Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An unsupervised methodology for the detection of Epileptic seizures in EEG recordings is proposed. The time-frequency content of the EEG signals is extracted using the Short Time Fourier Transform. The analysis focuses on the EEG energy distribution among the ll-established delta, theta and alpha rhythms (2–13 Hz), as energy variations in these frequency bands are widely associated with seizure activity. Relying on seizure rhythmicity, the classification is performed by isolating the segments where each rhythm is more clearly and dominantly expressed over the others. For the first time, an unsupervised methodology is evaluated using more than 978 hours of EEG recordings from a public database. The results show that the proposed methodology achieves high seizure detection sensitivity with significantly reduced human intervention.
['Kostas M. Tsiouris', 'Spiros Konitsiotis', 'Sofia Markoula', 'Dimitrios Koutsouris', 'Antonis I. Sakellarios', 'Dimitrios I. Fotiadis']
I am writing a research paper on a idea An unsupervised methodology for the detection of epileptic seizures in long-term EEG signals
[{"from": "human", "value": "I am writing a research paper on a idea An unsupervised methodology for the detection of epileptic seizures in long-term EEG signals"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An unsupervised methodology for the detection of Epileptic seizures in EEG recordings is proposed. The time-frequency content of the EEG signals is extracted using the Short Time Fourier Transform. The analysis focuses on the EEG energy distribution among the ll-established delta, theta and alpha rhythms (2\u201313 Hz), as energy variations in these frequency bands are widely associated with seizure activity. Relying on seizure rhythmicity, the classification is performed by isolating the segments where each rhythm is more clearly and dominantly expressed over the others. For the first time, an unsupervised methodology is evaluated using more than 978 hours of EEG recordings from a public database. The results show that the proposed methodology achieves high seizure detection sensitivity with significantly reduced human intervention."}]