Automatic and continuous assessment of ERPs for mismatch negativity detection

Accurate and fast detection of event related potential (ERP) components is an unresolved issue in neuroscience and critical health care. Mismatch negativity (MMN) is a component of the ERP to an odd stimulus in a sequence of identical stimuli which has good correlation with coma awakening. All of the previous studies for MMN detection are based on visual inspection of the averaged ERPs (over a long recording time) by a skilled neurophysiologist. However, in practical situations, such an expert may not be available or familiar with all aspects of evoked potential methods. Further, we may miss important clinically essential events due to the implicit averaging process used to acquire the ERPs. In this paper we propose a practical machine learning approach for automatic and continuous assessment of the ERPs for detecting the presence of the MMN component. The proposed method is realized in a classification framework. Performance of the proposed method is demonstrated on 22 healthy subjects through a leave-one subject-out strategy where the MMN components are identified with about 93% accuracy.

[1]  Dominique Morlet,et al.  Novelty P3 elicited by the subject’s own name in comatose patients , 2008, Clinical Neurophysiology.

[2]  Sinisa Todorovic,et al.  Local-Learning-Based Feature Selection for High-Dimensional Data Analysis , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Qiang Cheng,et al.  The Fisher-Markov Selector: Fast Selecting Maximally Separable Feature Subset for Multiclass Classification with Applications to High-Dimensional Data , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  C. Escera,et al.  The individual replicability of mismatch negativity at short and long inter-stimulus intervals , 2000, Clinical Neurophysiology.

[5]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  D Morlet,et al.  Mismatch negativity and late auditory evoked potentials in comatose patients , 1999, Clinical Neurophysiology.

[7]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Claude Delpuech,et al.  Brain responses to a subject's own name uttered by a familiar voice , 2006, Brain Research.

[9]  Majid Komeili,et al.  Local Feature Selection for Data Classification , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Jane Labadin,et al.  Feature selection based on mutual information , 2015, 2015 9th International Conference on IT in Asia (CITA).

[11]  Henry L. Lew,et al.  Predictive value of somatosensory evoked potentials for awakening from coma* , 2003, Critical care medicine.

[12]  Dominique Morlet,et al.  MMN and Novelty P3 in Coma and Other Altered States of Consciousness: A Review , 2013, Brain Topography.

[13]  C. Fischer,et al.  Improved prediction of awakening or nonawakening from severe anoxic coma using tree-based classification analysis* , 2006, Critical care medicine.

[14]  C. C. Duncan,et al.  Event-related potentials in clinical research: Guidelines for eliciting, recording, and quantifying mismatch negativity, P300, and N400 , 2009, Clinical Neurophysiology.