Minerva Elements Records

Permanent URI for this collection

Search Results

Now showing 1 - 10 of 3533
  • Item
    No Preview Available
    Algorithmic Decisions, Desire for Control, and the Preference for Human Review over Algorithmic Review
    Lyons, H ; Miller, T ; Velloso, E (ASSOC COMPUTING MACHINERY, 2023)
  • Item
    No Preview Available
    Lossy Compression Options for Dense Index Retention
    Mackenzie, J ; Moffat, A (ASSOC COMPUTING MACHINERY, 2023)
  • Item
    No Preview Available
  • Item
    No Preview Available
    Generating Dynamic Kernels via Transformers for Lane Detection
    Chen, Z ; Liu, Y ; Gong, M ; Du, B ; Qian, G ; Smith-Miles, K (IEEE, 2023-01-01)
  • Item
    No Preview Available
  • Item
    No Preview Available
    Model Parameter Estimation As Features to Predict the Duration of Epileptic Seizures From Onset.
    Liu, Y ; Xia, S ; Soto-Breceda, A ; Karoly, P ; Cook, MJ ; Grayden, DB ; Schmidt, D ; Kuhlmann, L (IEEE, 2023-07)
    The durations of epileptic seizures are linked to severity and risk for patients. It is unclear if the spatiotemporal evolution of a seizure has any relationship with its duration. Understanding such mechanisms may help reveal treatments for reducing the duration of a seizure. Here, we present a novel method to predict whether a seizure is going to be short or long at its onset using features that can be interpreted in the parameter space of a brain model. The parameters of a Jansen-Rit neural mass model were tracked given intracranial electroencephalography (iEEG) signals, and were processed as time series features using MINIROCKET. By analysing 2954 seizures from 10 patients, patient-specific classifiers were built to predict if a seizure would be short or long given 7 s of iEEG at seizure onset. The method achieved an area under the receiver operating characteristic curve (AUC) greater than 0.6 for five of 10 patients. The behaviour in the parameter space has shown different mechanisms are associated with short/long seizures.Clinical relevance-This shows that it is possible to classify whether a seizure will be short or long based on its early characteristics. Timely interventions and treatments can be applied if the duration of the seizures can be predicted.
  • Item
    No Preview Available
    Scalable Label-efficient Footpath Network Generation Using Remote Sensing Data and Self-supervised Learning
    Wanyan, X ; Seneviratne, S ; Nice, K ; Thompson, J ; White, M ; Langenheim, N ; Stevenson, M (IEEE, 2023-01-01)
  • Item
    No Preview Available
    Establishing the Calibration Curve of a Compressive Ophthalmodynamometry Device.
    Kaplan, MA ; Bui, BV ; Ayton, LN ; Nguyen, B ; Grayden, DB ; John, S (IEEE, 2023-07)
    The relationship between externally applied force and intraocular pressure was determined using an ex-vivo porcine eye model (N=9). Eyes were indented through the sclera with a convex ophthalmodynamometry head (ODM). Intraocular pressure and ophthalmodynamometric force were simultaneously recorded to establish a calibration curve of this indenter head. A calibration coefficient of 0.140 ± 0.009 mmHg/mN was established and was shown to be highly linear (r = 0.998 ± 0.002). Repeat application of ODM resulted in a 0.010 ± 0.002 mmHg/mN increase to the calibration coefficient.Clinical Relevance- ODM has been highlighted as a potential method of non-invasively estimating intracranial pressure. This study provides relevant data for the practical performance of ODM with similar compressive devices.
  • Item
    No Preview Available
    The Future Can’t Help Fix The Past: Assessing Program Repair In The Wild
    Kabadi, V ; Kong, D ; Xie, S ; Bao, L ; Azriadi Prana, GA ; Le, T-DB ; Le, X-BD ; Lo, D (IEEE, 2023-10-01)
  • Item
    No Preview Available
    The Effect of Fetal Heart Rate Segment Selection on Deep Learning Models for Fetal Compromise Detection
    Mendis, L ; Palaniswami, M ; Brownfoot, F ; Keenan, E (IEEE, 2023)
    Monitoring the fetal heart rate (FHR) is common practice in obstetric care to assess the risk of fetal compromise. Unfortunately, human interpretation of FHR recordings is subject to inter-observer variability with high false positive rates. To improve the performance of fetal compromise detection, deep learning methods have been proposed to automatically interpret FHR recordings. However, existing deep learning methods typically analyse a fixed-length segment of the FHR recording after removing signal gaps, where the influence of this segment selection process has not been comprehensively assessed. In this work, we develop a novel input length invariant deep learning model to determine the effect of FHR segment selection for detecting fetal compromise. Using this model, we perform five times repeated five-fold cross-validation on an open-access database of 552 FHR recordings and assess model performance for FHR segment lengths between 15 and 60 minutes. We show that the performance after removing signal gaps improves with increasing segment length from 15 minutes (AUC = 0.50) to 60 minutes (AUC = 0.74). Additionally, we demonstrate that using FHR segments without removing signal gaps achieves superior performance across signal lengths from 15 minutes (AUC = 0.68) to 60 minutes (AUC = 0.76). These results show that future works should carefully consider FHR segment selection and that removing signal gaps might contribute to the loss of valuable information.