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المؤلفBoashash, Boualem
المؤلفOuelha, Samir
المؤلفMaqsood, Sadiq Ali
تاريخ الإتاحة2022-04-21T10:27:20Z
تاريخ النشر2016
اسم المنشورQatar Foundation Annual Research Conference Proceedings
المصدرqscience
الاقتباسBoashash B, Ouelha S, Maqsood SA. (2016). Design of a Time-Frequency Algorithm for Automatic Eeg Artifact Removal. Qatar Foundation Annual Research Conference Proceedings 2016: HBPP3376 http://dx.doi. org/10.5339/qfarc.2016.HBPP3376.
الرقم المعياري الدولي للكتاب2226-9649
معرّف المصادر الموحدhttps://doi.org/10.5339/qfarc.2016.HBPP3376
معرّف المصادر الموحدhttp://hdl.handle.net/10576/30225
الملخصThe injuries suffered by newborns during birth are a major health issue. To improve the health outcomes of sick newborns using EEG measurements, a number of recent studies focused on the use of high-resolution Time-Frequency Distributions to extract critical information from the collected signals [1]. Several algorithms have been proposed. A major problem in the implementation of such algorithms for fully automated EEG signal classification systems is caused by artifacts. In particular, previous studies have shown that a respiratory artifact looks like a seizure signal and can be misinterpreted by the automatic abnormality detection system thus resulting in false alarms. Hence, the successful removal of the artifacts is important, as shown in several previous studies [2]; and, there are two basic approaches for this: (1) use machine learning technique to detect and reject EEG segments corrupted by artifact; but this would result in the loss of EEG data [2]. (2) Correct EEG segments corrupted by artifacts; some artifacts can be corrected by a simple filter in a frequency domain, e.g. notch filter can be used to remove 50 Hz noise. This approach does not require any reference signals. For more complicated cases, when the spectrum of artifacts overlaps with the spectrum of EEG signals, blind source separation (BSS) algorithms can be used. Typically a multi-component EEG signal is transformed into a linear combination of independent components (that can be interpreted as channels (ICs)) by blind source separation techniques such as the independent component analysis (ICA) or canonical correlation analysis. The independent channels that are corrupted by artifacts are identified either manually or automatically using correlation information from a reference signal. The artifact free signal is then constructed by combining only artifact-free ICs.
اللغةen
الناشرHamad bin Khalifa University Press (HBKU Press)
الموضوعTime-Frequency Algorithm
Automatic Eeg Artifact Removal
Automatic Eeg
العنوانDesign of a Time-Frequency Algorithm for Automatic Eeg Artifact Removal
النوعConference Paper
رقم العدد1
رقم المجلد2016


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