Cardiovascular diseases, like arrhythmia, as the leading causes of death in the world, can be automatically diagnosed using an electrocardiogram (ECG). The ECG-based diagnostic has notably resulted in reducing human errors. The main aim of this study is to increase the accuracy of arrhythmia diagnosis and classify various types of arrhythmias in individuals (suffering from cardiovascular diseases) using a novel graph convolutional network (GCN) benefitting from mutual information (MI) indices extracted from the ECG leads. In this research, for the first time, the relationships of 12 ECG leads measured using MI as an adjacency matrix were illustrated by the developed GCN and included in the ECG-based diagnostic method. Cross-validation methods were applied to select both training and testing groups. The proposed methodology was validated in practice by applying it to the large ECG database, recently published by Chapman University. The GCN-MI structure with 15 layers was selected as the best model for the selected database, which illustrates a very high accuracy in classifying different types of rhythms. The classification indicators of sensitivity, precision, specificity, and accuracy for classifying heart rhythm type, using GCN-MI, were computed as 98.45%, 97.89%, 99.85%, and 99.71%, respectively. The results of the present study and its comparison with other studies showed that considering the MI index to measure the relationship between cardiac leads has led to the improvement of GCN performance for detecting and classifying the type of arrhythmias, in comparison to the existing methods. For example, the above classification indicators for the GCN with the identity adjacency matrix (or GCN-Id) were reported to be 68.24%, 72.83%, 95.24%, and 92.68%, respectively.

The development of sensors with the microelectromechanical systems technology expedites the emergence of new tools for human-computer interaction, such as inertial pens. These pens, which are used as writing tools, do not depend on a specific embedded hardware, and thus, they are inexpensive. Most of the available inertial pen character recognition approaches use the low-level features of inertial signals. This paper introduces a Persian/Arabic handwriting character recognition system for inertial-sensor-equipped pens. First, the motion trajectory of the inertial pen is reconstructed to estimate the position signals by using the theory of inertial navigation systems. The position signals are then used to extract high-level geometrical features. A new metric learning technique is then adopted to enhance the accuracy of character classification. To this end, a characteristic function is calculated for each character using a genetic programming algorithm. These functions form a metric kernel classifying all the characters. The experimental results show that the performance of the proposed method is superior to that of one of the state-of-the-art works in terms of recognizing Persian/Arabic handwriting characters.

Inertial sensors with microelectromechanical systems (MEMS) technology are an integral part of many modern electronic devices such as wearable medical products, which are inherently subject to memory, bandwidth, and energy constraints due to their size and purpose. One of the important challenges for the progress in this area is the storage, transmission, and processing of large quantities of inertial sensors signal. To address this issue, this paper presents a method for near-lossless compression of multi-axis inertial signals. To improve the inertial signal compression capability, the proposed compression method employs the Independent Component Analysis (ICA) method with a Principal Component Analysis (PCA) preprocessing step to extract independent components from the signals. A deep autoencoder is used to compress the independent components and later to estimate them in the reconstruction phase. The reconstruction error is also quantized and coded using arithmetic coding, and transmitted alongside the compressed components. This paper also proposes a new approach for improving the quality of the reconstructed signals. In this approach, on the receiver side, the reconstruction error is fed to the Madgwick filter as an external noise and is compensated using this filter. The experimental results demonstrate the high compression rate and low reconstruction error of the proposed method compared to the state-of-the-art methods.