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Intra-Operative Behavioral Tasks in Awake Humans Undergoing Deep Brain Stimulation Surgery
Published on: January 6, 2011
Interpretation Challenges of Intraoperative Neurophysiological Monitoring Waveforms: A Technical Note
Byeong Ho Oh1, Hyeong Cheol Moon1, Jae Hoon Woo1
1Department of Neurosurgery, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Cheongju, Korea.
Abstract:
Intraoperative neurophysiological monitoring (IONM) using motor evoked potentials (MEPs) and somatosensory evoked potentials (SEPs) is widely used during neurosurgical procedures to reduce the risk of postoperative neurological deficits. However, interpretation of intraoperative waveforms is often challenging because signal changes do not always clearly meet established alarm criteria. In this technical note, we present representative patterns of normal, uncertain, and abnormal IONM waveforms and discuss interpretative challenges associated with ambiguous recordings. A retrospective descriptive review was performed on seven adult patients who underwent neurosurgical procedures with intraoperative MEP and SEP monitoring. Waveforms were qualitatively categorized based on morphology, amplitude, latency, and reproducibility across repeated stimulations. Normal waveforms demonstrated stable morphology, reproducible amplitudes, and physiologic latency ranges. Abnormal waveforms showed marked signal attenuation or complete loss of reproducible responses. In contrast, uncertain waveforms exhibited partial amplitude reduction, fluctuating morphology, limited reproducibility, or latency values overlapping normal ranges despite qualitative instability. These equivocal patterns could not be reliably classified using conventional binary alarm criteria alone. Our observations suggest that intraoperative MEP and SEP interpretation cannot always be reduced to a binary normal-versus-abnormal framework. Recognition of an intermediate "uncertain" waveform category may improve contextual intraoperative assessment, prompt careful reassessment of reversible factors, and better reflect the dynamic complexity of neuromonitoring.

