Development and Characterization of Novel Conductive Sensing Fibers for In Vivo Nerve Stimulation.

Advancements in electrode technologies to both stimulate and record the central nervous system's electrical activities are enabling significant improvements in both the understanding and treatment of different neurological diseases. However, the current neural recording and stimulating electrodes are metallic, requiring invasive and damaging methods to interface with neural tissue. These electrodes may also degrade, resulting in additional invasive procedures. Furthermore, metal electrodes may cause nerve damage due to their inherent rigidity. This paper demonstrates that novel electrically conductive organic fibers (ECFs) can be used for direct nerve stimulation. The ECFs were prepared using a standard polyester material as the structural base, with a carbon nanotube ink applied to the surface as the electrical conductor. We report on three experiments: the first one to characterize the conductive properties of the ECFs; the second one to investigate the fiber cytotoxic properties in vitro; and the third one to demonstrate the utility of the ECF for direct nerve stimulation in an in vivo rodent model.

Neurologic Complications in Monitored versus Unmonitored Image-Guidance Assisted Posterior Lumbar Instrumentation.

BACKGROUND: Intraoperative neurophysiologic monitoring (IOM) has been used clinically since the 1970s and is a reliable tool for detecting impending neurologic compromise. However, there are mixed data as to whether long-term neurologic outcomes are improved with its use. We investigated whether IOM used in conjunction with image guidance produces different patient outcomes than with image guidance alone. METHODS: We reviewed 163 consecutive cases between January 2015 and December 2018 and compared patients undergoing posterior lumbar instrumentation with image guidance using and not using multimodal IOM. Monitored and unmonitored surgeries were performed by the same surgeons, ruling out variability in intersurgeon technique. Surgical and neurologic complication rates were compared between these 2 cohorts. RESULTS: A total of 163 patients were selected (110 in the nonmonitored cohort vs. 53 in the IOM cohort). Nineteen signal changes were noted. Only 3 of the 19 patients with signal changes had associated neurologic deficits postoperatively (positive predictive value 15.7%). There were 5 neurologic deficits that were observed in the nonmonitored cohort and 8 deficits observed in the monitored cohort. Transient neurologic deficit was significantly higher in the monitored cohort per case (P < 0.0198) and per screw (P < 0.0238); however, there was no difference observed between the 2 cohorts when considering permanent neurologic morbidity per case (P < 0.441) and per screw (P < 0.459). CONCLUSIONS: The addition of IOM to cases using image guidance does not appear to decrease long-term postoperative neurologic morbidity and may have a reduced diagnostic role given availability of intraoperative image-guidance systems.

A Deep Learning Model for Automated Classification of Intraoperative Continuous EMG.

OBJECTIVE: Intraoperative neurophysiological monitoring (IONM) is the use of electrophysiological methods during certain high-risk surgeries to assess the functional integrity of nerves in real time and alert the surgeon to prevent damage. However, the efficiency of IONM in current practice is limited by latency of verbal communications, inter-rater variability, and the subjective manner in which electrophysiological signals are described. METHODS: In an attempt to address these shortcomings, we investigate automated classification of free-running electromyogram (EMG) waveforms during IONM. We propose a hybrid model with a convolutional neural network (CNN) component and a long short-term memory (LSTM) component to better capture complicated EMG patterns under conditions of both electrical noise and movement artifacts. Moreover, a preprocessing pipeline based on data normalization is used to handle classification of data from multiple subjects. To investigate model robustness, we also analyze models under different methods for processing of artifacts. RESULTS: Compared with several benchmark modeling methods, CNN-LSTM performs best in classification, achieving accuracy of 89.54% and sensitivity of 94.23% in cross-patient evaluation. CONCLUSION: The CNN-LSTM model shows promise for automated classification of continuous EMG in IONM. SIGNIFICANCE: This technique has potential to improve surgical safety by reducing cognitive load and inter-rater variability.