Abstract: Cognitive studies have suggested that anxiety is correlated with cognitive performance. Previous research has focused on the relationship between anxiety level and the perceptual load within the frontal region, such as the dorsolateral prefrontal and anterior cingulate cortices. High-anxious individuals are predicted to have worse performance on cognitively-demanding tasks requiring efficient cognitive processing. A few functional magnetic resonance imaging studies have specifically discussed the performance and brain activity involving working memory for high-anxious individuals. This topic has been further explored with electroencephalography, although these studies have mostly provided results involving visual face-related stimuli. In this study, we used auditory stimulation to manipulate the working memory load and attempted to interpret the deficiency of cognitive function in high-anxious participants or patients using functional near infrared spectroscopy (fNIRS). The fNIRS signals of 30 participants were measured while they were performing an auditory working memory task. For the auditory n-back task, there were three experimental conditions, including two n-back task conditions of stimuli memorization with different memory load and a condition of passive listening to the stimuli. Hemodynamic responses from frontal brain regions were recorded using a wireless fNIRS device. Brain activation from the ventrolateral and orbital prefrontal cortex were measured with signals filtered and artifacts removed. The fNIRS signals were then standardized with statistical testing and group analysis was performed. The results revealed that there were significantly stronger hemodynamic responses in the right ventrolateral and orbital prefrontal cortex when subjects were attending to the auditory working memory task with higher load. Furthermore, the right lateralization of the prefrontal cortex was negatively correlated with the level of state anxiety. This study revealed the possibility of incorporating fNIRS signals as an index to evaluate cognitive performance and mood states given its flexibility regarding portable applications compared to other neuroimaging techniques.

Abstract: An automatic method is presented for detecting myocardial ischemia, which can be considered as the early symptom of acute coronary events. Myocardial ischemia commonly manifests as ST- and T-wave changes on ECG signals. The methods in this study are proposed to detect abnormal ECG beats using knowledge-based features and classification methods. A novel classification method, sparse representation-based classification (SRC), is involved to improve the performance of the existing algorithms. A comparison was made between two classification methods, SRC and support-vector machine (SVM), using rule-based vectors as input feature space. The two methods are proposed with quantitative evaluation to validate their performances. The results of SRC method encompassed with rule-based features demonstrate higher sensitivity than that of SVM do. However, the specificity and precision are a trade-off. Moreover, SRC method is less dependent on the selection of rule-based features and can achieve high performance using fewer features. The overall performances of the two methods proposed in this study are better than the previous methods.