Neurological Monitoring Overview

Principles

Definition

Neurological monitoring is a methodological approach focused on assessing the physiological function, activity, and integrity of the nervous system through continuous or intermittent measurement of relevant parameters. This process is critical for detecting changes, guiding interventions, and advancing understanding in both clinical and research settings.

Ontological type

Core Techniques

Clinical Indications

Monitoring Parameters

Key developments

Key Figures

Physiologic Bedside Monitoring era

Carl F. Kety and Paul F. Schmidt established the xenon-133 clearance technique to measure cerebral blood flow, providing quantitative bedside perfusion metrics. Horace H. Magoun and Giuseppe Moruzzi, through foundational work on EEG activation and brain function, laid the neurophysiological groundwork for interpreting cerebral activity alongside perfusion monitoring. Joseph G. Ojemann and colleagues advanced intraoperative neurophysiological monitoring, integrating somatosensory evoked potentials as objective intraoperative markers of neural pathway integrity. Collectively, these efforts created durable physiologic baselines and standardized measurement conventions that anchored neurosurgical and neurocritical care during the 1952–1972 era.

Electrophysiology Standardization and Quantification era

Jeffrey L. Saver [1] is a leading figure in the Electrophysiology Standardization era, with affiliations at Harvard University [3] and the University of California, San Francisco [4], shaping early consensus around clinical EEG interpretation. His contribution centers on quantifying the brain's time-to-event in EEG monitoring, illustrated by Time Is Brain—Quantified [7], a key driver of real-time analytic workflows and patient-specific interpretation in this era. Anantha P. Chandrakasan [2] is associated with the Massachusetts Institute of Technology [5] and Stanford University [6], reflecting a cross-institutional influence on embedded EEG analytics. His work on A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System [8], from 2010, demonstrates the move toward low-power, on-chip feature extraction that enables scalable seizure monitoring and closed-loop potential.

Automated Multimodal Standardization era

Jacqueline A. French[1], with affiliations at Harvard University[3] and Stanford University[4], represents leadership in neuromonitoring during the Automated Multimodal Standardization era. Her key contributions in this era include advancing operational classification of seizure types through the ILAE Position Paper on Classification and Terminology[7], which provided a standardized framework essential for interoperable multicenter data integration and automated decision support. Ingrid E. Scheffer[2], with Northwestern University[5] and University of Washington[6], exemplifies the broader move toward standardized, data-driven neuromonitoring in the era. Her key contributions include co-contributing to the same ILAE seizure classification position paper[7], providing the standardized terminology that underpins reproducible multimodal monitoring, ML classifiers, and quantitative biomarkers across centers.