In this paper, an effective electrocardiogram (ECG) recurrence plot (RP)-based arrhythmia classification algorithm that can be implemented in portable devices is presented. Public databases from PhysioNet were used to conduct this study including the MIT-BIH Atrial Fibrillation Database, the MIT-BIH Arrhythmia Database, the MIT-BIH Malignant Ventricular Ectopy Database, and the Creighton University Ventricular Tachyarrhythmia Database. ECG time series were segmented and converted using an RP, and two-dimensional images were used as inputs to the CNN classifiers. In this study, two-stage classification is proposed to improve the accuracy. The ResNet-18 architecture was applied to detect ventricular fibrillation (VF) and noise during the first stage, whereas normal, atrial fibrillation, premature atrial contraction, and premature ventricular contractions were detected using ResNet-50 in the second stage. The method was evaluated using 5-fold cross-validation which improved the results when compared to previous studies, achieving first and second stage average accuracies of 97.21% and 98.36%, sensitivities of 96.49% and 97.92%, positive predictive values of 95.54% and 98.20%, and F1-scores of 95.96% and 98.05%, respectively. Furthermore, a 5-fold improvement in the memory requirement was achieved when compared with a previous study, making this classifier feasible for use in resource-constricted environments such as portable devices. Even though the method is successful, first stage training requires combining four different arrhythmia types into one label (other), which generates more data for the other category than for VF and noise, thus creating a data imbalance that affects the first stage performance.

Brain tumor is a mutation in the brain cells in which the cells keep dividing. The earlier the tumor detected, the higher survival rate for the patient. This study develops the brain tumor detection system by utilizing the you only look once (YOLO). The model is based on YOLOv5 architect. The open dataset of tumorous images is utilized. From this dataset, the corresponding masks are given alongside the images. Our study tries to compare several YOLOv5 models to localize the brain tumor. The results show YOLOv5m, YOLOv5l, and YOLOv5x models have higher precision and recall values. The inference time from those models is relatively small for recent computational resources. In conclusion, the YOLOv5 models have produced superior result in localizing the brain tumor

Pertanika Journal information and guidelines

Heart disease or cardiovascular disease is the deadliest disease in the world which claims about 17.9 million lives globally every year. To solve the issues, it