Method for automatic detection and classification of N1 and P2 auditory evoked potentials in EEG recordings

The current averaging method for event related evoked potentials does not allow to study the potentials' variability, thus a detection algorithm that allows to recognise the potentials elicited by each stimulus is desirable. This paper compares Neural Networks and Supported Vector Machines as classifiers to be integrated to an automated detection algorithm. EEG recordings of 5 subjects (3 female, 2 male) were made, while auditory stimulus was provided. The recordings were filtered with a 2nd order Butterworth lowpass filter with cutoff frequency at 40 Hz. A free database provided by California Institute of Technology with several auditory evoked potential (EP) events was used to train the models used to identify de N100-P200 complex. Once a model was achieved, it was first validated with new data, and then incorporated to an algorithm that identified the complex in a non segmented EEG recording. An average sensitivity of 93.26% was achieved with only 136 false positives in over 25 minutes of 6 channel EEG recording. These results prove that individual detection of EPs is possible, thus enabling future studies in variability.

[1]  O. Longe,et al.  Auditory evoked potentials to abrupt pitch and timbre change of complex tones: electrophysiological evidence of 'streaming'? , 1998, Electroencephalography and clinical neurophysiology.

[2]  John D. Enderle,et al.  Introduction to Biomedical Engineering , 1999 .

[3]  J.R. Boston,et al.  Automated interpretation of brainstem auditory evoked potentials: a prototype system , 1989, IEEE Transactions on Biomedical Engineering.

[4]  R. Näätänen,et al.  The mismatch negativity (MMN) in basic research of central auditory processing: A review , 2007, Clinical Neurophysiology.

[5]  S. Koelsch,et al.  Electric brain responses to inappropriate harmonies during listening to expressive music , 2002, Clinical Neurophysiology.

[6]  Pierre J. M. Cluitmans,et al.  Clinical evaluation of a method for automatic detection and removal of artifacts in auditory evoked potential monitoring , 1995, Journal of Clinical Monitoring.

[7]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[8]  F Lolas,et al.  [Event-related cerebral potentials. Classification and clinical use]. , 1979, Arquivos de neuro-psiquiatria.

[9]  J. Webster.  Encyclopedia of Medical Devices and Instrumentation , 1988 .

[10]  M. Stokes,et al.  Auditory evoked potentials to spectro-temporal modulation of complex tones in normal subjects and patients with severe brain injury. , 2000, Brain : a journal of neurology.

[11]  B. Kotchoubey,et al.  Event-related potentials, cognition, and behavior: A biological approach , 2006, Neuroscience & Biobehavioral Reviews.

[12]  E. Uncuoğlu,et al.  Comparison of three backpropagation training algorithms for two case studies , 2010 .

[13]  S. Hillyard,et al.  Human auditory evoked potentials. I. Evaluation of components. , 1974, Electroencephalography and clinical neurophysiology.

[14]  A. Oxenham,et al.  Pitch, harmonicity and concurrent sound segregation: Psychoacoustical and neurophysiological findings , 2010, Hearing Research.

[15]  E. Maby,et al.  Analysis of auditory evoked potential parameters in the presence of radiofrequency fields using a support vector machines method , 2004, Medical and Biological Engineering and Computing.

[16]  O. Kisi,et al.  Comparison of three back-propagation training algorithms for two case studies , 2005 .

[17]  Heinz Ulrich Hoppe,et al.  An automatic sequential recognition method for cortical auditory evoked potentials , 2001, IEEE Transactions on Biomedical Engineering.

[18]  Nurettin Acir,et al.  Automatic classification of auditory brainstem responses using SVM-based feature selection algorithm for threshold detection , 2006, Eng. Appl. Artif. Intell..

[19]  R Sánchez,et al.  Automatic detection of auditory brainstem responses using feature vectors. , 1995, International journal of bio-medical computing.

[20]  Michael Döllinger,et al.  Statistical detection and analysis of mismatch negativity derived by a multi-deviant design from normal hearing children , 2009, Hearing Research.

[21]  Gudrun Stockmanns,et al.  Auditory evoked potentials for the assessment of depth of anaesthesia: different configurations of artefact detection algorithms , 2007, Biomedizinische Technik. Biomedical engineering.