Introduction: Brain visual evoked potential (VEP) signals are commonly known to be accompanied by high levels of background noise typically from the spontaneous background brain activity of electroencephalography (EEG) signals. Material and Methods: A model based on dyadic filter bank, discrete wavelet transform (DWT), and singular value decomposition (SVD) was developed to analyze the raw data of visual evoked potentials and extract time-locked signals with external visual stimulation. A bio-amplifier (iERG 100P) and data acquisition system (OMB-DAQ-3000) were utilized to record EEG raw data from the human scalp. MATLAB Data Acquisition Toolbox, Wavelet Toolbox, and Simulink model were employed to analyze EEG signals and extract VEP responses. Results: Results were verified in Simulink environment for the real recorded EEG data. The proposed model allowed precise decomposition and classification of VEP signals through the combined operation of DWT and SVD. DWT was successfully used for the decomposition of VEP signals to different frequencies followed by SVD for Feature extraction and classification. Conclusion: The visual and quantitative results indicated that the impact of the proposed technique of combining DWT and SVD was promising. Combining the two techniques led to a two-fold increase in the impact of peak signal to noise ratio of all the tested signals compared to using each technique individually.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent Features. Multi-label Feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local search strategy has been proposed to select a set of prominent Features. To this aim, Features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group Features have more similarity to class and less similarity to other Features, and the second is redundant and less relevant Features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant Features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

Background: Tetralogy of Fallot (TOF) refers to a condition in which left ventricular volume is normal or slightly less than normal. Given the differences observed in some Asian patients with TOF، the present study was conducted to investigate left heart by determining Z-scores for the mitral valve in Iranian patients with TOF. Methods: Eligible subjects in this prospective descriptive study comprised all patients with TOF presenting to Shahid Modarres Hospital in Tehran from March 2012 to March 2015 and diagnosed as the candidates for surgery. After determining the need for surgery and the therapeutic method required، the mitral valve size and Z-scores were calculated. We analyzed sex، age، body surface area (BSA)، mitral size (in 2-chamber and 4-chamber view)، mitral Z-score، other cardiac anomalies، number of surgery and previous surgery. Results: Of a total of 80 patients included in the study over 3 years، 29 (36. 3%) were male and 51 (63. 8%) were female. The mean age of the patients was 7. 15± 3. 37 years and their mitral size was found to be 10-27 mm (16. 2± 5. 99 mm) using echocardiography. Z-scores of the mitral valve were also obtained as-3. 09± 2. 11، ranging between-7. 1 SD and +1. 3 SD. 58 (72. 5%) patients have only TOF and 22 (27. 5%) with other cardiac anomalies. 45 patients were for first time underwent surgery and 22 patients for second time، 12 patients for three time and 1 for forth. Of a total of 35 patients had previous surgery، the most common were shunt 15 (42. 9%) and then tetralogy of Fallot total correction (TFTC) in 12 (34. 3%). Of a total of 80 patients، 59 (73. 8%) underwent TFTC، 17 (21. 3%) under pulmonary valve replacement and 4 (5%) shunt. Conclusion: Z-scores of the mitral valve were found to be significantly below the normal value، (i. e. 0± 2 SD)، in the study patients، suggesting the risk of hypoplastic left heart syndrome in Iranian patients with TOF، nevertheless، the type of TOF examined in these patients might have been different from those observed in other races and regions.