Millions of people worldwide are affected by arrhythmias. Arrhythmias are abnormal activity of the heart functioning. Some arrhythmias are harmful to the heart and can cause sudden mortality. The electrocardiogram (ECG) is a significant tool in cardiology for the diagnosis of arrhythmia beats. Computer-aided diagnosis (CAD) systems have been proposed in several studies to automatically classify different types of arrhythmias from ECG signals. To improve the classification of arrhythmias, a new end-to-end feature learning and classification model has been developed. This work focuses on the implementation of a one-dimensional convolution neural network (1D-CNN) model based on an auto-encoder convolution network (ACN) that learned the best ECG features from eachheartbeat window. After that, we applied a Support Vector Machine (SVM) classifier for auto-encode features in order to detect the four different types of arrhythmic beats, including normal beats. These arrhythmia beats are left bundle branch block (L), right bundle branch block (R), paced beats (P), and premature ventricular contractions (V). using the MIT-BIH arrhythmia database. The statistical performance of the model is evaluated using tenfold crossvalidation strategies and obtained as an overall accuracy of 98.84%, average accuracy of 99.53%, sensitivity of 98.24% and precision of 97.58%, respectively. This model has presents better results than other state-of-the-art models. Therefore, this approach may also help in clinical heart care systems.

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Millions of people worldwide are affected by arrhythmias. Arrhythmias are abnormal activity of the heart functioning. Some arrhythmias are harmful to the heart and can cause sudden mortality. The electrocardiogram (ECG) is a significant tool in cardiology for the diagnosis of arrhythmia beats. Computer-aided diagnosis (CAD) systems have been proposed in several studies to automatically classify different types of arrhythmias from ECG signals. To improve the classification of arrhythmias, a new end-to-end feature learning and classification model has been developed. This work focuses on the implementation of a one-dimensional convolution neural network (1D-CNN) model based on an auto-encoder convolution network (ACN) that learned the best ECG features from each heartbeat window. After that, we applied a Support Vector Machine (SVM) classifier for auto-encode features in order to detect the four different types of arrhythmic beats, including normal beats. These arrhythmia beats are left bundle branch block (L), right bundle branch block (R), paced beats (P), and premature ventricular contractions (V). using the MIT-BIH arrhythmia database. The statistical performance of the model is evaluated using tenfold cross-validation strategies and obtained as an overall accuracy of 98.84%, average accuracy of 99.53%, sensitivity of 98.24% and precision of 97.58%, respectively. This model has presents better results than other state-of-the-art models. Therefore, this approach may also help in clinical heart care systems.

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The stationary hierarchical network faces considerable challenges from hotspots and faster network breakdowns, especially in smart monitoring applications. As a solution to this issue, mobile sinks were recommended since they are associated with huge and balanced ways to transfer data and energy across the network. Again, due to the mobile sink node advertisement around the network latency and the energy utilization overheads introduced across the network, ring routing reduces the control overhead while preserving the benefits of the mobile sink, thereby optimizing the energy and improving the network life span. Consequently, we suggested a novel, distributed advanced ring routing strategy, in this work, for the mobile wireless sensor network. Extensive simulations and performance evaluation, in comparison to previous distributed mobile approaches, reveal a 37% and 40% boost in the network throughput and end-to end delay, respectively. Additionally, the lifespan of a network is determined by the control overhead and energy demand.