Synthetic photoplethysmogram (PPG) signal generation using a genetic programming-based generative model.

Nowadays, photoplethysmograph (PPG) technology is being used more often in smart devices and mobile phones due to advancements in information and communication technology in the health field, particularly in monitoring cardiac activities. Developing generative models to generate synthetic PPG signals requires overcoming challenges like data diversity and limited data available for training deep learning models. This paper proposes a generative model by adopting a genetic programming (GP) approach to generate increasingly diversified and accurate data using an initial PPG signal sample.

Fault tolerance challenges in wearable computing for vital applications: a survey.

Wearable computers can be used in different domains including healthcare. However, due to suffering from challenges such as faults their applications may be limited in real practice. So, in designing wearable devices, designer must take into account fault tolerance techniques.

Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac leads.

One of the most common causes of death worldwide is heart disease, including arrhythmia. Today, sciences such as artificial intelligence and medical statistics are looking for methods and models for correct and automatic diagnosis of cardiac arrhythmia. In pursuit of increasing the accuracy of automated methods, many studies have been conducted.

A novel method for modeling effective connections between brain regions based on EEG signals and graph neural networks for motor imagery detection.

Classified as biomedical signal processing, cerebral signal processing plays a key role in human-computer interaction (HCI) and medical diagnosis. The motor imagery (MI) problem is an important research area in this field. Accurate solutions to this problem will greatly affect real-world applications.

Joint learning method with teacher-student knowledge distillation for on-device breast cancer image classification.

The deep learning models such as AlexNet, VGG, and ResNet achieved a good performance in classifying the breast cancer histopathological images in BreakHis dataset. However, these models are not practically appropriate due to their computational complexity and too many parameters; as a result, they are rarely utilized on devices with limited computational resources. This paper develops a lightweight learning model based on knowledge distillation to classify the histopathological images of breast cancer in BreakHis.

Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals.

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.

Overcoming limitation of dissociation between MD and MI classifications of breast cancer histopathological images through a novel decomposed feature-based knowledge distillation method.

Magnification-independent (MI) classification is considered a promising method for detecting the histopathological images of breast cancer. However, it has too many parameters for real implementation due to dependence on input images in different magnification factors. In addition, magnification-dependent (MD) classification usually performs poorly on unseen samples, although it has lower input image sizes and fewer parameters.

Profile-based assessment of diseases affective factors using fuzzy association rule mining approach: A case study in heart diseases.

The existing data mining solutions to identify risk factors associated with diseases are burdened with quite a few shortcomings. They usually use crisp partitions for numerical features and also do not use patient-specific profiles. These shortcomings create limitations for solving real problems.

EEG electrode selection for person identification thru a genetic-algorithm method.

New biometric identification techniques are continually being developed to meet various applications. Electroencephalography (EEG) signals may provide a reasonable option for this type of identification due its unique features that overcome the lacks of other common methods. Currently, however, the processing load for such signals requires considerable time and labor.

A new near-lossless EEG compression method using ANN-based reconstruction technique.

Compression algorithm is an essential part of Telemedicine systems, to store and transmit large amount of medical signals. Most of existing compression methods utilize fixed transforms such as discrete cosine transform (DCT) and wavelet and usually cannot efficiently extract signal redundancy especially for non-stationary signals such as electroencephalogram (EEG). In this paper, we first propose learning-based adaptive transform using combination of DCT and artificial neural network (ANN) reconstruction technique.

Evolutionary Metric-Learning-Based Recognition Algorithm for Online Isolated Persian/Arabic Characters, Reconstructed Using Inertial Pen Signals.

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.

Toward a Fault Tolerant Architecture for Vital Medical-Based Wearable Computing.

Advancements in computers and electronic technologies have led to the emergence of a new generation of efficient small intelligent systems. The products of such technologies might include Smartphones and wearable devices, which have attracted the attention of medical applications. These products are used less in critical medical applications because of their resource constraint and failure sensitivity.

Automatic vessel network features quantification using local vessel pattern operator.

Automatic measurement and quantification of blood vessels' features and detection of vessel landmarks are key steps in the computer-aided diagnosis and diseases monitoring. This work proposes a novel and robust method for detecting vessel landmarks, i.e.