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Objective: An increase in intracranial pressure (ICP) is frequently observed in patients with severe traumatic brain injury (TBI). The information derived from the observation of temporal changes in the mean ICP is insufficient for assessment of the compensatory reserve of the injured brain. This assessment can be achieved via continuous morphological analysis of the pulse waveform of the ICP. Methods: Continuous arterial blood pressure (ABP) and ICP recordings from 292 TBI patients were analyzed. The algorithm extracted morphological landmarks (peaks, troughs, and flats) from the ICP. Among the extracted peaks, P1, P2, and P3 were assigned through peak clustering. The performance of the proposed method was validated through a comparison of the algorithm-defined peaks and those manually identified by experienced observers. Results: The proposed algorithm successfully identified the three distinguishing peaks of the ICP with satisfactory accuracy (95.3%, 87.8%, and 87.5% for P1, P2, and P3, respectively), even from minimally filtered raw signals. Conclusion: The algorithm extracted the morphological features from both ABP and ICP recordings with high accuracy. Significance: The ABP and ICP pulse waveforms can be simultaneously analyzed in real time using the proposed algorithm. The morphological features from these signals may aid the continuous care of patients with TBI. [ABSTRACT FROM PUBLISHER]

In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

This study aims to develop maximal voluntary isometric contraction (MVIC) and submaximal voluntary isometric contraction (subMVIC) methods and to assess the reliability of the developed methods for in-bed healthy individuals and patients with subacute stroke. The electromyography (EMG) activities from the lower-limb muscles including the tensor fascia lata (TFL), rectus femoris (RF), tibialis anterior (TA), and gastrocnemius (GC) on both sides were recorded during MVIC and subMVIC using surface EMG sensors in 20 healthy individuals and 20 subacute stroke patients. In inter-trial reliability, both MVIC and subMVIC methods demonstrated excellent reliability for all the measured muscles at baseline and follow-up evaluations in both healthy individuals and stroke patients. In inter-day reliability, MVIC showed good reliability for the TFL and moderate reliability for the RF, TA, and GC, while subMVIC showed good reliability for the TFL, RF, and GC and poor reliability for the TA in healthy individuals. In conclusion, the MVIC and subMVIC methods of EMG activities were feasible in in-bed healthy individuals and patients with subacute stroke. The results can serve as a basis for the clinical evaluation of muscular activities using quantitative EMG signals on the lower-limb muscles in stroke patients with impaired mobility., Competing Interests: Conflict of Interest: The authors have no potential conflicts of interest to disclose., (Copyright © 2024. Korean Society for Neurorehabilitation.)