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Clasificación de crisis epilépticas mediante un marco híbrido de aprendizaje profundo con características Hjorth
Neerja Dharmale1, Rupesh Mahamune1, Kamlesh Kahar1
1Department of Electronics & Telecommunication Engineering, Shri Sant Gajanan Maharaj College of Engineering, Shegaon, India.
Este estudio presenta un marco novedoso que utiliza parámetros Hjorth y modelado temporal mejorado por atención para clasificar las etapas de las crisis epilépticas. El enfoque multidominio logró una alta precisión en la detección de crisis binarias, de tres clases y de cinco clases.
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