Differentiating loss of consciousness causes through artificial intelligence-enabled decoding of functional connectivity.

Differential diagnosis of acute loss of consciousness (LOC) is crucial due to the need for different therapeutic strategies despite similar clinical presentations among etiologies such as nonconvulsive status epilepticus, metabolic encephalopathy, and benzodiazepine intoxication. While altered functional connectivity (FC) plays a pivotal role in the pathophysiology of LOC, there has been a lack of efforts to develop differential diagnosis artificial intelligence (AI) models that feature the distinctive FC change patterns specific to each LOC cause. Three approaches were applied for extracting features for the AI models: three-dimensional FC adjacency matrices, vectorized FC values, and graph theoretical measurements. Deep learning using convolutional neural networks (CNN) and various machine learning algorithms were implemented to compare classification accuracy using electroencephalography (EEG) data with different epoch sizes. The CNN model using FC adjacency matrices achieved the highest accuracy with an AUC of 0.905, with 20-s epoch data being optimal for classifying the different LOC causes. The high accuracy of the CNN model was maintained in a prospective cohort. Key distinguishing features among the LOC causes were found in the delta and theta brain wave bands. This research advances the understanding of LOC's underlying mechanisms and shows promise for enhancing diagnosis and treatment selection. Moreover, the AI models can provide accurate LOC differentiation with a relatively small amount of EEG data in 20-s epochs, which may be clinically useful.

Enhanced Vision-Based Taillight Signal Recognition for Analyzing Forward Vehicle Behavior.

This study develops a vision-based technique for enhancing taillight recognition in autonomous vehicles, aimed at improving real-time decision making by analyzing the driving behaviors of vehicles ahead. The approach utilizes a convolutional 3D neural network (C3D) with feature simplification to classify taillight images into eight distinct states, adapting to various environmental conditions. The problem addressed is the variability in environmental conditions that affect the performance of vision-based systems. Our objective is to improve the accuracy and generalizability of taillight signal recognition under different conditions. The methodology involves using a C3D model to analyze video sequences, capturing both spatial and temporal features. Experimental results demonstrate a significant improvement in the model's accuracy (85.19%) and generalizability, enabling precise interpretation of preceding vehicle maneuvers. The proposed technique effectively enhances autonomous vehicle navigation and safety by ensuring reliable taillight state recognition, with potential for further improvements under nighttime and adverse weather conditions. Additionally, the system reduces latency in signal processing, ensuring faster and more reliable decision making directly on the edge devices installed within the vehicles.

Two-Level Blockchain System for Digital Crime Evidence Management.

Digital evidence, such as evidence from CCTV and event data recorders, is highly valuable in criminal investigations, and is used as definitive evidence in trials. However, there are risks when digital evidence obtained during the investigation of a case is managed through a physical hard disk drive until it is submitted to the court. Previous studies have focused on the integrated management of digital evidence in a centralized system, but if a centralized system server is attacked, major operations and investigation information may be leaked. Therefore, there is a need to reliably manage digital evidence and investigation information using blockchain technology in a distributed system environment. However, when large amounts of data-such as evidence videos-are stored in a blockchain, the data that must be processed only within one block before being created increase, causing performance degradation. Therefore, we propose a two-level blockchain system that separates digital evidence into hot and cold blockchains. In the criminal investigation process, information that frequently changes is stored in the hot blockchain, and unchanging data such as videos are stored in the cold blockchain. To evaluate the system, we measured the storage and inquiry processing performance of digital crime evidence videos according to the different capacities in the two-level blockchain system.

Hemodynamic Instability and Cardiovascular Events After Traumatic Brain Injury Predict Outcome After Artifact Removal With Deep Belief Network Analysis.

BACKGROUND: Hemodynamic instability and cardiovascular events heavily affect the prognosis of traumatic brain injury. Physiological signals are monitored to detect these events. However, the signals are often riddled with faulty readings, which jeopardize the reliability of the clinical parameters obtained from the signals. A machine-learning model for the elimination of artifactual events shows promising results for improving signal quality. However, the actual impact of the improvements on the performance of the clinical parameters after the elimination of the artifacts is not well studied. MATERIALS AND METHODS: The arterial blood pressure of 99 subjects with traumatic brain injury was continuously measured for 5 consecutive days, beginning on the day of admission. The machine-learning deep belief network was constructed to automatically identify and remove false incidences of hypotension, hypertension, bradycardia, tachycardia, and alterations in cerebral perfusion pressure (CPP). RESULTS: The prevalences of hypotension and tachycardia were significantly reduced by 47.5% and 13.1%, respectively, after suppressing false incidents (P=0.01). Hypotension was particularly effective at predicting outcome favorability and mortality after artifact elimination (P=0.015 and 0.027, respectively). In addition, increased CPP was also statistically significant in predicting outcomes (P=0.02). CONCLUSIONS: The prevalence of false incidents due to signal artifacts can be significantly reduced using machine-learning. Some clinical events, such as hypotension and alterations in CPP, gain particularly high predictive capacity for patient outcomes after artifacts are eliminated from physiological signals.

Novel index for predicting mortality during the first 24 hours after traumatic brain injury.

OBJECTIVE: Failure of cerebral autoregulation and subsequent hypoperfusion is common during the acute phase of traumatic brain injury (TBI). The cerebrovascular pressure-reactivity index (PRx) indirectly reflects cerebral autoregulation and has been used to derive optimal cerebral perfusion pressure (CPP). This study provides a method for the use of a combination of PRx, CPP, and intracranial pressure (ICP) to better evaluate the extent of cerebral hypoperfusion during the first 24 hours after TBI, allowing for a more accurate prediction of mortality risk. METHODS: Continuous ICP and arterial blood pressure (ABP) signals acquired from 295 TBI patients during the first 24 hours after admission were retrospectively analyzed. The CPP at the lowest PRx was determined as the optimal CPP (CPPopt). The duration of a severe hypoperfusion event (dHP) was defined as the cumulative time that the PRx was > 0.2 and the CPP was < 70 mm Hg with the addition of intracranial hypertension (ICP > 20 or > 22 mm Hg). The outcome was determined as 6-month mortality. RESULTS: The cumulative duration of PRx > 0.2 and CPP < 70 mm Hg exhibited a significant association with mortality (p < 0.001). When utilized with basic clinical information available during the first 24 hours after admission (i.e., Glasgow Coma Scale score, age, and mean ICP), a dHP > 25 minutes yielded a significant predictive capacity for mortality (p < 0.05, area under the curve [AUC] = 0.75). The parameter was particularly predictive of mortality for patients with a mean ICP > 20 or > 22 mm Hg (AUC = 0.81 and 0.87, respectively). CONCLUSIONS: A short duration (25 minutes) of severe hypoperfusion, evaluated as lowered CPP during worsened cerebrovascular reactivity during the 1st day after TBI, is highly indicative of mortality.

Spectral analysis of intracranial pressure: Is it helpful in the assessment of shunt functioning in-vivo?

OBJECTIVE: Shunt failure is common in hydrocephalic patients. The cerebrospinal fluid (CSF) infusion test enables the assessment of CSF absorption capacity, which is represented by the resistance to CSF outflow (ROUT) However, shunt failure may not only affect the CSF absorption capacity but also the intracranial compliance or compensatory properties. Spectral analysis of the ICP signal obtained during the infusion test may enable the comprehensive assessment of the overall deterioration caused by shunt failure. MATERIAL AND METHODS: A total of 121 hydrocephalic shunted patients underwent the infusion test with continuous intracranial pressure (ICP) and arterial blood pressure (ABP) recording. The maximum amplitudes of three major frequency bandwidths (0.2-2.6, 2.6-4.0 and 4.0-15 Hz, respectively) were calculated from the ICP. Statistical analyses were conducted to identify factors significantly associated with shunt failure, to construct an index (i.e., the shunt response parameter, SRP) for detecting shunt failure, and to define thresholds for ROUT and SRP. RESULTS: The ROUT threshold for detecting shunt failure was 7.59 mmHg/ml/min, and this threshold showed an accuracy of 82.64%. All spectral parameters were found to be significantly associated with shunt patency (p<0.05). The SRP exhibited significantly better accuracy than ROUT in detecting shunt failure (91.74%). CONCLUSION: The hydrodynamic assessment of shunted patients enhanced by spectral analysis during the infusion test detected shunt failure with high accuracy. Although further validation is needed, the SRP exhibited promising results.