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Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches.
Al-Qazzaz, Noor Kamal; Alrahhal, Maher; Jaafer, Sumai Hamad; Ali, Sawal Hamid Bin Mohd; Ahmad, Siti Anom.
Afiliación
  • Al-Qazzaz NK; Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, 47146, Iraq. Electronic address: noorbme@kecbu.uobaghdad.edu.iq.
  • Alrahhal M; Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Hyderabad, University College of Engineering, Science and Technology Hyderabad, Telangana, India. Electronic address: maherrahal92@gmail.com.
  • Jaafer SH; Medical Laboratory Department, Erbil Medical Institute, Erbil Polytechnic University, Kirkuk Road, Hadi Chawshli Street, Kurdistan Region, Erbil, Iraq. Electronic address: sumaya.hamad@epu.edu.iq.
  • Ali SHBM; Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, 43600, Malaysia; Centre of Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering,
  • Ahmad SA; Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang, Selangor, 43400, Malaysia; Malaysian Research Institute of Ageing (MyAgeing)TM, Universiti Putra Malaysia, UPM Serdang, Selangor, 43400, Malaysia. Electronic address: sanom@upm.edu.my
Med Eng Phys ; 130: 104206, 2024 08.
Article en En | MEDLINE | ID: mdl-39160030
ABSTRACT
Epilepsy is one of the most common brain diseases, characterised by repeated seizures that occur on a regular basis. During a seizure, a patient's muscles flex uncontrollably, causing a loss of mobility and balance, which can be harmful or even fatal. Developing an automatic approach for warning patients of oncoming seizures necessitates substantial research. Analyzing the electroencephalogram (EEG) output from the human brain's scalp region can help predict seizures. EEG data were analyzed to extract time domain features such as Hurst exponent (Hur), Tsallis entropy (TsEn), enhanced permutation entropy (impe), and amplitude-aware permutation entropy (AAPE). In order to automatically diagnose epileptic seizure in children from normal children, this study conducted two sessions. In the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models, including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), and in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers in The EEG dataset was obtained from the Neurology Clinic of the Ibn Rushd Training Hospital. In this regard, extensive explanations and research from the time domain and entropy characteristics demonstrate that employing GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All-time-entropy fusion feature improves the final classification results.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Señales Asistido por Computador / Entropía / Electroencefalografía / Epilepsia / Aprendizaje Profundo Límite: Child / Child, preschool / Humans / Male Idioma: En Revista: Med Eng Phys Asunto de la revista: BIOFISICA / ENGENHARIA BIOMEDICA Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Señales Asistido por Computador / Entropía / Electroencefalografía / Epilepsia / Aprendizaje Profundo Límite: Child / Child, preschool / Humans / Male Idioma: En Revista: Med Eng Phys Asunto de la revista: BIOFISICA / ENGENHARIA BIOMEDICA Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido