A Systematic Review of Neurophysiology-Based Localization Techniques Used in Deep Brain Stimulation Surgery of the Subthalamic Nucleus.

OBJECTIVE: This systematic review is conducted to identify, compare, and analyze neurophysiological feature selection, extraction, and classification to provide a comprehensive reference on neurophysiology-based subthalamic nucleus (STN) localization. MATERIALS AND METHODS: The review was carried out using the methods and guidelines of the Kitchenham systematic review and provides an in-depth analysis on methods proposed on STN localization discussed in the literature between 2000 and 2021. Three research questions were formulated, and 115 publications were identified to answer the questions. RESULTS: The three research questions formulated are answered using the literature found on the respective topics. This review discussed the technologies used in past research, and the performance of the state-of-the-art techniques is also reviewed. CONCLUSION: This systematic review provides a comprehensive reference on neurophysiology-based STN localization by reviewing the research questions other new researchers may also have.

Fully automated estimation of the mean linear intercept in histopathology images of mouse lung tissue.

Purpose: The mean linear intercept (MLI) score is a common metric for quantification of injury in lung histopathology images. The automated estimation of the MLI score is a challenging task because it requires accurate segmentation of different biological components of the lung tissue. Therefore, the most widely used approaches for MLI quantification are based on manual/semi-automated assessment of lung histopathology images, which can be expensive and time-consuming. We describe a fully automated pipeline for MLI estimation, which is capable of producing results comparable to human raters. Approach: We use a convolutional neural network based on U-Net architecture to segment the diagnostically relevant tissue segments in the whole slide images (WSI) of the mouse lung tissue. The proposed method extracts multiple field-of-view (FOV) images from the tissue segments and screen the FOV images, rejecting images based on presence of certain biological structures (i.e., blood vessels and bronchi). We used color slicing and region growing for segmentation of different biological structures in each FOV image. Results: The proposed method was tested on ten WSIs from mice and compared against the scores provided by three human raters. In segmenting the relevant tissue segments, our method obtained a mean accuracy, Dice coefficient, and Hausdorff distance of 98.34%, 98.22%, and 109.68 µ m , respectively. Our proposed method yields a mean precision, recall, and F 1 -score of 93.37%, 83.47%, and 87.87%, respectively, in screening of FOV images. There was substantial agreement found between the proposed method and the manual scores (Fleiss Kappa score of 0.76). The mean difference between the calculated MLI score between the automated method and average rater's score was 2.33 ± 4.13 ( 4.25 % ± 5.67 % ). Conclusion: The proposed pipeline for automated calculation of the MLI score demonstrates high consistency and accuracy with human raters and can be a potential replacement for manual/semi-automated approaches in the field.

Decision Tree Learning Algorithm for Classifying Knee Injury Status Using Return-to-Activity Criteria.

Anterior cruciate ligament (ACL) injury rates in female adolescents are increasing. Irrespective of treatment options, approximately 1/3 will suffer secondary ACL injuries following their return to activity (RTA). Despite this, there are no evidence-informed RTA guidelines to aid clinicians in deciding when this should occur. The first step towards these guidelines is to identify relevant and feasible measures to assess the functional status of these patients. The purpose of this study was therefore to evaluate tests frequently used to assess functional capacity following surgery using a Reduced Error Pruning Tree (REPT). Thirty-six healthy and forty-two ACLinjured adolescent females performed a series of functional tasks. Motion analysis along with spatiotemporal measures were used to extract thirty clinically relevant variables. The REPT reduced these variables down to two limb symmetry measures (maximum anterior hop and maximum lateral hop), capable of classifying injury status between the healthy and ACL injured participants with a 69% sensitivity, 78% specificity and kappa statistic of 0.464. We, therefore, conclude that the REPT model was able to evaluate functional capacity as it relates to injury status in adolescent females. We also recommend considering these variables when developing RTA assessments and guidelines.Clinical Relevance- Our results indicate that spatiotemporal measures may differentiate ACL-injured and healthy female adolescents with moderate confidence using a REPT. The identified tests may reasonably be added to the clinical evaluation process when evaluating functional capacity and readiness to return to activity.

Realtime phase-amplitude coupling analysis of micro electrode recorded brain signals.

OBJECTIVE: To demonstrate a method to calculate phase amplitude coupling (PAC) quickly and robustly for realtime applications. METHODS: We designed and implemented a multirate PAC algorithm with efficient filter bank processing and efficient computation of PAC for many frequency-pair combinations. We tested the developed algorithm for computing PAC on simulated data and on intraoperative neural recording data obtained during deep brain stimulation (DBS) electrode implantation surgery. RESULTS: A combination of parallelized frequency-domain filtering and modulation index for PAC estimation provided robust results that could be calculated in real time on modest computing hardware. CONCLUSION: The standard methods for calculating PAC can be optimized for quick and robust performance. SIGNIFICANCE: These results demonstrated that PAC can be extracted in real time and is suitable for neurofeedback applications.

Signal Quality Analysis of Ambulatory Electrocardiograms to Gate False Myocardial Ischemia Alarms.

OBJECTIVE: The objective of this study is to propose and validate an alarm gating system for a myocardial ischemia monitoring system that uses ambulatory electrocardiogram. The PeriOperative ISchemic Evaluation study recommended the selective administration of ß blockers to patients at risk of cardiac events following noncardiac surgery. Patients at risk are identified by monitoring ST segment deviations in the electrocardiogram (ECG); however, patients are encouraged to ambulate to improve recovery, which deteriorates the signal quality of the ECG leading to false alarms. METHODS: The proposed alarm gating system computes a signal quality index (SQI) to quantify the ECG signal quality and rejects alarms with a low SQI. The system was validated by artificially contaminating ECG records with motion artifact records obtained from the long-term ST database and MIT-BIH noise stress test database, respectively. RESULTS: Without alarm gating, the myocardial ischemia monitoring system attained a Precision of 0.31 and a Recall of 0.78. The alarm gating improved the Precision to 0.58 with a reduction of Recall to 0.77. CONCLUSION: The proposed system successfully gated false alarms with future work exploring the misidentification of fiducial points by myocardial ischemia monitoring systems. SIGNIFICANCE: The reduction of false alarms due to the proposed system will decrease the incidence of the alarm fatigue condition typically found in clinicians. Alarm fatigue condition was rated as the top patient safety hazard from 2012 to 2015 by the Emergency Care Research Institute.

The Placental Distal Villous Hypoplasia Pattern: Interobserver Agreement and Automated Fractal Dimension as an Objective Metric.

The distal villous hypoplasia (DVH) pattern is a placental correlate of fetal growth restriction. Because the pattern seems to involve less complexity than do appropriately developed placental villi, we postulated that it may be associated with lower fractal dimension-a mathematical measure of complexity. Our study objectives were to evaluate interobserver agreement related to the DVH pattern among expert pathologists and to determine whether pathologist classification of DVH correlates with fractal dimension. A study set of 30 images of placental parenchyma at ×4 magnification was created by a single pathologist from a digital slide archive. The images were graded for the DVH pattern according to pre-specified definitions and included 10 images graded as "no DVH" (grade  =  0), 10 with mild to moderate DVH (grade  =  1), and 10 with severe DVH (grade  =  2). The images were randomly sorted and shown to a panel of 4 international experts who similarly graded the images for DVH. Weighted kappas were calculated. For each image, fractal dimension was calculated by the Box Counting method. The correlation coefficient between (1) the averaged DVH scores obtained by the 5 pathologists and (2) fractal dimension was calculated. The mean weighted kappa score among the observers was 0.59 (range: 0.42-0.70). The correlation coefficient between fractal dimension and the averaged DVH score was -0.915 (P < 0.001). Expert pathologists achieve fair to substantial agreement in grading DVH, indicating consensus on the definition of DVH. Distal villous hypoplasia correlates extremely well with fractal dimension and represents an objective measure for DVH.