An Effective Brain-Computer Interface System Based on the Optimal Timeframe Selection of Brain Signals

Background: Brain responds in a short timeframe (with certain delay) after the request for doing a motor imagery task and therefore it is most likely that the individual not focus continuously on the task at entire interval of data acquisition time or even think about other things in a very short time slice. In this paper, an effective brain-computer interface system is presented based on the optimal timeframe selection of brain signals.Methods: To prove the stated claim, various timeframes with different durations and delays selected based on a specific rule from EEG signals recorded during right/left hand motor imagery task and subsequently, feature extraction and classification are done.Results: Implementation results on the two well-known datasets termed Graz 2003 and Graz 2005; shows that the smallest systematically created timeframe of data acquisition interval have had the best results of classification. Using this smallest timeframe, the classification accuracy increased up to 91.43% for Graz 2003 and 88.96, 83.64 and 84.86 percent for O3, S4 and X11 subjects of Graz 2005 database respectively.Conclusion: Removing the additional information in which the individual does not focus on the motor imagery task and utilizing the most distinguishing timeframe of EEG signals that correctly interpret individual intentions improves the BCI system performance

Prevalence of the molar incisor hypomineralization in seven to twelve-year-old students of Kerman, Iran, in 2015-2016

BACKGROUND AND AIM: Regarding the prevalence of molar incisor hypomineralization (MIH) among students and different populations and continuation of related problems, it seems necessary to assess the prevalence among students in Kerman, Iran. The aim of this study was to review the prevalence of MIH and its relation to the sex of 7-12-year-old students in Kerman during 2015-2016. METHODS: In this cross-sectional study, 779 students from different schools of Kerman were studied after examination. We used a checklist to keep the record of MIH cases, which was filled by an inspector. Discolored (milky white or yellow and brown) teeth were counted as defective. Diagnosis of the MIH was done on basis of similar studies. After extracting the results, the data were analyzed by SPSS software, considering the sex of participants. The confidence interval (CI) of 95% was considered. RESULTS: The prevalence of MIH was 6.5% among the studied students (51 students were diagnosed). There was no significant relationship between MIH and the students’ sex. Among the 169 obviously defective teeth, the most prevalence was for lower right molar (54.9%), upper right central (52.9%), and lower left molar (49.0%). CONCLUSION: Although the prevalence of MIH among the students was relatively low, it seems that awareness among the students and their parents’ needs to be enhanced so that they can take better actions for the treatment of the defective teeth. KEYWORDS: Molar-Incisor; Hypomineralization; Molar Incisor Hypomineralization; Prevalence; Enamel Defect

Electrocardiogram based identification using a new effective intelligent selection of fused features

Over the years, the feasibility of using Electrocardiogram (ECG) signal for human identification issue has been investigated, and some methods have been suggested. In this research, a new effective intelligent feature selection method from ECG signals has been proposed. This method is developed in such a way that it is able to select important features that are necessary for identification using analysis of the ECG signals. For this purpose, after ECG signal preprocessing, its characterizing features were extracted and then compressed using the cosine transform. The more effective features in the identification, among the characterizing features, are selected using a combination of the genetic algorithm and artificial neural networks. The proposed method was tested on three public ECG databases, namely, MIT-BIH Arrhythmias Database, MITBIH Normal Sinus Rhythm Database and The European ST-T Database, in order to evaluate the proposed subject identification method on normal ECG signals as well as ECG signals with arrhythmias. Identification rates of 99.89% and 99.84% and 99.99% are obtained for these databases respectively. The proposed algorithm exhibits remarkable identification accuracies not only with normal ECG signals, but also in the presence of various arrhythmias. Simulation results showed that the proposed method despite the low number of selected features has a high performance in identification task

Antibiotic use during the first 6 months of COVID-19 pandemic in Iran : a large-scale multi-centre study

WHAT IS KNOWN AND OBJECTIVE: Although antibiotics are ineffective against viral infections, epidemiological studies have revealed that the COVID‐19 pandemic resulted in the overuse of antibiotics and disruption of antimicrobial stewardship programmes. We investigated the pattern of antibiotic use during the first 6 months of the COVID‐19 pandemic in Iran. METHODS: A multi‐centre retrospective study was designed to investigate the use of 16 broad‐spectrum antibiotics in 12 medical centres. The rate of antibiotic use was calculated and reported based on the Defined Daily Dose (DDD) per 100 hospital bed‐days. The bacterial co‐infection rate was also reported. RESULTS AND DISCUSSION: Totally, 43,791 hospitalized COVID‐19 patients were recruited in this study. It was found that 121.6 DDD of antibiotics were used per 100 hospital bed‐days, which estimated that each patient received approximately 1.21 DDDs of antibiotics every day. However, the bacterial co‐infections were detected only in 14.4% of the cases. A direct correlation was observed between the rate of antibiotic use and mortality (r[142] = 0.237, p = 0.004). The rate of antibiotic consumption was not significantly different between the ICU and non‐ICU settings (p = 0.15). WHAT IS NEW AND CONCLUSION: In this study, widespread antibiotic use was detected in the absence of the confirmed bacterial coinfection in COVID‐19 patients. This over‐consumption of broad‐spectrum antibiotics may be associated with increased mortality in hospitalized COVID‐19 patients, which can be an alarming finding