arXiv subjects

Pardis Biglarbeigi

Publications and source records attributed to Pardis Biglarbeigi.

3 recordsLinked to original sources

arXiv2024

Frequency content and filtering of head sensor kinematics: A method to enable field-based inter-study comparisons

Wearable head sensor systems use different kinematic signal processing approaches which limits field-based inter-study comparisons, especially when artefacts are present in the signal. The aim of this study is to assess the frequency content and characteristics of head kinematic signals from head impact reconstruction laboratory and field-based environments to develop an artefact attenuation filtering method (artefact attenuation method). Laboratory impacts (n=72) on a test-dummy headform ranging from 25-150 g were conducted and 126 elite-level rugby union players were equipped with instrumented mouthguards (iMG) for up to four matches. Power spectral density (PSD) characteristics of the laboratory impacts and on-field HAE (n=5694) such as the 95th percentile cumulative sum PSD frequency were utilised to develop the artefact attenuation method. The artefact attenuation method was compared to two other common filtering approaches (Fourth order (2x2 pole), zero-lag Butterworth filter with 200 Hz (-6 dB) cut-off frequency (Butterworth-200Hz) and CFC180 filter) through signal-to-noise ratio (SNR) and mixed linear effects models for laboratory and on-field events, respectively. The artefact attenuation method produced an overall higher SNR than the Butterworth-200Hz and CFC180 filter and on-field peak linear acceleration (PLA) and peak angular acceleration (PAA) values within the magnitude range tested in the laboratory. Median PLA and PAA were higher for the CFC180 filter than the Butterworth-200Hz (p<0.01) and artefact attenuation method (p<0.01), reporting values as high as 294 g and 31.2 krad/s2. The artefact attenuation method can be applied to all commercially available iMG kinematic signals with adequate sample rates to enable field-based inter-study comparisons.

physics.med-ph↗

arXiv2022

Epileptic Seizure Classification Using Combined Labels and a Genetic Algorithm

Epilepsy affects 50 million people worldwide and is one of the most common serious neurological disorders. Seizure detection and classification is a valuable tool for diagnosing and maintaining the condition. An automated classification algorithm will allow for accurate diagnosis. Utilising the Temple University Hospital (TUH) Seizure Corpus, six seizure types are compared; absence, complex partial, myoclonic, simple partial, tonic and tonic- clonic models. This study proposes a method that utilises unique features with a novel parallel classifier - Parallel Genetic Naive Bayes (NB) Seizure Classifier (PGNBSC). The PGNBSC algorithm searches through the features and by reclassifying the data each time, the algorithm will create a matrix for optimum search criteria. Ictal states from the EEGs are segmented into 1.8 s windows, where the epochs are then further decomposed into 13 different features from the first intrinsic mode function (IMF). The features are compared using an original NB classifier in the first model. This is improved upon in a second model by using a genetic algorithm (Binary Grey Wolf Optimisation, Option 1) with a NB classifier. The third model uses a combination of the simple partial and complex partial seizures to provide the highest classification accuracy for each of the six seizures amongst the three models (20%, 53%, and 85% for first, second, and third model, respectively).

eess.SP↗

arXiv2021

Seizure Classification of EEG based on Wavelet Signal Denoising Using a Novel Channel Selection Algorithm

Epilepsy is a disorder of the nervous system that can affect people of any age group. With roughly 50 million people worldwide diagnosed with the disorder, it is one of the most common neurological disorders. The EEG is an indispensable tool for diagnosis of epileptic seizures in an ideal case, as brain waves from an epileptic person will present distinct abnormalities. However, in real world situations there will often be biological and electrical noise interference, as well as the issue of a multichannel signal, which introduce a great challenge for seizure detection. For this study, the Temple University Hospital (TUH) EEG Seizure Corpus dataset was used. This paper proposes a novel channel selection method which isolates different frequency ranges within five channels. This is based upon the frequencies at which normal brain waveforms exhibit. A one second window was selected, with a 0.5 second overlap. Wavelet signal denoising was performed using Daubechies 4 wavelet decomposition, thresholding was applied using minimax soft thresholding criteria. Filter banking was used to localise frequency ranges from five specific channels. Statistical features were then derived from the outputs. After performing bagged tree classification using 500 learners, a test accuracy of 0.82 was achieved.

q-bio.PE↗