A Bayesian approach to estimate evoked potentials

• Published in: Computer methods and programs in biomedicine Vol. 68; no. 3; pp. 233 - 248
• Main Authors: Sparacino, Giovanni, Milani, Stefano, Arslan, Edoardo, Cobelli, Claudio
• Format: Journal Article
• Language: English
• Published: Shannon Elsevier Ireland Ltd 01.06.2002; Elsevier Science
• Subjects: Audiology; Averaging; Bayes Theorem; Biological and medical sciences; Child; Cochlea - physiopathology; Electrodiagnosis. Electric activity recording; Electroencephalography - methods; Electrophysiology; Evoked Potentials - physiology; Evoked Potentials, Auditory - physiology; Humans; Investigative techniques, diagnostic techniques (general aspects); Medical sciences; Nervous system; Optimal filtering; Otorhinolaryngology functional investigation (larynx, voice, audiometry, vestibular function, equilibration...); Smoothing; Averaging; Smoothing; Optimal filtering; Audiology; Bayes estimation; Human; Averaging method; Filtering; ENT; Electroencephalography; Optimization; Electrodiagnosis; Hearing; Evoked potential; Auditory evoked potential; A priori estimation; Signal processing; Signal analysis; Medical application; Signal to noise ratio
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/S0169-2607(01)00175-4

Several approaches, based on different assumptions and with various degree of theoretical sophistication and implementation complexity, have been developed for improving the measurement of evoked potentials (EP) performed by conventional averaging (CA). In many of these methods, one of the major challenges is the exploitation of a priori knowledge. In this paper, we present a new method where the 2nd-order statistical information on the background EEG and on the unknown EP, necessary for the optimal filtering of each sweep in a Bayesian estimation framework, is, respectively, estimated from pre-stimulus data and obtained through a multiple integration of a white noise process model. The latter model is flexible (i.e. it can be employed for a large class of EP) and simple enough to be easily identifiable from the post-stimulus data thanks to a smoothing criterion. The mean EP is determined as the weighted average of the filtered sweeps, where each weight is inversely proportional to the expected value of the norm of the correspondent filter error, a quantity determinable thanks to the employment of the Bayesian approach. The performance of the new approach is shown on both simulated and real auditory EP. A signal-to-noise ratio enhancement is obtained that can allow the (possibly automatic) identification of peak latencies and amplitudes with less sweeps than those required by CA. For cochlear EP, the method also allows the audiology investigator to gather new and clinically important information. The possibility of handling single-sweep analysis with further development of the method is also addressed.