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1. Evaluation of Robotic-Assisted Carotid Artery Stenting in a Virtual Model Using Motion-Based Performance Metrics

   https://doi.org/10.1177/15266028221125592  

   Legeza, Peter T. ; Lettenberger, Ahalya B. ; Murali, Barathwaj ; Johnson, Lianne R. ; Berczeli, Marton ; Byrne, Michael D. ; Britz, Gavin ; O’Malley, Marcia K. ; Lumsden, Alan B ( January 2022 , Journal of Endovascular Therapy)

   Purpose: Robotic-assisted carotid artery stenting (CAS) cases have been demonstrated with promising results. However, no quantitative measurements have been made to compare manual with robotic-assisted CAS. This study aims to quantify surgical performance using tool tip kinematic data and metrics of precision during CAS with manual and robotic control in an ex vivo model. Materials and Methods: Transfemoral CAS cases were performed in a high-fidelity endovascular simulator. Participants completed cases with manual and robotic techniques in 2 different carotid anatomies in random order. C-arm angulations, table position, and endovascular devices were standardized. Endovascular tool tip kinematic data were extracted. We calculated the spectral arc length (SPARC), average velocity, and idle time during navigation in the common carotid artery and lesion crossing. Procedural time, fluoroscopy time, movements of the deployed filter wire, precision of stent, and balloon positioning were recorded. Data were analyzed and compared between the 2 modalities. Results: Ten participants performed 40 CAS cases with a procedural success of 100% and 0% residual stenosis. The median procedural time was significantly higher during the robotic-assisted cases (seconds, median [interquartile range, IQR]: 128 [49.5] and 161.5 [62.5], p=0.02). Fluoroscopy time differed significantly between manual and robotic-assisted procedures (seconds, median [IQR]: 81.5 [32] and 98.5 [39.5], p=0.1). Movement of the deployed filter wire did not show significant difference between manual and robotic interventions (mm, median [IQR]: 13 [10.5] and 12.5 [11], p=0.5). The postdilation balloon exceeded the margin of the stent with a median of 2 [1] mm in both groups. Navigation with robotic assistance showed significantly lower SPARC values (–5.78±3.14 and –8.63±3.98, p=0.04) and higher idle time values (8.92±8.71 and 3.47±3.9, p=0.02) than those performed manually. Conclusions: Robotic-assisted and manual CAS cases are comparable in the precision of stent and balloon positioning. Navigation in the carotid artery is associated with smoother motion and higher idle time values. These findings highlight the accuracy and the motion stabilizing capability of the endovascular robotic system. Clinical Impact Robotic assistance in the treatment of peripheral vascular disease is an emerging field and may be a tool for radiation protection and the geographic distribution of endovascular interventions in the future. This preclinical study compares the characteristics of manual and robotic-assisted carotid stenting (CAS). Our results highlight, that robotic-assisted CAS is associated with precise navigation and device positioning, and smoother navigation compared to manual CAS. 

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2. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEG channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9. 

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3. Wind Stress, Wind Stress Curl, and Upwelling Velocities in the Northwest Atlantic (80-45W, 30-45N) during 1980-2019

   https://doi.org/10.5281/zenodo.8200832  

   Gifford, Ian ; Gangopadhyay, Avijit ; Andres, Magdalena ; Gawarkiewicz, Glen ; Oliver, Hilde ; Silver, Adrienne ( January 2023 , Zenodo)

   This dataset contains three netcdf files that pertain to monthly, seasonal, and annual fields of surface wind stress, wind stress curl, and curl-derived upwelling velocities over the Northwest Atlantic (80-45W, 30-45N) covering a forty year period from 1980 to 2019. Six-hourly surface (10 m) wind speed components from the Japanese 55-year reanalysis (JRA-55; Kobayashi et al., 2015) were processed from 1980 to 2019 over a larger North Atlantic domain of 100W to 10E and 10N to 80N. Wind stress was computed using a modified step-wise formulation, originally based on (Gill, 1982) and a non-linear drag coefficient (Large and Pond, 1981), and later modified for low speeds (Trenberth et al., 1989). See Gifford (2023) for more details.   

   After the six-hourly zonal and meridional wind stresses were calculated, the zonal change in meridional stress (curlx) and the negative meridional change in zonal stress (curly) were found using NumPy’s gradient function in Python (Harris et al., 2020) over the larger North Atlantic domain (100W-10E, 10-80N). The curl (curlx + curly) over the study domain (80-45W, 10-80N) is then extracted, which maintain a constant order of computational accuracy in the interior and along the boundaries for the smaller domain in a centered-difference gradient calculation. 

   The monthly averages of the 6-hour daily stresses and curls were then computed using the command line suite climate data operators (CDO, Schulzweida, 2022) monmean function. The seasonal (3-month average) and annual averages (12-month average) were calculated in Python using the monthly fields with NumPy (NumPy, Harris et al., 2020). 

   Corresponding upwelling velocities at different time-scales were obtained from the respective curl fields and zonal wind stress by using the Ekman pumping equation of the study by Risien and Chelton (2008; page 2393). Please see Gifford (2023) for more details.   

   The files each contain nine variables that include longitude, latitude, time, zonal wind stress, meridional wind stress, zonal change in meridional wind stress (curlx), the negative meridional change in zonal wind stress (curly), total curl, and upwelling. Units of time begin in 1980 and are months, seasons (JFM etc.), and years to 2019. The longitude variable extends from 80W to 45W and latitude is 30N to 45N with uniform 1.25 degree resolution.  

   Units of stress are in Pascals, units of curl are in Pascals per meter, and upwelling velocity is described by centimeters per day. The spatial grid is a 29 x 13 longitude x latitude array. 

   Filenames: 

   monthly_windstress_wsc_upwelling.nc: 480 time steps from 80W to 45W and 30N to 45N.

   seasonal_windstress_wsc_upwelling.nc: 160 time steps from 80W to 45W and 30N to 45N.

   annual_windstress_wsc_upwelling.nc: 40 time steps from 80W to 45W and 30N to 45N.

   Please contact igifford@earth.miami.edu for any queries. {"references": ["Gifford, I.H., 2023. The Synchronicity of the Gulf Stream Free Jet and the Wind Induced Cyclonic Vorticity Pool. MS Thesis, University of Massachusetts Dartmouth. 75pp.", "Gill, A. E. (1982). Atmosphere-ocean dynamics (Vol. 30). Academic Press.", "Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357\u2013362 (2020). DOI: 10.1038/s41586-020-2649-2.", "Japan Meteorological Agency/Japan (2013), JRA-55: Japanese 55-year Reanalysis, Daily 3-Hourly and 6-Hourly Data, https://doi.org/10.5065/D6HH6H41, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, Colo. (Updated monthly.)", "Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H. and Miyaoka, K., 2015. The JRA-55 reanalysis: General specifications and basic characteristics.\u202fJournal of the Meteorological Society of Japan. Ser. II,\u202f93(1), pp.5-48.", "Large, W.G. and Pond, S., 1981. Open ocean momentum flux measurements in moderate to strong winds.\u202fJournal of physical oceanography,\u202f11(3), pp.324-336.", "Risien, C.M. and Chelton, D.B., 2008. A global climatology of surface wind and wind stress fields from eight years of QuikSCAT scatterometer data.\u202fJournal of Physical Oceanography,\u202f38(11), pp.2379-2413.", "Schulzweida, Uwe. (2022). CDO User Guide (2.1.0). Zenodo. https://doi.org/10.5281/zenodo.7112925.", "Trenberth, K.E., Large, W.G. and Olson, J.G., 1989. The effective drag coefficient for evaluating wind stress over the oceans.\u202fJournal of Climate,\u202f2(12), pp.1507-1516."]} 

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4. Composite quantitative knee structure metrics predict the development of accelerated knee osteoarthritis: data from the osteoarthritis initiative

   https://doi.org/10.1186/s12891-020-03338-7  

   Harkey, Matthew S. ; Davis, Julie E. ; Price, Lori Lyn ; Ward, Robert J. ; MacKay, James W. ; Eaton, Charles B. ; Lo, Grace H. ; Barbe, Mary F. ; Zhang, Ming ; Pang, Jincheng ; et al ( December 2020 , BMC Musculoskeletal Disorders)

   Abstract Background We aimed to determine if composite structural measures of knee osteoarthritis (KOA) progression on magnetic resonance (MR) imaging can predict the radiographic onset of accelerated knee osteoarthritis. Methods We used data from a nested case-control study among participants from the Osteoarthritis Initiative without radiographic KOA at baseline. Participants were separated into three groups based on radiographic disease progression over 4 years: 1) accelerated (Kellgren-Lawrence grades [KL] 0/1 to 3/4), 2) typical (increase in KL, excluding accelerated osteoarthritis), or 3) no KOA (no change in KL). We assessed tibiofemoral cartilage damage (four regions: medial/lateral tibia/femur), bone marrow lesion (BML) volume (four regions: medial/lateral tibia/femur), and whole knee effusion-synovitis volume on 3 T MR images with semi-automated programs. We calculated two MR-based composite scores. Cumulative damage was the sum of standardized cartilage damage. Disease activity was the sum of standardized volumes of effusion-synovitis and BMLs. We focused on annual images from 2 years before to 2 years after radiographic onset (or a matched time for those without knee osteoarthritis). To determine between group differences in the composite metrics at all time points, we used generalized linear mixed models with group (3 levels) and time (up to 5 levels). For our prognostic analysis, we used multinomial logistic regression models to determine if one-year worsening in each composite metric change associated with future accelerated knee osteoarthritis (odds ratios [OR] based on units of 1 standard deviation of change). Results Prior to disease onset, the accelerated KOA group had greater average disease activity compared to the typical and no KOA groups and this persisted up to 2 years after disease onset. During a pre-radiographic disease period, the odds of developing accelerated KOA were greater in people with worsening disease activity [versus typical KOA OR (95% confidence interval [CI]): 1.58 (1.08 to 2.33); versus no KOA: 2.39 (1.55 to 3.71)] or cumulative damage [versus typical KOA: 1.69 (1.14 to 2.51); versus no KOA: 2.11 (1.41 to 3.16)]. Conclusions MR-based disease activity and cumulative damage metrics may be prognostic markers to help identify people at risk for accelerated onset and progression of knee osteoarthritis. 

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5. On the Role of Interconnection Directionality in the Quadratic Performance of Double-Integrator Networks

   https://doi.org/10.1109/TAC.2021.3135358  

   Oral, Hasan Giray ; Mallada, Enrique ; Gayme, Dennice ( December 2021 , IEEE Transactions on Automatic Control)

   This paper provides a framework to evaluate the performance of single and double integrator networks over arbitrary directed graphs. Adopting vehicular network terminology, we consider quadratic performance metrics defined by the L2-norm of position and velocity based response functions given impulsive inputs to each vehicle. We exploit the spectral properties of weighted graph Laplacians and output performance matrices to derive a novel method of computing the closed-form solutions for this general class of performance metrics, which include H2-norm based quantities as special cases. We then explore the effect of the interplay between network properties (such as edge directionality and connectivity) and the control strategy on the overall network performance. More precisely, for systems whose interconnection is described by graphs with normal Laplacian L, we characterize the role of directionality by comparing their performance with that of their undirected counterparts, represented by the Hermitian part of L. We show that, for single-integrator networks, directed and undirected graphs perform identically. However, for double-integrator networks, graph directionality -expressed by the eigenvalues of L with nonzero imaginary part- can significantly degrade performance. Interestingly, in many cases, well-designed feedback can also exploit directionality to mitigate degradation or even improve the performance to exceed that of the undirected case. Finally we focus on a system coherence metric -aggregate deviation from the state average- to investigate the relationship between performance and degree of connectivity, leading to somewhat surprising findings. For example, increasing the number of neighbors on a ω-nearest neighbor directed graph does not necessarily improve performance. Similarly, we demonstrate equivalence in performance between all-to-one and all-to-all communication graphs. 

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