Safe surgical corridor for iliosacral screw placement in unstable pelvic fractures: a computed-tomography-guided validation study of the "triangulation method".

BACKGROUND: The percutaneous iliosacral screw technique represents a global standard fixation method for unstable fractures of the posterior pelvic ring. However, the inaccurate positioning of iliosacral screws is associated with a significant risk of severe intra-operative complications. Therefore, this study aimed to investigate the relationship between the skin entry point of the transverse iliosacral screw of the first sacral vertebral body and the anterior superior iliac spine and the greater trochanter of the femur using computed-tomography-guided validation. METHODS: Overall, 91 consecutive patients admitted to a tertiary referral center in China for posterior pelvic ring fixation via the "triangulation method" using computed-tomography-guided validation between January 1, 2020, and December 31, 2020, were included in this retrospective observational cohort study. Modeling and simulated iliosacral screw placement were performed using the Mimics software. The distance between the three points of interest was measured, and their relationship in a rectangular coordinate system was determined. Patients were categorized according to gender, body mass index, and femoral rotation angle to investigate the factors affecting the positional relationship between the three points. RESULTS: An equilateral triangular relationship was observed between the positioning points of the transverse iliosacral screw, anterior iliac spine, and greater trochanter. Additionally, 95% of the entry points were within a circle radius centered 12 mm at the apex of an equilateral triangle comprising the anterior superior iliac spine and the greater trochanter as the base. The entry point in the femoral external rotation was more dorsal than that in the internal femoral rotation. Furthermore, the entry point in females was more rostral than that in males, and the entry point in overweight patients was more dorsal than that in normal-weight patients. CONCLUSIONS: The skin entry point of the percutaneous iliosacral screw can be located by drawing an equilateral triangle from the anterior superior iliac spine and the greater trochanter as the base to the dorsum end of the patient's head. In summary, this retrospective cohort study validated the safety and efficacy of the "triangulation methods" for percutaneous fixation of unstable posterior pelvic ring injuries.

Automatic Epileptic Tissue Localization through Spatial Pattern Clustering of High Frequency Activity.

High-frequency activity (HFA) in intracranial electroencephalography recordings are diagnostic biomarkers for refractory epilepsy. Clinical utilities based on HFA have been extensively examined. HFA often exhibits different spatial patterns corresponding to specific states of neural activation, which will potentially improve epileptic tissue localization. However, research on quantitative measurement and separation of such patterns is still lacking. In this paper, spatial pattern clustering of HFA (SPC-HFA) is developed. The process is composed of three steps: (1) feature extraction: skewness which quantifies the intensity of HFA is extracted; (2) clustering: k-means clustering is applied to separate column vectors within the feature matrix into intrinsic spatial patterns; (3) localization: the determination of epileptic tissue is performed based on the cluster centroid with HFA expanding to the largest spatial extent. Experiments were conducted on a public iEEG dataset with 20 patients. Compared with existing localization methods, SPC-HFA demonstrates improvement (Cohen's d > 0.2) and ranks top in 10 out of 20 patients in terms of the area under the curve. In addition, after extending SPC-HFA to high-frequency oscillation detection algorithms, corresponding localization results also improve with effect size Cohen's d ≥ 0.48. Therefore, SPC-HFA can be utilized to guide clinical and surgical treatment of refractory epilepsy.

BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System.

Stable and accurate electroencephalogram (EEG) signal acquisition is fundamental in non-invasive brain-computer interface (BCI) technology. Commonly used EEG acquisition systems' hardware and software are usually closed-source. Its inability to flexible expansion and secondary development is a major obstacle to real-time BCI research. This paper presents the Beijing University of Posts and Telecommunications EEG Acquisition Tool System named BEATS. It implements a comprehensive system from hardware to software, composed of the analog front end, microprocessor, and software platform. BEATS is capable of collecting 32-channel EEG signals at a guaranteed sampling rate of 4 kHz with wireless transmission. Compared to state-of-the-art systems used in many EEG fields, it displays a better sampling rate. Using techniques including direct memory access, first in first out, and timer, the precision and stability of the acquisition are ensured at the microsecond level. An evaluation is conducted during 24 hours of continuous acquisitions. There are no packet losses and the average maximum delay is only 0.07 s/h. Moreover, as an open-source system, BEATS provides detailed design files, and adopts a plug-in structure and easy-to-access materials, which makes it can be quickly reproduced. Schematics, source code, and other materials of BEATS are available at https://github.com/buptantEEG/BEATS.

EEG-based Driving Fatigue Detection during Operating the Steering Wheel Data Section.

Accurate and reliable detecting of driving fatigue using Electroencephalography (EEG) signals is a method to reduce traffic accidents. So far, it is natural to cut the part of operating the steering wheel data away for achieving the relatively high accuracy in detecting driving fatigue using EEG data. However, the data segment during operating the steering wheel also contains valuable information. Moreover, operating the steering wheel is a common practice during actual driving. In this study, we utilize the part of data operating the steering wheel to detecting fatigue. The feature used is the spectral band power calculates from the data. For each experiment and each experimental participant, the data and features are divided into sessions and subjects. Using the divided features, this work performs cross-session and cross-subject verification and comparison on the two classification methods of logistic regression and multi-layer perceptron. To compare the effect, the experiment is conducted on the data both operating the steering wheel and not operating the steering wheel. The result shows that the bias between the average accuracy of two types of data is only 2.27%, and the effect of using multi-layer perceptron is 10.37% better than using logistic regression. This proves that the data segment during operating the steering wheel also contains valid information and can be used for driving fatigue detection.

Tensor-Based EEG Network Formation and Feature Extraction for Cross-Session Driving Drowsiness Detection.

Drowsy driving is one of the major causes in traffic accidents worldwide. Various electroencephalography (EEG)-based feature extraction methods are proposed to detect driving drowsiness, to name a few, spectral power features and fuzzy entropy features. However, most existing studies only concentrate on features in each channel separately to identify drowsiness, making them vulnerable to variability across different sessions and subjects without sufficient data. In this paper, we propose a method called Tensor Network Features (TNF) to exploit underlying structure of drowsiness patterns and extract features based on tensor network. This TNF method first introduces Tucker decomposition to tensorized EEG channel data of training set, then features of training and testing tensor samples are extracted from the corresponding subspace matrices through tensor network summation. The performance of the proposed TNF method was evaluated through a recently published EEG dataset during a sustained-attention driving task. Compared with spectral power features and fuzzy entropy features, the accuracy of TNF method is improved by 6.7% and 10.3% on average with maximum value 17.3% and 29.7% respectively, which is promising in developing practical and robust cross-session driving drowsiness detection system.