Narrow Results

Reading requires the integration of several central cognitive subsystems, ranging from attention and oculomotor control to word identification and language comprehension. Reading saccades and fixations contain information that can be correlated with word properties. When reading a sentence, the brain must decide where to direct the next saccade according to what has been read up to the actual fixation. In this process, the retrieval memory brings information about the current word features and attributes into working memory. According to this information, the prefrontal cortex predicts and triggers the next saccade. The frequency and cloze predictability of the fixated word, the preceding words and the upcoming ones affect when and where the eyes will move next. In this paper we present a diagnostic technique for early stage cognitive impairment detection by analyzing eye movements during reading proverbs. We performed a case-control study involving 20 patients with probable Alzheimer's disease and 40 age-matched, healthy control patients. The measurements were analyzed using linear mixed-effects models, revealing that eye movement behavior while reading can provide valuable information about whether a person is cognitively impaired. To the best of our knowledge, this is the first study using word-based properties, proverbs and linear mixed-effect models for identifying cognitive abnormalities.

Alzheimer's disease (AD) is the deterioration of cognitive functions such as problem-solving, memory and reasoning that interferes with an individual's daily functioning. Symptoms are exhibited in accordance to the affected brain area (e.g. language and learning). Magnetic Resonance Imaging is the best diagnostic method to monitor the damage in brain tissues. Therefore, this paper proposed a hybrid method to segregate between the MRIs of healthy subjects and those diagnosed with AD. In this combined method, around 70 MRIs collected from local hospitals of Jordan are analyzed based on the use of Low-pass morphological filters and equalizer filter. Discrete wavelet transform (DWT) described as mathematical functions that discriminate data into different frequency components, and then each component is considered as resolution — scale matching. In this work, we used the best DWT type namely Haar because it is satisfactory for most of applications of image processing. The DWT then applied on the same MRIs where the entropies and energy values were extracted from each sub-band of the third-level wavelet coefficients to be fed into adaptive neuro-fuzzy inference system (ANFIS) classifier. The ANFIS was trained with the back-propagation gradient descent method in combination with least squares method. The combined method has demonstrated the best classification accuracy of 93% which can be reliably used for diagnosis purposes.