Novel DERMA fusion technique for ECG heartbeat classification

An electrocardiogram (ECG) consists of five types of different waveforms or characteristics (P, QRS, and T) that represent electrical activity within the heart. Identification of time intervals and morphological appearance of the waves are the major measuring instruments to detect cardiac abnormality from ECG signals. The focus of this study is to classify five different types of heartbeats, including premature ventricular contraction (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), PACE, and atrial premature contraction (APC), to identify the exact condition of the heart. Prior to the classification, extensive experiments on feature extraction were performed to identify the specific events from ECG signals, such as P, QRS complex, and T waves. This study proposed the fusion technique, dual event-related moving average (DERMA) with the fractional Fourier-transform algorithm (FrlFT) to identify the abnormal and normal morphological events of the ECG signals. The purpose of the DERMA fusion technique is to analyze certain areas of interest in ECG peaks to identify the desired location, whereas FrlFT analyzes the ECG waveform using a time-frequency plane. Furthermore, detected highest and lowest components of the ECG signal such as peaks, the time interval between the peaks, and other necessary parameters were utilized to develop an automatic model. In the last stage of the experiment, two supervised learning models, namely support vector machine and K-nearest neighbor, were trained to classify the cardiac condition from ECG signals. Moreover, two types of datasets were used in this experiment, specifically MIT-BIH Arrhythmia with 48 subjects and the newly disclosed Shaoxing and Ningbo People’s Hospital (SPNH) database, which contains over 10,000 patients. The performance of the experimental setup produced overwhelming results, which show around 99.99% accuracy, 99.96% sensitivity, and 99.9% specificity

Inhibition of Enterovirus 71 (EV-71) Infections by a Novel Antiviral Peptide Derived from EV-71 Capsid Protein VP1

Enterovirus 71 (EV-71) is the main causative agent of hand, foot and mouth disease (HFMD). In recent years, EV-71 infections were reported to cause high fatalities and severe neurological complications in Asia. Currently, no effective antiviral or vaccine is available to treat or prevent EV-71 infection. In this study, we have discovered a synthetic peptide which could be developed as a potential antiviral for inhibition of EV-71. Ninety five synthetic peptides (15-mers) overlapping the entire EV-71 capsid protein, VP1, were chemically synthesized and tested for antiviral properties against EV-71 in human Rhabdomyosarcoma (RD) cells. One peptide, SP40, was found to significantly reduce cytopathic effects of all representative EV-71 strains from genotypes A, B and C tested, with IC50 values ranging from 6–9.3 µM in RD cells. The in vitro inhibitory effect of SP40 exhibited a dose dependent concentration corresponding to a decrease in infectious viral particles, total viral RNA and the levels of VP1 protein. The antiviral activity of SP40 peptide was not restricted to a specific cell line as inhibition of EV-71 was observed in RD, HeLa, HT-29 and Vero cells. Besides inhibition of EV-71, it also had antiviral activities against CV-A16 and poliovirus type 1 in cell culture. Mechanism of action studies suggested that the SP40 peptide was not virucidal but was able to block viral attachment to the RD cells. Substitutions of arginine and lysine residues with alanine in the SP40 peptide at positions R3A, R4A, K5A and R13A were found to significantly decrease antiviral activities, implying the importance of positively charged amino acids for the antiviral activities. The data demonstrated the potential and feasibility of SP40 as a broad spectrum antiviral agent against EV-71