Multi-stimuli multi-channel data and decision fusion strategies for dyslexia prediction using neonatal ERPs

Data fusion and decision fusion classification strategies are introduced to predict dyslexia from multi-channel event related potentials (ERPs) recorded, at birth, in response to multiple stimuli. Two data and two decision fusion strategies are developed in conjunction with nearest-mean classification rank selection to classify multi-stimuli multi-channel (MSMC) ERPs. The fusion vector in the data fusion strategy is formed by directly combining the rank-ordered MSMC ERP vectors or the rank-ordered elements of the MSMC ERPS. The resulting fusion vector is classified using a vector nearest-mean classifier. The nearest-mean classification decisions of the rank-ordered MSMC ERP vectors or the rank-ordered MSMC ERP elements are combined into a fusion vector in the decision fusion strategy. The resulting decision fusion vector is classified using a discrete Bayes classifier. The MSMC fusion classification strategies are tested on the averaged ERPs recorded at birth of 48 children: 17 identified as dyslexic readers, 7 as poor readers, and 24 identified as normal readers at 8 years of age. The ERPs were recorded at 6 electrode sites in response to two speech sounds and two non-speech sounds. It is shown that through the MSMC ERP element decision fusion strategy, dyslexic readers and poor readers can be predicted with almost 100% accuracy. Consequently, future reading problems can be detected early using neonatal responses making it possible to introduce more effective interventions earlier to children with reading problems emerging later in their lives. Furthermore, it is noted that because of the generalized formulations, the fusion strategies introduced can be applied, in general, to problems involving the classification of multi-category multi-sensor signals.