- Poster presentation
- Open Access
Description and removal of background activity in EEG power spectra under general anesthesia using the Lorentzian curve
© Fedotenkova et al. 2015
- Published: 18 December 2015
- Power Spectral Density
- Background Activity
- Alpha Power
- Lorentzian Curve
General anesthesia is an important medical procedure in today's hospital practice and comprises loss of consciousness, analgesia, amnesia and immobility. Our current work analyzes patient reaction on nociception stimuli during a surgical operation and differences in this reaction provided different anesthetic drugs, propofol and desflurane. The studied dataset comprises EEG-recordings before and after incision obtained from 115 patients . The task is the identification of spectral EEG signal features reflecting the incision. This analysis will reveal a possible new marker of pain during deep anesthesia.
This work considers one of the approaches to the problem, namely spectral analysis. First, power spectral density (PSD) estimates were obtained using Welch's method. It is well known that EEG power spectrum decays with higher frequencies following ~1/f scaling [2–4]. We attribute this behavior to background activity , which takes place in the brain when no other activity is present. Background activity was describe by fitting regression curve P(f)~a/f b to each PSD estimate. However, the resulting goodness of fit was not satisfactory. It is due to rise of power in delta peak, which becomes prominent under general anesthesia and makes the process of curve fitting less reliable. Thus, the original model was substituted by the Lorentzian function P(f)=a/(f b + c), which resembles the shape of actual power spectrum quite well. Afterwards, regression curves were subtracted from each power spectrum to normalize it  and to analyze spectral power contained in delta and alpha peaks regardless of distinctions in patients.
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