Multi-Modal EEG Monitoring of Severely Neurologically Ill Patients

Contents
Contributors
List of Abbreviations
Part I: Electroencephalogram (EEG) Basis and Quantitative Electroencephalogram (qEEG) Technology
	1: Basic Theory of EEG
		1.1	 Formation of EEG
		1.2	 Mechanism of the Formation of an EEG
		1.3	 EEG Distribution, Waveform, and Amplitude
			1.3.1	 Frequency
			1.3.2	 Amplitude
			1.3.3	 Waveform
			1.3.4	 Phase Relation
			1.3.5	 Mode of Occurrence of Abnormal Waves
			1.3.6	 Distribution and Breadth
			1.3.7	 Reactivity
		1.4	 Common EEG Recording Methods
			1.4.1	 Monopolar Lead (Fig. 1.1)
			1.4.2	 Bipolar Lead (Fig. 1.2)
			1.4.3	 The Parameters to Check
		1.5	 Interpretation of the EEG of Healthy Adults
		1.6	 Recognition of Common Artifacts in the EEGs of Patients in Severe Condition
			1.6.1	 Physiological Artifacts
			1.6.2	 Artifacts from Instruments and Electrodes
			1.6.3	 Artifacts from Environmental Electromagnetic Interference
		References
	2: qEEG Monitoring System in Severely Ill Patients
		2.1	 Basic Concepts of qEEG
		2.2	 Trend Chart Commonly Used in qEEG
			2.2.1	 aEEG
			2.2.2	 RBP
			2.2.3	 RAV
			2.2.4	 Spectral Entropy
			2.2.5	 Other Trend Graphs
		2.3	 Common Artifacts and Recognition of qEEG
			2.3.1	 Physiological Artifacts
			2.3.2	 Nonphysiological Artifacts
		References
Part II: Interpretation and Clinical Significance of Abnormal EEG in Severely Ill Patients
	3: Common Abnormal EEG in Neurocritical Ill Patients
		3.1	 Common Abnormal Discharge Patterns in Severely Ill Patients
			3.1.1	 Generalized or Diffuse Discharges (Figs. 3.1, 3.2, 3.3, 3.4, and 3.5)
				3.1.1.1	 Generalized Epileptiform Discharges (GEDs)
				3.1.1.2	 Diffuse Discharge (Fig. 3.6)
			3.1.2	 Unilateral Discharges (Figs. 3.7 and 3.8)
			3.1.3	 Multifocal Discharges (Figs. 3.9 and 3.10)
		3.2	 PDs
			3.2.1	 The Discovery and Historical Evolution of PDs
			3.2.2	 The Classification of PDs
				3.2.2.1	 LPDs
				3.2.2.2	 BIPDs
				3.2.2.3	 GPDs
				3.2.2.4	 Others
			3.2.3	 Prevalence
			3.2.4	 Clinical Characteristics of PDs
				3.2.4.1	 LPDs
				3.2.4.2	 GPDs
				3.2.4.3	 BIPDs
				3.2.4.4	 TWs
				3.2.4.5	 SIRPIDs
				3.2.4.6	 Case
		3.3	 IIC in cEEG Monitoring
			3.3.1	 Background
				3.3.1.1	 IIC Definition
				3.3.1.2	 Patterns Along the IIC
			3.3.2	 Risks of Seizure and Prognosis Related to Patterns Along the IIC
				3.3.2.1	 LPDs
				3.3.2.2	 GPDs
				3.3.2.3	 BIPDs
				3.3.2.4	 RDA
				3.3.2.5	 SIRPIDs
				3.3.2.6	 B(I)RDs
			3.3.3	 Treatment Algorithm
				3.3.3.1	 Use of AEDs
				3.3.3.2	 Multimodal Monitoring
			3.3.4	 Conclusion
		3.4	 Abnormal Slow-Wave Activity
			3.4.1	 PNDA
			3.4.2	 IRDA
			3.4.3	 PHDA
		3.5	 α Coma and β Coma
			3.5.1	 Introduction
			3.5.2	 Historical Evolution
			3.5.3	 Concepts
				3.5.3.1	 α Coma
				3.5.3.2	 β Coma
			3.5.4	 The Clinical Significance and Related Pathological Mechanisms of α Coma and β Coma
				3.5.4.1	 α Coma
				3.5.4.2	 β Coma
				3.5.4.3	 Cases
		3.6	 BS Pattern in EEG
			3.6.1	 The Historical Evolution of the BS Pattern in EEGs
			3.6.2	 The Possible Mechanism of BS Patterns
			3.6.3	 The BS Pattern in Different Diseases
				3.6.3.1	 The BS Pattern in Epileptic Encephalopathy
				3.6.3.2	 The BS Pattern in Hypoxic Brain Injury
				3.6.3.3	 The Effect of Anesthetics on BS Patterns
				3.6.3.4	 The BS Pattern and Postanesthesia Delirium
				3.6.3.5	 The BS Pattern in Brain Injury
				3.6.3.6	 The BS Pattern in the Dying State
				3.6.3.7	 The BS Pattern in Metabolic Encephalopathy
			3.6.4	 Advances in BS Pattern Research
				3.6.4.1	 The Relationship Between EEG Power Spectral Density and the BS Pattern with Propofol
				3.6.4.2	 The Use of FDG-PET Combined with the BS Pattern to Predict the Effect of Drug Treatment
		3.7	 Nonreactive Low-Voltage Slow Activity and ECS
		References
	4: Abnormal EEG Background Activity
		4.1	 EEG Symmetry
			4.1.1	 The Clinical Significance of Mild/Significant Amplitude Asymmetry
			4.1.2	 Gap Effect
			4.1.3	 The Clinical Significance of the Front-Back Gradient and Front-Back Inverse Gradient
		4.2	 EEG Continuity
			4.2.1	 Definition of EEG Continuity
			4.2.2	 Near-Continuous and Discontinuous EEG Patterns
			4.2.3	 Discontinuous EEG of BS Patterns (See the Sect. 3.6 for Details)
			4.2.4	 Suppression-State Background Mode
		4.3	 EEG-R
			4.3.1	 The Definition of EEG-R
			4.3.2	 The Prognostic Value of EEG-R for Adult Patients
			4.3.3	 The Prognostic Value of EEG-R in Children
			4.3.4	 It Is Feasible to Study EEG-R Using Quantitative Methods?
			4.3.5	 The Shortcomings of the Use of EEG-R to Judge Prognosis
		References
	5: Patterns and Clinical Significance of Abnormal Sleep EEG
		5.1	 Sleep Structure and Sleep Cycle
		5.2	 Atypical Sleep Types
		5.3	 Sleep Apnea
		5.4	 Application of Abnormal Sleep in Brain Function Damage and Prognosis Judgment of Severely Ill Patients
		References
Part III: Application of Multimodal EEG in Severely Ill Patients
	6: Application of Multimodal EEG in Coma Patients
		6.1	 Methods to Evaluate Brain Function in Patients with Impaired Consciousness and the Significance of Multimodal EEG Evaluation
		6.2	 The Application of Multimodal EEG in Patients with Consciousness Disorder
			6.2.1	 Common Abnormal EEG Background Activity in Coma Patients
			6.2.2	 The Role of EEG-R in the Evaluation of Brain Function and the Prognosis of Patients with Severe Coma
			6.2.3	 The Role of Abnormal Sleep Waveforms in the Brain Function and Prognosis of Patients with Severe Coma
			6.2.4	 The Application of EPs in the Judgment of Brain Function and the Prognosis of Patients with Severe Coma
			6.2.5	 aEEG
			6.2.6	 BIS
		References
	7: Application of Multimodal EEG in HIE
		7.1	 Clinical Features of HIE
		7.2	 The Pathogenesis of HIE
		7.3	 The Prognosis of HIE
		7.4	 Common Brain Function Assessment Methods in HIE
		7.5	 Multidimensional EEG Predicts the Prognosis of Patients with HIE
			7.5.1	 Common EEG Manifestations of HIE After CPR
			7.5.2	 EEG-R
			7.5.3	 EEG Sleep Spindles
		7.6	 The Role of Multimode EEG in the Diagnosis, Treatment, and Prognosis of HIE
			7.6.1	 Application of aEEG in HIE
			7.6.2	 The Application of the RAV Coefficient in HIE
			7.6.3	 The Application of the BIS in HIE
		References
	8: Application of Multimodal EEG in SE
		8.1	 Definition of SE
		8.2	 Classification of SE
		8.3	 Application of Multimodal EEG in SE
			8.3.1	 Indications for EEG
			8.3.2	 Manifestations of SE on EEG
				8.3.2.1	 Traditional EEG
					Spike Waves
					Sharp Waves
					Multispike Waves
					Spike Slow Waves
					Sharp Slow Waves
					Nonspecific Epileptiform Discharges
						PDs
						GPDs
						BS
						RDA
				8.3.2.2	 aEEG
			8.3.3	 The Role of Multimodal EEG in the Diagnosis of SE
				8.3.3.1	 EEG Monitoring Indications
					Start Time of EEG Monitoring
					Monitoring Duration
					The Positive Rate of Monitoring
				8.3.3.2	 Standards for the Diagnosis of SE by EEG
				8.3.3.3	 The Role of Conventional EEG in the Diagnosis of NCSE
					EEG Diagnostic Criteria for NCSE
					Clinical Practice
				8.3.3.4	 The Role of Conventional EEG in the Diagnosis of CSE
				8.3.3.5	 The Role of aEEG in the Diagnosis of SE
					The Value of aEEG in the Diagnosis of SE
					The Performance of SE on aEEG
					Clinical Practice
				8.3.3.6	 The Role of Sleep EEG in the Diagnosis of SE
			8.3.4	 The Role of Multimodal EEG in the Treatment of SE
				8.3.4.1	 Treatment Goals
				8.3.4.2	 Efficacy Criterion
				8.3.4.3	 The Role of aEEG in the Treatment of SE
			8.3.5	 The Role of Multimodal EEG in Judging the Prognosis of SE
				8.3.5.1	 The Use of aEEG to Judge the Prognosis of Patients with SE
					The Role of aEEG in Judging Prognosis
					Clinical Practice
				8.3.5.2	 The Role of BIS in Judging the Prognosis of Patients with SE
				8.3.5.3	 Video-EEG to Judge the Prognosis of Patients with SE
		8.4	 New Developments in Multimodal EEG
			8.4.1	 Improvement of EEG Electrodes
			8.4.2	 Predicting the Recurrence of RSE After the Discontinuation of Anesthetics
		8.5	 Discussion
		References
	9: Clinical Application of Multimodal EEG in Acute Ischemic Stroke
		9.1	 Pathophysiological Basis of EEG Changes in Ischemic Stroke
		9.2	 EEG Manifestations of Ischemic Stroke
		9.3	 qEEG Changes in Acute Ischemic Stroke
		References
	10: Application of Multimodal EEG in TBI
		10.1	 Overview
		10.2	 Mechanisms of EEG Changes Related to the Pathophysiology of TBI
			10.2.1	 Cortical Spreading Depolarization (CSD) or Cortical Spreading Depression (CSD)
				10.2.1.1	 Discovery and Definition
				10.2.1.2	 The Relationship Between SD and Pathophysiological Changes in TBI
			10.2.2	 IEFPs and Spreading Convulsions (SCs)
				10.2.2.1	 Discovery and Definition
				10.2.2.2	 The Relationship Between the Pathophysiology of TBI and IEFPs and SCs
				10.2.2.3	 The Relationship Between IEFPs and SD
			10.2.3	 Traumatic Axonal Injury and Neural Network
		10.3	 Common EEG Manifestations and Clinical Significance of TBI
			10.3.1	 Abnormal Background Activity
				10.3.1.1	 The Diversity of EEG Background Patterns
				10.3.1.2	 The Main Frequency of EEG and TBI
				10.3.1.3	 The Sleep Structure of EEGs and TBI
				10.3.1.4	 The Variability and Reactivity of EEGs and TBI
				10.3.1.5	 The Continuity of EEG Background Activities and TBI
			10.3.2	 Abnormal Paroxysmal Activity
				10.3.2.1	 SD Wave Monitoring and Clinical Significance
					Monitoring Methods
					SD Model and Clinical Significance
				10.3.2.2	 IEEs and IIC
					IEEs
					IIC
		References
	11: Application of Multimodal EEG in AE
		11.1	 Overview
		11.2	 Epidemiology of AE and Related Inspection Methods
		11.3	 EEG Features of AE
			11.3.1	 Slow Waves
			11.3.2	 Epilepsy Waves
			11.3.3	 EBA
			11.3.4	 EDB Wave
		11.4	 The Clinical Value of EEG in AE
			11.4.1	 Role in Early Diagnosis
			11.4.2	 Effect on Epilepsy and SE
			11.4.3	 Role in Prognostic Evaluation
		References
	12: Application of Multimodal EEG in ICP Monitoring
		12.1	 Common Methods and Importance of ICP Monitoring
		12.2	 Effects of Increased ICP on EEGs
		12.3	 Common EEG Changes Due to Intracranial Hypertension
		12.4	 EEG Manifestations of Ischemia-Reperfusion Injury
		12.5	 The Clinical Application Value of Multimodal Monitoring in Patients with Intracranial Hypertension
		References
	13: Application of Multimodal EEG in Sedation and Analgesia
		13.1	 The Effect of Sedative Drugs on EEG
			13.1.1	 Benzodiazepines
			13.1.2	 Propofol
			13.1.3	 Ketamine
			13.1.4	 Dexmedetomidine
		13.2	 The Role of Multimodal EEG Monitoring to Evaluate the Sedative Effect
			13.2.1	 The Role of BIS Monitoring in Evaluating Sedation and Analgesia
			13.2.2	 The Role of aEEG in Evaluating Sedation and Analgesia
			13.2.3	 95% SEF
			13.2.4	 Entropy Index
			13.2.5	 RBP
		13.3	 Multimodal EEG and Different Sedative Drugs
		13.4	 Multimodal EEG Manifestations of Excessive Sedation
		References
	14: Application of Multimodal EEG in Predicting the Risk of Suicide
		14.1	 The Neurophysiological Mechanism of EEG Changes in Suicidal Patients
		14.2	 The Role of PSG in Predicting the Risk of Suicide in Patients
			14.2.1	 The Neurobiological Mechanism of Suicide
			14.2.2	 Progress in Research on Suicide Risk and Objective Sleep Monitoring
				14.2.2.1	 Suicide Risk and Changes in Macroscopic Sleep Structure
					Prolonged Sleep Latency
					Increased REM Sleep
					Sleep Reduction in Stage 4 of NREM
					Lower Sleep Efficiency
				14.2.2.2	 Suicide Risk and Changes in Microscopic Sleep Structure
				14.2.2.3	 The Correlation Between Suicide Risk and OSAS
			14.2.3	 The Role of Objective Sleep Monitoring Indicators in Determining the Early Warning Signs of and Guiding Interventions for Suicide Risk
		14.3	 Common Multimodal EEG Manifestations in Suicide Patients
			14.3.1	 Background
			14.3.2	 EEG Manifestations
			14.3.3	 EEG Power Spectrum Manifestations
			14.3.4	 ERP Manifestations
			14.3.5	 Summary
		14.4	 Research Progress on the Use of Multimodal EEG in Predicting Suicide
			14.4.1	 Background
			14.4.2	 Multiple Cognitive Deficits Are Associated with Suicide
				14.4.2.1	 Defects in Decision-Making Functions
				14.4.2.2	 Insufficient Anti-interference Ability
				14.4.2.3	 Deficits in Cognitive Control
				14.4.2.4	 Abnormality in the Expectation and Initial Reaction to Rewards
			14.4.3	 EEG Abnormalities Related to Suicide
				14.4.3.1	 Changes in EEG Power Spectra
				14.4.3.2	 Abnormal Spatial Distribution of EEG Frequency
			14.4.4	 Suicide-Related Brain Structural and Functional Changes
				14.4.4.1	 Various Brain Structural and Functional Abnormalities
				14.4.4.2	 Machine Learning-Assisted Imaging Diagnosis of Suicide
			14.4.5	 Summary and Outlook
		References
	15: Application of Multimodal EEG in the Determination of Brain Death
		15.1	 Prerequisites for Judgment
			15.1.1	 The Cause of the Coma Is Clear
			15.1.2	 The Exclusion of Various Causes of Reversible Coma
		15.2	 Clinical Judgment
			15.2.1	 Deep Coma
			15.2.2	 All Brainstem Reflexes Disappeared [8]
			15.2.3	 No Spontaneous Breathing
		15.3	 Confirmation Tests
			15.3.1	 EEG
			15.3.2	 SLSEP Monitoring
			15.3.3	 TCD Monitoring
			15.3.4	 Order of Confirmation Tests
		15.4	 Judgment Steps
		15.5	 Judgment Times
		References
	16: Application of the BIS in the ICU
		16.1	 Principle of BIS Monitoring
		16.2	 Factors Influencing the BIS
		16.3	 Correlation Between the BIS and Clinical Anesthetics
		16.4	 Clinical Application of the BIS
			16.4.1	 Monitoring the Depth of Sedation
			16.4.2	 Analgesia
			16.4.3	 The Use of BIS Monitoring to Evaluate the Degree and Prognosis of Brain Injury
		References
	17: Application of aEEG in Severely Ill Patients
		17.1	 Overview
		17.2	 aEEG
			17.2.1	 Signal Processing of aEEG
			17.2.2	 Monitoring Channels of aEEG
			17.2.3	 Analysis Indexes of aEEGs, the Classification of aEEG Patterns, and Criteria for the Classification of aEEGs
				17.2.3.1	 Analysis Indexes of aEEGs
				17.2.3.2	 Classification of aEEG Patterns
				17.2.3.3	 Criteria for the Classification of aEEGs [6]
		17.3	 Application of aEEG in PICUs
			17.3.1	 HIE
			17.3.2	 Brain Injury in Premature Infants
			17.3.3	 Neonatal Convulsions
			17.3.4	 Epilepsy
			17.3.5	 Others
		17.4	 Application of aEEG in Adult ICUs
			17.4.1	 Prognosis Assessment of Coma Patients After Cardiac Arrest
			17.4.2	 Application in Epilepsy Patients
			17.4.3	 Prognosis Assessment of Anti-NMDAR Encephalitis
		References