skip to main content
Idioma:

IMPROVED SEGMENTATION FOR AUTOMATED SEIZURE DETECTION USING CHANNEL-DEPENDENT POSTERIORS

0000-0001-5193-0206 ; Shah, Vinit

Temple University. Libraries

Sem texto completo

Citações Citado por
  • Título:
    IMPROVED SEGMENTATION FOR AUTOMATED SEIZURE DETECTION USING CHANNEL-DEPENDENT POSTERIORS
  • Autor: 0000-0001-5193-0206 ; Shah, Vinit
  • Descrição: The electroencephalogram (EEG) is the primary tool used for the diagnosis of a varietyof neural pathologies such as epilepsy. Identification of a critical event, such as an epileptic; seizure, is difficult because the signals are collected by transducing extremely low voltages,; and as a result, are corrupted by noise. Also, EEG signals often contain artifacts due to; clinical phenomena such as patient movement. These artifacts are easily confused as; seizure events. Factors such as slowly evolving morphologies make accurate marking of; the onset and offset of a seizure event difficult. Precise segmentation, defined as the ability; to detect start and stop times within a fraction of a second, is a challenging research; problem. In this dissertation, we improve seizure segmentation performance by developing; deep learning technology that mimics the human interpretation process.; The central thesis of this work is that separation of the seizure detection problem into; a two-phase problem – epileptiform activity detection followed by seizure detection –; should improve our ability to detect and localize seizure events. In the first phase, we use; a sequential neural network algorithm known as a long short-term memory (LSTM); network to identify channel-specific epileptiform discharges associated with seizures. In; the second phase, the feature vector is augmented with posteriors that represent the onset; and offset of ictal activities. These augmented features are applied to a multichannel; convolutional neural network (CNN) followed by an LSTM network.; The multiphase model was evaluated on a blind evaluation set and was shown to detect; 106 segment boundaries within a 2-second margin of error. Our previous best system,; which delivers state-of-the-art performance on this task, correctly detected only 9 segment; boundaries. Our multiphase system was also shown to be robust by performing well on two; blind evaluation sets. Seizure detection performance on the TU Seizure Detection (TUSZ); Corpus development set is 41.60% sensitivity with 5.63 false alarms/24 hours; (FAs/24 hrs). Performance on the corresponding evaluation set is 48.21% sensitivity with; 16.54 FAs/24 hrs. Performance on a previously unseen corpus, the Duke University; Seizure (DUSZ) Corpus is 46.62% sensitivity with 7.86 FAs/24 hrs. Our previous best; system yields 30.83% sensitivity with 6.74 FAs/24 hrs on the TUSZ development set,; 33.11% sensitivity with 19.89 FAs/24 hrs on the TUSZ evaluation set and 33.71%; sensitivity with 40.40 FAs/24 hrs on DUSZ.; Improving seizure detection performance through better segmentation is an important; step forward in making automated seizure detection systems clinically acceptable. For a; real-time system, accurate segmentation will allow clinicians detect a seizure as soon as it; appears in the EEG signal. This will allow neurologists to act during the early stages of the; event which, in many cases, is essential to avoid permanent damage to the brain. In a; similar way, accurate offset detection will help with delivery of therapies designed to; mitigate postictal (after seizure) period symptoms. This will also help reveal the severity; of a seizure and consequently provide guidance for medicating a patient. Electrical and Computer Engineering
  • Editor: Temple University. Libraries
  • Idioma: Inglês
Links
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.