (Near-infrared diffuse correlation spectroscopy, DCS)", has been used to measure the state of change of blood flow in tissue microvasculature. The technology uses near infrared light as a detection means, describes the influence of the diffusion motion of red blood cells in tissues on the scattering of the light field by utilizing the time autocorrelation function of the light field to detect the motion state of the red blood cells, and is an emerging technology for directly measuring the microcirculation blood flow. This evaluation method provides the possibility of standardized quantitative measurements for maximum utilization of the data. DCS provides several attractive properties for the measurement of microvascular blood flow, such as non-invasive, portable, real-time, and relatively large penetration depths (the cortex of the brain can be measured). Due to the advantages, the DCS technology is already applied to cerebral ischemia of carotid artery stenosis, cerebral vascular diseases such as cerebral apoplexy and Alzheimer, and the like, and shows higher sensitivity of the technology in the aspect of brain microcirculation detection.
Recently, COGiTATE studies by Tas et al in 2021 have shown that the use of cerebral blood flow autoregulation indicators to determine optimal cerebral perfusion pressure is of positive clinical significance in traumatic brain injury patients, and that management under guidance of cerebral blood flow autoregulation indicators is superior to fixed blood pressure thresholds in both functional outcomes and brain injuries in acute stroke patients (Petersen et al 2020). These studies open up the possibility of cerebral blood flow autoregulation indicators in a variety of clinical contexts. Another multicenter study (INFOMATAS study) also has spawned a number of commentary articles that lay the foundation for Meta analysis of the upcoming acute ischemic stroke patient database (Beishon et al, 2020). As a brain imaging technology with high ecological efficiency, high time sampling rate and low cost, a good complementary relation of brain anatomy imaging is formed with fMRI research, but no report on brain function microcirculation and dynamic association of comprehensive indexes such as cerebral blood flow, brain electricity, blood oxygen, arterial blood pressure and oxygenation index is found.
Therefore, there is a need for a new or multi-modal electroencephalogram monitoring based anesthesia management system and method.
Disclosure of Invention
The invention aims to provide an anesthesia management system and method based on novel or multi-mode electroencephalogram monitoring, which comprehensively analyze related influencing factors in a medical fusion mode, deeply mine brain function data, establish a dynamic balance mode between the multi-mode brain function data and anesthesia depth monitoring and management modes, and further improve the accurate diagnosis of anesthesia management and the capability of real-time evaluation management first aid.
In order to achieve the above object, the present invention provides the following solutions:
an anesthesia management system based on novel or multi-modality electroencephalogram monitoring, comprising: the device comprises a data acquisition module, a data analysis module and an anesthesia management module;
The data acquisition module is used for acquiring operation data and brain function monitoring data of a patient in operation;
the data analysis module is used for analyzing the operation data and the brain function monitoring data to obtain an analysis result;
and the anesthesia management module is used for performing anesthesia management based on the analysis result.
Optionally, the data acquisition module includes: a surgical data acquisition unit and a brain function monitoring data acquisition unit;
The operation data acquisition unit is used for acquiring operation types, operation positions and anesthesia modes of patients in operation;
the brain function monitoring data acquisition unit is used for acquiring brain electrical function indexes, brain blood flow indexes and physiological indexes in anesthesia of the patient in operation based on a multi-mode brain electrical monitoring technology.
Optionally, the electroencephalogram function index is measured by an 8-conductor electroencephalogram system, the cerebral blood flow index is measured by a near infrared diffuse light blood flow meter, and the near infrared diffuse light blood flow meter comprises a flat-plate type optical fiber probe suitable for brain measurement and is used for covering forehead and temporal lobe areas and measuring task-induced brain cortex microcirculation blood flow reaction.
Optionally, the physiological index comprises: sex, age, body weight, body temperature, radial artery blood pressure, intraocular pressure, internal carotid venous pressure, heart rate, pulse oxygen saturation, arterial blood PH, blood lactic acid, arterial blood oxygen saturation, arterial blood oxygen partial pressure, arterial blood CO 2 partial pressure, hemoglobin concentration, hematocrit HCT, brain oxygen saturation, BIS value, duration of surgery, length of anesthesia, wake-up time, wake-up quality, preoperative and postoperative early cognitive function scores, infusion volume, urine volume.
Optionally, the data analysis module includes: the model building unit and the data analysis unit;
the model construction unit is used for constructing an analysis model based on data analysis and feature extraction;
the data analysis unit is used for inputting the operation type, the operation position, the anesthesia mode, the brain electrical function index, the brain blood flow index and the physiological index in anesthesia into the analysis model to obtain anesthesia characteristic parameters.
Optionally, the analysis model includes: the device comprises a first analysis layer, a second analysis layer, a third analysis layer and a summarizing layer, wherein the first analysis layer is used for analyzing an electroencephalogram function index, a cerebral blood flow index and an in-anesthesia physiological index and acquiring characteristic parameters related to anesthesia depth; the second analysis layer is used for carrying out correlation analysis on the characteristic parameters and obtaining synchronous and asynchronous relations of the characteristic parameters in the activation process of the cerebral cortex; the third analysis layer is used for determining the relevance between physiological indexes in anesthesia; the summarizing layer is used for determining the anesthesia characteristic parameters according to analysis results of the first analysis layer, the second analysis layer and the third analysis layer.
Optionally, the process of constructing the analysis model includes:
constructing n groups of subjects according to the operation type, the operation position and the anesthesia mode;
Acquiring an intraoperative electroencephalogram function index, a cerebral blood flow index and an anesthetizing physiological index of the subject based on a brain function monitoring technology;
comparing the difference among the brain electrical function index, the brain blood flow index and the physiological index in anesthesia by using student t test and analysis of variance to obtain a sensitivity index;
Classifying and performing correlation analysis on the electroencephalogram function index, the cerebral blood flow index and the physiological index in anesthesia by using a support vector machine, and determining a brain microcirculation mechanism of nerve-blood vessel coupling and synchronous and asynchronous relations of the electroencephalogram function index, the cerebral blood flow index and the physiological index in anesthesia in the activation process of cerebral cortex;
adopting multiple linear regression to analyze the association between physiological indexes in anesthesia to obtain the association of anesthesia indexes;
The analysis model is constructed based on the sensitivity index, the brain microcirculation mechanism of nerve-blood vessel coupling, the brain electrical function index, the brain blood flow index and the synchronous and asynchronous relation of physiological indexes in anesthesia in the cerebral cortex activation process, and the anesthesia index relevance.
Optionally, the anesthesia management module includes: the device comprises an anesthesia administration determining unit, an anesthesia depth control unit and an anesthesia risk early warning unit;
The anesthesia administration determining unit is used for determining an anesthesia administration dosage based on the analysis result;
the anesthesia depth control unit is used for controlling the anesthesia depth based on the analysis result;
and the anesthesia risk early warning unit is used for early warning of anesthesia risk based on the analysis result.
In order to further achieve the above object, the present invention further provides an anesthesia management method based on novel or multi-mode electroencephalogram monitoring, including:
Collecting operation data and brain function monitoring data of a patient in operation;
Analyzing the operation data and the brain function monitoring data to obtain an analysis result;
and performing anesthesia management based on the analysis result.
The beneficial effects of the invention are as follows:
(1) According to the characteristics of brain function technology (BIS/DCS) and the brain microcirculation regulation and control process, the invention applies signal acquisition and information technology processing to general anesthesia operation, monitors brain electricity and brain blood flow and multiple physiological parameter indexes, and the brain function technology and the physiological parameter indexes applied by the invention directly reflect brain microcirculation mechanism in general anesthesia, have unique technical advantages, are beneficial to an anesthesia provider to make optimal decisions, and accurately adjust anesthesia depth so as to realize safer anesthesia management.
(2) The invention collects brain function signals (brain electricity and brain blood flow), carries out data modeling and feature extraction on physiological parameter indexes and maps to obtain a plurality of feature parameters related to microcirculation regulation, and determines objective connection between brain microcirculation and general anesthesia modes and depths by using a machine learning method (support vector machine algorithm) and an artificial intelligence method for evaluating brain functions, thereby observing the application of the microcirculation regulation in closed loop anesthesia in future, helping to understand the brain of a patient in depth and making optimal decisions for anesthesiologists.
(3) According to brain microcirculation mechanism and brain electrical characteristics of related anesthetic drugs, brain blood flow images and data, the invention can further help anesthesiologists to know the anesthesia state of patients, thereby guiding individual medication, evaluating and adjusting important vital organ functions of patients in the perioperative period, timely preventing and treating abnormal conditions in the operation, reducing the occurrence of postoperative complications and minimizing the risks of operation and general anesthesia.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an anesthesia management system based on novel or multi-modal electroencephalogram monitoring according to an embodiment of the present invention;
FIG. 2 is a flow chart of the analytical model construction according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a distribution of fiber optic probes for DCS cerebral blood flow measurement in accordance with an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
The embodiment provides an anesthesia management system based on novel or multi-mode electroencephalogram monitoring, as shown in fig. 1, including: the device comprises a data acquisition module, a data analysis module and an anesthesia management module;
the data acquisition module is used for acquiring operation data and brain function monitoring data of a patient in operation;
The data analysis module is used for analyzing the operation data and the brain function monitoring data and obtaining analysis results;
And the anesthesia management module is used for performing anesthesia management based on the analysis result.
The data acquisition module comprises: the device comprises an operation data acquisition unit and a brain function monitoring data acquisition unit, wherein the operation data acquisition unit is used for acquiring operation types, operation positions and anesthesia modes of patients in operation; the brain function monitoring data acquisition unit is used for acquiring brain electrical function indexes, brain blood flow indexes and physiological indexes in anesthesia of the patient in operation based on a multi-mode brain electrical monitoring technology.
The data analysis module comprises: the system comprises a model construction unit and a data analysis unit, wherein the model construction unit is used for constructing an analysis model based on data analysis and feature extraction; the data analysis unit is used for inputting the operation type, operation position, anesthesia mode, brain electrical function index, brain blood flow index and physiological index in anesthesia into the analysis model to obtain anesthesia characteristic parameters.
Specifically, the analysis model comprises a first analysis layer, a second analysis layer, a third analysis layer and a total junction layer, wherein the first analysis layer is used for analyzing an electroencephalogram function index, a cerebral blood flow index and a physiological index in anesthesia to obtain characteristic parameters related to anesthesia depth; the second analysis layer is used for carrying out correlation analysis on the characteristic parameters and obtaining synchronous and asynchronous relations of the characteristic parameters in the activation process of the cerebral cortex; the third analysis layer is used for determining the relevance between physiological indexes in anesthesia; the summarizing layer is used for determining anesthesia characteristic parameters according to analysis results of the first analysis layer, the second analysis layer and the third analysis layer.
The construction method and process of the analysis model are shown in fig. 2, and specifically include the following steps:
Step 1, subject grouping:
The present example selects patients who are subjected to general anesthesia non-neurosurgery in a surgery room, and the general anesthesia of different surgical positions is divided into eight groups: (1) a general intravenous anesthesia recumbent position (supine position, lateral position and prone position) (2) a general intravenous anesthesia head high-foot low position (3) a general intravenous anesthesia head low-foot high position (4) a general inhalation anesthesia recumbent position (supine position, lateral position and prone position) (5) a general inhalation anesthesia head high-foot low position (6) a general inhalation anesthesia head low-foot high position (7) a general abdominal operation patient, no other complications, and (8) an extracorporeal circulation heart operation patient, no other complications. The exclusion criteria were: symptomatic lung disease with age less than or equal to 18 years old, poorly controlled hypertension (systolic blood pressure greater than or equal to 140mm Hg), poorly controlled diabetes (blood glucose greater than or equal to 160mg dl 21), or diabetes requiring insulin therapy. All patients did not take any medication orally 8 hours prior to surgery. Each group had 30 subjects, 8 total, and 240 total subjects.
Step2, anesthesia mode intervention:
Brain function and hemodynamic variables were evaluated in eight groups, with different body position changes during surgery and the effects of medication on physiological parameters and on electroencephalogram and cerebral blood flow indices, and when surgery resulted in failure of brain autoregulation function, the vasodilation response increased CBF, resulting in cerebral congestion and altered brain microcirculation. The cerebral blood flow index and BIS parameters and profiles were measured at the end of each step. Clinical indexes are analyzed by combining near infrared spectrum and electroencephalogram. Effective clinical comparable parameters are defined, reflecting the automatic regulation of tissue cerebral oxygenation and cerebral microcirculation, the anesthesiologist evaluates and adjusts the vital organ functions of the patient before the body position changes, the values obtained by near infrared DCS are compared with the measured values of brain electricity and physiology, and each variable represents different physiological processes. The correlation that exists between them is found to assess its ability in brain self-regulating monitoring.
The main measurement index is as follows: gender, age, body weight, body temperature, radial arterial blood pressure (based on patient head position, reflecting cranially arterial blood pressure), intraocular pressure (ultrasound measurement, assessing intracranial pressure), jugular venous pressure (ultrasound measurement), heart rate, pulse oxygen saturation, arterial blood PH, blood lactic acid, arterial blood oxygen saturation, arterial blood oxygen partial pressure, arterial blood CO2 partial pressure, hemoglobin concentration, hematocrit HCT, brain oxygen saturation, BIS value, operative duration, length of anaesthesia, wake time, wake quality, preoperative and postoperative early cognitive function score, infusion volume (average ml/h), urine volume) were measured at various time points.
Measuring time sequence: the cerebral blood flow and BIS index and clinical physiological parameters were measured after 1) baseline recording was 5 minutes before the first anesthesia induction, 2) the second time after the induction of the cannula, 3) the third time when disinfection was performed before the operation was started, 4) the fourth time when the skin was incised by the operation, 5) the fifth time when the body position was changed, 6) the sixth time was five minutes before the operation was completed, 7) the seventh time after the general anesthesia extubation, 8) the eighth time after general anesthesia awakening. Is used for early identification and subsequent treatment, and timely prevents and treats abnormal conditions in the operation.
Step 3, brain function measurement:
(i) Brain electrical (BIS) measurement: the equipment selects an 8-conduction electroencephalogram system, and the sampling frequency is 1000Hz. The electrode potential is placed according to the international 10-20system (international 10-20 system), and the acquisition points are located in the frontal lobe area and the temporal lobe area, so that the anesthesia depth of the surgical patient can be effectively detected. And has no hair interference, and can obtain data with high signal-to-noise ratio.
(Ii) DCS cerebral blood flow measurement: the near infrared diffuse light (DCS) blood flow instrument is a novel optical technology device capable of directly detecting tissue microcirculation, and mainly comprises a near infrared single longitudinal mode laser (coherence length is more than 5 m), a single photon detector, a digital correlator, a data acquisition card and other modules, wherein the hardware is controlled by a computer according to the following modes: near infrared light emitted by the laser is transmitted into the tissue through the multimode optical fiber, photons injected into the tissue are absorbed and scattered in a series, and finally a part of photons escape from the surface of the tissue which is a few centimetres away from the source optical fiber. The collected photons are counted by a single-photon detector through a single-mode fiber, the finally output photons are received by an 8-channel digital correlator, and the correlator performs autocorrelation operation to finally obtain a non-normalized light intensity time autocorrelation function G2 (tau). The normalized G2 (τ) function (i.e., G2 (τ)) satisfies Siegert relationship with the optical field time autocorrelation G1 (τ). The unnormalized G1 (τ) function (i.e., G1 (τ)) satisfies the diffusion correlation equation. Typically, the blood flow index values are analytically extracted from the diffusion-dependent equation under specific boundary conditions (e.g., semi-infinite).
DCS technology has been used in the united states and europe for a number of physiological and clinical studies of stroke, craniocerebral injury, anesthesia and intensive care, with which the accuracy, stability and safety of measuring cerebral blood flow have been fully demonstrated, and this example will first apply DCS to surgical patients receiving general anesthesia. The accuracy, stability and safety of measuring cerebral blood flow by using the technology have been fully verified, and the embodiment is to apply DCS to NVU damage mechanism research in anesthesia management for the first time. For this purpose, a flat-panel fiber optic probe suitable for brain measurement is designed to cover the frontal and temporal lobe areas, and can be used to measure task-induced micro-circulatory blood flow responses of the cerebral cortex, as shown in fig. 3.
Measuring indexes of each time point before and after operation are recorded with different results, and measuring eight times in total comprises the following steps: sex, age, body weight, body temperature, radial artery blood pressure, intraocular pressure, internal carotid venous pressure, heart rate, pulse oxygen saturation, arterial blood PH, blood lactic acid, arterial blood oxygen saturation, arterial blood oxygen partial pressure, arterial blood CO2 partial pressure, hemoglobin concentration, hematocrit HCT, brain oxygen saturation, BIS value, duration of surgery, length of anesthesia, wake-up time, wake-up quality, preoperative and postoperative early cognitive function scores, infusion volume (average ml/h), urine volume.
Measuring time sequence: the cerebral blood flow and BIS index and clinical physiological parameters were measured after 1) baseline recording was 5 minutes before the first anesthesia induction, 2) the second time after the induction of the cannula, 3) the third time when disinfection was performed before the operation was started, 4) the fourth time when the skin was incised by the operation, 5) the fifth time when the body position was changed, 6) the sixth time was five minutes before the operation was completed, 7) the seventh time after the general anesthesia extubation, 8) the eighth time after general anesthesia awakening. Is used for early identification and subsequent treatment, and timely prevents and treats abnormal conditions in the operation.
Step 4, data extraction and analysis:
step 4.1. Brain function monitoring and physiological parameter modeling extraction:
for eight groups of subjects, the following characteristic indices were calculated:
(1) For the microcirculation blood flow and brain electrical changes caused by different positions of the total intravenous anesthesia, calculating response slope values within 30 seconds, wherein Fourier transformation and energy spectrum parameters are used for representing the adjusting capability of the anesthesia depth to the environment in the tissue; a smaller slope indicates better ability of closed loop control of anesthesia to maintain the environment within the tissue and the blood brain barrier.
(2) For parameter changes of different positions of total inhalation anesthesia, calculating an integral value of brain electricity/brain blood flow, and representing sensing and regulating capacity of peripheral cells on hypoxia; a larger integrated value indicates that the coupling of neurovascular is evident for total inhaled brain microcirculation vasodilation.
(3) Calculating the slope and integral value of the brain electricity within 10 seconds of inhalation and total vein anesthesia to represent the instantaneous activation speed and amplitude of brain neurons; a larger slope and integral value indicates a better activation of the neurons; in addition, the slope and the integral value of the electroencephalogram/cerebral blood flow are calculated; the larger slope and integral value characterize the information transfer capacity and brain metabolism rate of the brain.
(4) Calculating a response slope, an integral value, a gravity center value and a recovery period slope within 10 seconds of the electroencephalogram/cerebral blood flow in the inhaled and whole veins and the normal subject to represent stabilization of cerebral hemodynamics; a larger slope and integral value and a smaller gravity center value indicate that the brain function monitoring overall response is better; a large recovery slope indicates good maintainance of the cerebral neurovascular coupling.
Statistical methods of student t-test and analysis of variance (ANOVA) were used to compare the differences between the above characteristic parameters between groups to determine characteristic parameters that are closely related to depth of anesthesia. On the basis, carrying out correlation analysis and synchronous/asynchronous relation between characteristic parameters; finally, determining classification, calculation, induction and summarization of different anesthesia management mode groups by multiple regression analysis and a machine learning method, and evaluating the personalized medical intervention effect and the optimization scheme for different groups by an artificial intelligence method.
Step 4.2. Physiological parameter index measurement:
the main measurement index is as follows: sex, age, body weight, body temperature, radial arterial blood pressure (based on patient's head position, reflecting cranially arterial blood pressure), intraocular pressure (ultrasound measurement, evaluation of intracranial pressure), internal jugular venous pressure (ultrasound measurement), heart rate, pulse oxygen saturation, arterial blood PH, blood lactic acid, arterial blood oxygen saturation, arterial blood oxygen partial pressure, arterial blood CO2 partial pressure, hemoglobin concentration, hematocrit HCT, brain oxygen saturation, BIS value, operative duration, length of anaesthesia, wake time, wake quality, preoperative and postoperative early cognitive function scores, infusion volume (average ml/h), urine volume) are measured by the anesthesiologist at operative anaesthesia to record the change of various physiological parameters at various time points.
Step 4.3. Data analysis:
(i) The original signal collected by the BIS (brain electrical) equipment is the voltage amplitude collected by each channel electrode, and the data point with the signal being centered at each second (namely about 0.5 seconds each) is taken as a series. After pretreatment by hanning window to reduce the influence of spectrum leakage, the data length of the series is the original data sampling rate (for example, 500 Hz). Converting the original data of each series into a frequency domain through Fast Fourier Transform (FFT) to obtain a continuous power spectrum curve, wherein the sampling rate is 1Hz; the present embodiment mainly analyzes the frequency spectrum of the alpha and beta bands related to the anesthetic depth at the frontal and temporal lobe sites.
(Ii) Blood flow index collected by DCS equipment; scatter plots and blood flow images, mainly including oxygenated, deoxygenated hemoglobin, and changes in total hemoglobin concentration (i.e., oxy-Hb, and THC); the sampling rate is 10Hz. Converting the sampling rate of the cerebral blood oxygen variable into 1Hz by a moving average method; the blood flow index collected by the DCS device can be set according to the signal-to-noise ratio, and the sampling rate is also set to 1Hz in this embodiment, which is consistent with the processed blood oxygen variable.
(Iii) Statistical analysis is carried out on physiological indexes and electroencephalogram data in anesthesia extracted in operation through student t test and ANOVA method, so that significant differences and sensitivity indexes among all groups are obtained, and the threshold value is set to be p <0.05.
(Iv) A machine learning method (support vector machine) is used to classify the plurality of feature parameters to obtain an accurate anesthesia management scheme and a relationship between depth and brain microcirculation. For each subject, physiological signals (brain electricity/blood oxygen/blood flow/blood pressure) generated by the same task protocol were subjected to correlation analysis to determine the brain microcirculation mechanism of the neurovascular coupling and the synchronous and asynchronous relationship of these signals during activation of the brain cortex.
Step 5, constructing an analysis model;
based on the sensitivity index obtained by the analysis, the brain microcirculation mechanism of nerve-blood vessel coupling, the brain electrical function index, the brain blood flow index, the synchronous and asynchronous relation of the physiological index in anesthesia in the activation process of the cerebral cortex and the anesthesia index relevance, an analysis model is constructed.
The anesthesia management module comprises an anesthesia administration determining unit, an anesthesia depth control unit and an anesthesia risk early warning unit, wherein the anesthesia administration determining unit is used for determining an anesthesia administration dosage based on an analysis result; an anesthesia depth control unit for controlling an anesthesia depth based on the analysis result; and the anesthesia risk early warning unit is used for early warning of anesthesia risk based on the analysis result.
In order to further optimize the technical scheme, the embodiment also provides an anesthesia management method based on novel or multi-mode electroencephalogram monitoring, which comprises the following steps:
Collecting operation data and brain function monitoring data of a patient in operation;
analyzing the operation data and the brain function monitoring data to obtain an analysis result;
Anesthesia management is performed based on the analysis results.
Collecting surgical data and brain function monitoring data of an intraoperative patient includes: the method comprises the steps of collecting operation types, operation positions and anesthesia modes of patients in operation, and obtaining brain electrical function indexes, brain blood flow indexes and physiological indexes in anesthesia of the patients in operation based on a multi-mode brain electrical monitoring technology.
The analysis model is constructed through data analysis and feature extraction, and comprises a first analysis layer, a second analysis layer, a third analysis layer and a total junction layer, wherein the first analysis layer is used for analyzing an electroencephalogram function index, a cerebral blood flow index and an in-anesthesia physiological index to obtain feature parameters related to anesthesia depth; the second analysis layer is used for carrying out correlation analysis on the characteristic parameters and obtaining synchronous and asynchronous relations of the characteristic parameters in the activation process of the cerebral cortex; the third analysis layer is used for determining the relevance between physiological indexes in anesthesia; the summarizing layer is used for determining anesthesia characteristic parameters according to analysis results of the first analysis layer, the second analysis layer and the third analysis layer.
The analytical model construction method and process are as follows:
Step 1, subject grouping:
The present example selects patients who are subjected to general anesthesia non-neurosurgery in a surgery room, and the general anesthesia of different surgical positions is divided into eight groups: (1) a general intravenous anesthesia recumbent position (supine position, lateral position and prone position) (2) a general intravenous anesthesia head high-foot low position (3) a general intravenous anesthesia head low-foot high position (4) a general inhalation anesthesia recumbent position (supine position, lateral position and prone position) (5) a general inhalation anesthesia head high-foot low position (6) a general inhalation anesthesia head low-foot high position (7) a general abdominal operation patient, no other complications, and (8) an extracorporeal circulation heart operation patient, no other complications. The exclusion criteria were: symptomatic lung disease with age less than or equal to 18 years old, poorly controlled hypertension (systolic blood pressure greater than or equal to 140mm Hg), poorly controlled diabetes (blood glucose greater than or equal to 160mg dl 21), or diabetes requiring insulin therapy. All patients did not take any medication orally 8 hours prior to surgery. Each group had 30 subjects, 8 total, and 240 total subjects.
Step2, anesthesia mode intervention:
Brain function and hemodynamic variables were evaluated in eight groups, with different body position changes during surgery and the effects of medication on physiological parameters and on electroencephalogram and cerebral blood flow indices, and when surgery resulted in failure of brain autoregulation function, the vasodilation response increased CBF, resulting in cerebral congestion and altered brain microcirculation. The cerebral blood flow index and BIS parameters and profiles were measured at the end of each step. Clinical indexes are analyzed by combining near infrared spectrum and electroencephalogram. Effective clinical comparable parameters are defined, reflecting the automatic regulation of tissue cerebral oxygenation and cerebral microcirculation, the anesthesiologist evaluates and adjusts the vital organ functions of the patient before the body position changes, the values obtained by near infrared DCS are compared with the measured values of brain electricity and physiology, and each variable represents different physiological processes. The correlation that exists between them is found to assess its ability in brain self-regulating monitoring.
The main measurement index is as follows: gender, age, body weight, body temperature, radial arterial blood pressure (arterial blood pressure reflecting the cranium with the head position of the patient as a baseline level), intraocular pressure (ultrasound measurement, evaluation of intracranial pressure), internal jugular venous pressure (ultrasound measurement), heart rate, pulse oxygen saturation, arterial blood PH, blood lactic acid, arterial blood oxygen saturation, arterial blood oxygen partial pressure, arterial blood CO2 partial pressure, hemoglobin concentration, hematocrit HCT, brain oxygen saturation, BIS value, operative duration, anesthesia duration, wake time, wake quality, preoperative and postoperative early cognitive function scores, infusion volume (average ml/h), various time points of urine volume were measured with different recorded results.
Measuring time sequence: the brain blood flow microcirculation values and BIS index and clinical physiological parameters were measured after 1) baseline recording was 5 minutes before the first anesthesia induction, 2) the second time after the induction of the cannula, 3) the third time when disinfection was performed before the operation was started, 4) the fourth time when the skin was incised by the operation, 5) the fifth time when the body position was changed, 6) the sixth time was five minutes before the operation was ended, 7) the seventh time after the general anesthesia extubation, 8) the eighth time after general anesthesia awakening. Is used for early identification and subsequent treatment, and timely prevents and treats abnormal conditions in the operation.
Step 3, brain function measurement:
(i) Brain electrical (BIS) measurement: the equipment selects an 8-conduction electroencephalogram system, and the sampling frequency is 1000Hz. The electrode potential is placed according to the international 10-20system (international 10-20 system), and the acquisition points are located in the frontal lobe area and the temporal lobe area, so that the anesthesia depth of the surgical patient can be effectively detected. And has no hair interference, and can obtain data with high signal-to-noise ratio.
(Ii) DCS cerebral blood flow measurement: the near infrared diffuse light (DCS) blood flow instrument is a novel optical technology device capable of directly detecting tissue microcirculation, and mainly comprises a near infrared single longitudinal mode laser (coherence length is more than 5 m), a single photon detector, a digital correlator, a data acquisition card and other modules, wherein the hardware is controlled by a computer according to the following modes: near infrared light emitted by the laser is transmitted into the tissue through the multimode optical fiber, photons injected into the tissue are absorbed and scattered in a series, and finally a part of photons escape from the surface of the tissue which is a few centimetres away from the source optical fiber. The collected photons are counted by a single-photon detector through a single-mode fiber, the finally output photons are received by an 8-channel digital correlator, and the correlator performs autocorrelation operation to finally obtain a non-normalized light intensity time autocorrelation function G2 (tau). The normalized G2 (τ) function (i.e., G2 (τ)) satisfies Siegert relationship with the optical field time autocorrelation G1 (τ). The unnormalized G1 (τ) function (i.e., G1 (τ)) satisfies the diffusion correlation equation. Typically, the blood flow index values are analytically extracted from the diffusion-dependent equation under specific boundary conditions (e.g., semi-infinite).
DCS technology has been used in physiological and clinical studies of various cerebral strokes, craniocerebral injuries, anesthesia and intensive care, and the accuracy, stability and safety of measuring cerebral blood flow microcirculation using this technology has been fully verified, the present example will be the first application of DCS to surgical patients receiving general anesthesia. The accuracy, stability and safety of measuring cerebral blood flow by using the technology have been fully verified, and the embodiment is to apply DCS to NVU damage mechanism research in anesthesia management for the first time. For this purpose, a flat-panel fiber optic probe suitable for brain measurement is designed to cover the frontal and temporal lobe areas, and can be used to measure task-induced micro-circulatory blood flow responses of the cerebral cortex, as shown in fig. 3.
Measuring indexes of each time point before and after operation are recorded with different results, and measuring eight times in total comprises the following steps: sex, age, body weight, body temperature, radial artery blood pressure, intraocular pressure, internal carotid venous pressure, heart rate, pulse oxygen saturation, arterial blood PH, blood lactic acid, arterial blood oxygen saturation, arterial blood oxygen partial pressure, arterial blood CO2 partial pressure, hemoglobin concentration, hematocrit HCT, brain oxygen saturation, BIS value, duration of surgery, length of anesthesia, wake-up time, wake-up quality, preoperative and postoperative early cognitive function scores, infusion volume (average ml/h), urine volume.
Measuring time sequence: the cerebral blood flow and BIS index and clinical physiological parameters were measured after 1) baseline recording was 5 minutes before the first anesthesia induction, 2) the second time after the induction of the cannula, 3) the third time when disinfection was performed before the operation was started, 4) the fourth time when the skin was incised by the operation, 5) the fifth time when the body position was changed, 6) the sixth time was five minutes before the operation was completed, 7) the seventh time after the general anesthesia extubation, 8) the eighth time after general anesthesia awakening. Is used for early identification and subsequent treatment, and timely prevents and treats abnormal conditions in the operation.
Step 4, data extraction and analysis:
step 4.1. Brain function monitoring and physiological parameter modeling extraction:
for eight groups of subjects, the following characteristic indices were calculated:
(1) For cerebral blood flow and brain electrical changes caused by different body positions of the total intravenous anesthesia, calculating response slope values within 30 seconds, wherein Fourier transformation and energy spectrum parameters are used for representing the adjusting capability of the anesthesia depth to the environment in the tissue; a smaller slope indicates better ability of closed loop control of anesthesia to maintain the environment within the tissue and the blood brain barrier.
(2) For parameter changes of different positions of total inhalation anesthesia, calculating an integral value of brain electricity/brain blood flow, and representing sensing and regulating capacity of peripheral cells on hypoxia; a larger integrated value indicates that the coupling of neurovascular is evident for total inhaled brain microcirculation vasodilation.
(3) Calculating the slope and integral value of the brain electricity within 10 seconds of inhalation and total vein anesthesia to represent the instantaneous activation speed and amplitude of brain neurons; a larger slope and integral value indicates a better activation of the neurons; in addition, the slope and the integral value of the electroencephalogram/cerebral blood flow are calculated; the larger slope and integral value characterize the information transfer capacity and brain metabolism rate of the brain.
(4) Calculating a response slope, an integral value, a gravity center value and a recovery period slope within 10 seconds of the electroencephalogram/cerebral blood flow in the inhaled and whole veins and the normal subject to represent stabilization of cerebral hemodynamics; a larger slope and integral value and a smaller gravity center value indicate that the brain function monitoring overall response is better; a large recovery slope indicates good maintainance of the cerebral neurovascular coupling.
Statistical methods of studentt test and analysis of variance (ANOVA) were used to compare the above feature parameters between groups and determine feature parameters that are closely related to depth of anesthesia. On the basis, carrying out correlation analysis and synchronous/asynchronous relation between characteristic parameters; finally, determining classification, calculation, induction and summarization of different anesthesia management mode groups by multiple regression analysis and a machine learning method, and evaluating the personalized medical intervention effect and the optimization scheme for different groups by an artificial intelligence method.
Step 4.2. Physiological parameter index measurement:
the main measurement index is as follows: sex, age, body weight, body temperature, radial arterial blood pressure (based on patient's head position, reflecting cranially arterial blood pressure), intraocular pressure (ultrasound measurement, evaluation of intracranial pressure), internal jugular venous pressure (ultrasound measurement), heart rate, pulse oxygen saturation, arterial blood PH, blood lactic acid, arterial blood oxygen saturation, arterial blood oxygen partial pressure, arterial blood CO2 partial pressure, hemoglobin concentration, hematocrit HCT, brain oxygen saturation, BIS value, operative duration, length of anaesthesia, wake time, wake quality, preoperative and postoperative early cognitive function scores, infusion volume (average ml/h), urine volume) are measured by the anesthesiologist at operative anaesthesia to record the change of various physiological parameters at various time points.
Step 4.3. Data analysis:
(i) The original signal collected by the BIS (brain electrical) equipment is the voltage amplitude collected by each channel electrode, and the data point with the signal being centered at each second (namely about 0.5 seconds each) is taken as a series. After pretreatment by hanning window to reduce the influence of spectrum leakage, the data length of the series is the original data sampling rate (for example, 500 Hz). Converting the original data of each series into a frequency domain through Fast Fourier Transform (FFT) to obtain a continuous power spectrum curve, wherein the sampling rate is 1Hz; the present embodiment mainly analyzes the frequency spectrum of the alpha and beta bands related to the anesthetic depth at the frontal and temporal lobe sites.
(Ii) Blood flow index collected by DCS equipment; scatter plots and blood flow images, mainly including oxygenated, deoxygenated hemoglobin, and changes in total hemoglobin concentration (i.e., oxy-Hb, and THC); the sampling rate is 10Hz. Converting the sampling rate of the cerebral blood oxygen variable into 1Hz by a moving average method; the blood flow index collected by the DCS device can be set according to the signal-to-noise ratio, and the sampling rate is also set to 1Hz in this embodiment, which is consistent with the processed blood oxygen variable.
(Iii) Statistical analysis is carried out on physiological indexes and electroencephalogram data in anesthesia extracted in operation through student t test and ANOVA method, so that significant differences and sensitivity indexes among all groups are obtained, and the threshold value is set to be p <0.05.
(Iv) A machine learning method (support vector machine) is used to classify the plurality of feature parameters to obtain an accurate anesthesia management scheme and a relationship between depth and brain microcirculation. For each subject, physiological signals (brain electricity/blood oxygen/blood flow/blood pressure) generated by the same task protocol were subjected to correlation analysis to determine the brain microcirculation mechanism of the neurovascular coupling and the synchronous and asynchronous relationship of these signals during activation of the brain cortex.
Step 5, constructing an analysis model;
based on the sensitivity index obtained by the analysis, the brain microcirculation mechanism of nerve-blood vessel coupling, the brain electrical function index, the brain blood flow index, the synchronous and asynchronous relation of the physiological index in anesthesia in the activation process of the cerebral cortex and the anesthesia index relevance, an analysis model is constructed.
Performing anesthesia management based on the analysis results includes: presetting a data range of cerebral microcirculation blood flow, carrying out abnormality early warning according to whether an analysis result is in the preset data range, carrying out further analysis according to the abnormality early warning, judging the reason for causing cerebral microcirculation abnormality, and carrying out anesthesia management specific adjustment according to the specific reason for causing cerebral microcirculation abnormality and patient reaction.
The method comprises the following steps: the brain microcirculation blood flow is preset to have a proper data range, and the analysis result is more or less than the range, so that brain injury results can occur, and early warning can be provided to help a clinician to discover abnormality in time. The abnormality may be triggered by a number of factors, alone or in combination, by further layering processes, helping the clinician analyze the cause of the cerebral blood flow microcirculation abnormality: adjusting the anesthesia depth if the anesthesia depth is not suitable for the main reason; if the blood pressure abnormality is the main cause, the circulation index is adjusted; if abnormal blood flow velocity is the main cause, the correlation of vasomotor status, arterial blood pressure and intracranial pressure and venous pressure is concerned; if the combination of the factors results, the monitoring process can be further carried out through the adjustment of the related factors. In a word, the method is a closely monitoring and dynamically adjusting process, so that a clinician can track and monitor and process more accurately and pertinently, and the perioperative medical safety of a patient in operation is better ensured.
The embodiment adopts the international emerging Diffusion Correlation Spectroscopy (DCS) technology to measure the microvascular blood flow of the cerebral cortex, can directly measure the microcirculation of the cerebral tissue, and constructs the key link of the method part. In addition, the physiological parameter indexes detected in BIS and anesthesia are also utilized, and the physiological parameter indexes are also utilized together with the DCS technology to carry out multi-mode physiological parameter measurement on the hypoxia state of neuron activity and posture change and blood flow reaction. The medical fusion method is applied to anesthesiology brain function detection for the first time.
The embodiment adopts the international emerging Diffusion Correlation Spectroscopy (DCS) technology to measure the microvascular blood flow of the cerebral cortex, can quantitatively describe the response of the blood vessel, and constructs the key link of the method part. In the study of the nerve-blood vessel coupling mechanism, since the physical properties of the micro-blood vessels cannot be directly measured in vivo, physiological responses such as diastole and systole can be reflected by the change of blood flow, so the blood flow dynamics of the micro-blood vessels are key factors for the study of the nerve-blood vessel coupling mechanism. In the clinically available techniques, transcranial Doppler is currently only able to measure blood flow in the main blood vessels and does not directly reflect blood flow in the microvasculature. In addition, neuronal activity, blood oxygen response, blood flow response are measured using the multi-modal electroencephalogram technique together, so that the nerve-blood vessel coupling of each level is analyzed and interpreted in all directions, which has not been used in anesthesia management.
In addition, the embodiment designs a plurality of characteristic parameters of electroencephalogram/blood flow under a plurality of paradigms, accurately classifies the damage of different mechanisms such as neuron, A-P signal transmission, pericyte, endothelial cell and the like by utilizing an artificial intelligence method, adopts diversified anesthesia intervention measures, and can directly convert research results into clinic, thereby being beneficial to patients. The embodiment can be used for evaluation and diagnosis before anesthesia, further development of diseases can be prevented in advance, intervention treatment is performed early, brain microcirculation mechanism and intervention targets in anesthesia management are explored, morbidity and disability rate are reduced, the field is still heavy and far away, and research is currently performed, and results are expected and verified.
The above embodiments are merely illustrative of the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, but various modifications and improvements made by those skilled in the art to which the present invention pertains are made without departing from the spirit of the present invention, and all modifications and improvements fall within the scope of the present invention as defined in the appended claims.
Claims (9)
1. Anesthesia management system based on novel or multimode brain electricity monitoring, characterized by comprising: the device comprises a data acquisition module, a data analysis module and an anesthesia management module;
The data acquisition module is used for acquiring operation data and brain function monitoring data of a patient in operation;
the data analysis module is used for analyzing the operation data and the brain function monitoring data to obtain an analysis result;
and the anesthesia management module is used for performing anesthesia management based on the analysis result.
2. The anesthesia management system based on novel or multi-modality electroencephalogram monitoring according to claim 1, wherein the data acquisition module comprises: a surgical data acquisition unit and a brain function monitoring data acquisition unit;
The operation data acquisition unit is used for acquiring operation types, operation positions and anesthesia modes of patients in operation;
the brain function monitoring data acquisition unit is used for acquiring brain electrical function indexes, brain blood flow indexes and physiological indexes in anesthesia of the patient in operation based on a multi-mode brain electrical monitoring technology.
3. The anesthesia management system based on novel or multi-modal electroencephalograph according to claim 2 wherein the electroencephalographic function index is measured by an 8-conductor electroencephalographic system and the cerebral blood flow index is measured by a near infrared diffuse light flow meter comprising a flat-plate fiber probe adapted for brain measurement for covering frontal and temporal lobe areas for measuring task induced micro-circulatory blood flow responses of the cerebral cortex.
4. The anesthesia management system based on novel or multimodal electroencephalogram monitoring according to claim 2, characterized in that the physiological index comprises: sex, age, body weight, body temperature, radial artery blood pressure, intraocular pressure, internal carotid venous pressure, heart rate, pulse oxygen saturation, arterial blood PH, blood lactic acid, arterial blood oxygen saturation, arterial blood oxygen partial pressure, arterial blood CO 2 partial pressure, hemoglobin concentration, hematocrit HCT, brain oxygen saturation, BIS value, duration of surgery, length of anesthesia, wake-up time, wake-up quality, preoperative and postoperative early cognitive function scores, infusion volume, urine volume.
5. The anesthesia management system based on novel or multi-modality electroencephalogram monitoring according to claim 2, wherein the data analysis module comprises: the model building unit and the data analysis unit;
the model construction unit is used for constructing an analysis model based on data analysis and feature extraction;
the data analysis unit is used for inputting the operation type, the operation position, the anesthesia mode, the brain electrical function index, the brain blood flow index and the physiological index in anesthesia into the analysis model to obtain anesthesia characteristic parameters.
6. The anesthesia management system based on novel or multi-modality electroencephalogram monitoring according to claim 5, wherein the analytical model comprises: the device comprises a first analysis layer, a second analysis layer, a third analysis layer and a summarizing layer, wherein the first analysis layer is used for analyzing an electroencephalogram function index, a cerebral blood flow index and an in-anesthesia physiological index and acquiring characteristic parameters related to anesthesia depth; the second analysis layer is used for carrying out correlation analysis on the characteristic parameters and obtaining synchronous and asynchronous relations of the characteristic parameters in the activation process of the cerebral cortex; the third analysis layer is used for determining the relevance between physiological indexes in anesthesia; the summarizing layer is used for determining the anesthesia characteristic parameters according to analysis results of the first analysis layer, the second analysis layer and the third analysis layer.
7. The anesthesia management system based on novel or multi-modality electroencephalogram monitoring according to claim 5, wherein the process of constructing the analysis model comprises:
constructing n groups of subjects according to the operation type, the operation position and the anesthesia mode;
Acquiring an intraoperative electroencephalogram function index, a cerebral blood flow index and an anesthetizing physiological index of the subject based on a brain function monitoring technology;
comparing the difference among the brain electrical function index, the brain blood flow index and the physiological index in anesthesia by using student t test and analysis of variance to obtain a sensitivity index;
Classifying and performing correlation analysis on the electroencephalogram function index, the cerebral blood flow index and the physiological index in anesthesia by using a support vector machine, and determining a brain microcirculation mechanism of nerve-blood vessel coupling and synchronous and asynchronous relations of the electroencephalogram function index, the cerebral blood flow index and the physiological index in anesthesia in the activation process of cerebral cortex;
adopting multiple linear regression to analyze the association between physiological indexes in anesthesia to obtain the association of anesthesia indexes;
The analysis model is constructed based on the sensitivity index, the brain microcirculation mechanism of nerve-blood vessel coupling, the brain electrical function index, the brain blood flow index and the synchronous and asynchronous relation of physiological indexes in anesthesia in the cerebral cortex activation process, and the anesthesia index relevance.
8. The anesthesia management system based on novel or multi-modality electroencephalogram monitoring according to claim 1, wherein the anesthesia management module comprises: the device comprises an anesthesia administration determining unit, an anesthesia depth control unit and an anesthesia risk early warning unit;
The anesthesia administration determining unit is used for determining an anesthesia administration dosage based on the analysis result;
the anesthesia depth control unit is used for controlling the anesthesia depth based on the analysis result;
and the anesthesia risk early warning unit is used for early warning of anesthesia risk based on the analysis result.
9. An anesthesia management method based on novel or multi-modal electroencephalogram monitoring for implementing an anesthesia management system based on novel or multi-modal electroencephalogram monitoring as claimed in any one of claims 1-8, the method comprising:
Collecting operation data and brain function monitoring data of a patient in operation;
Analyzing the operation data and the brain function monitoring data to obtain an analysis result;
and performing anesthesia management based on the analysis result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410284046.4A CN118000677A (en) | 2024-03-13 | 2024-03-13 | Anesthesia management system and method based on novel or multi-mode electroencephalogram monitoring |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410284046.4A CN118000677A (en) | 2024-03-13 | 2024-03-13 | Anesthesia management system and method based on novel or multi-mode electroencephalogram monitoring |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118000677A true CN118000677A (en) | 2024-05-10 |
Family
ID=90951969
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410284046.4A Pending CN118000677A (en) | 2024-03-13 | 2024-03-13 | Anesthesia management system and method based on novel or multi-mode electroencephalogram monitoring |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118000677A (en) |
-
2024
- 2024-03-13 CN CN202410284046.4A patent/CN118000677A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Cowen et al. | Assessing pain objectively: the use of physiological markers | |
| Soderstrom et al. | Involvement of sympathetic nerve activity in skin blood flow oscillations in humans | |
| Jospin et al. | Detrended fluctuation analysis of EEG as a measure of depth of anesthesia | |
| US6042548A (en) | Virtual neurological monitor and method | |
| Karunakaran et al. | NIRS measures in pain and analgesia: fundamentals, features, and function | |
| Moraes et al. | Noninvasive intracranial pressure monitoring methods: a critical review | |
| Chowdhury et al. | Deep learning via ECG and PPG signals for prediction of depth of anesthesia | |
| CN111481174A (en) | Anesthesia and consciousness depth monitoring system and method | |
| US20210338092A1 (en) | Portable device for quantitative measurement of tissue autoregulation and neurovascular coupling using eeg, metabolism, and blood flow diagnostics | |
| Perpetuini et al. | Prediction of state anxiety by machine learning applied to photoplethysmography data | |
| Wang et al. | Monitoring the depth of anesthesia through the use of cerebral hemodynamic measurements based on sample entropy algorithm | |
| US20230389855A1 (en) | Non-Invasive Assessment Of Glymphatic Flow And Neurodegeneration From A Wearable Device | |
| US20240130663A1 (en) | Apparatus for and method of measuring intracranial dynamics | |
| Macnab et al. | The evolution of wireless near infrared spectroscopy applications in urology and rationale for clinical use | |
| Stevens et al. | Determining the structure of acute pain responses in vulnerable neonates | |
| Tiba et al. | Novel noninvasive method of cerebrovascular blood volume assessment using brain bioimpedance | |
| TW202133800A (en) | Real-time pain detection and pain management system | |
| CN104138257A (en) | Surgical anesthesia pain perception monitor | |
| CN118000677A (en) | Anesthesia management system and method based on novel or multi-mode electroencephalogram monitoring | |
| Naguib et al. | Changes in near-infrared spectroscopy and the bispectral index during tilt-table examination | |
| CN118000676A (en) | Anesthesia management method based on brain function monitoring | |
| CN106037759B (en) | A kind of brain self-regulation index detection method towards sleep apnea | |
| Bader | Gizmos and gadgets for the neuroscience intensive care unit | |
| Smith | Key monitoring in neuroanesthesia: principles, techniques, and indications | |
| Li et al. | Functional near infrared spectroscopy in the noninvasive assessment of brain death |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination |