Classification of electrocardiogram signals using deep learning based on genetic algorithm feature extraction.

Arrhythmias using electrocardiogram (ECG) signal is important in medical and computer research due to the timely diagnosis of dangerous cardiac conditions. The current study used the ECG to classify cardiac signals into normal heartbeats, congestive heart failure, ventricular arrhythmias, atrial fibrillation arrhythmias, atrial flutter, malignant ventricular arrhythmias, and premature atrial fibrillation. A deep learning algorithm was used to identify and diagnose cardiac arrhythmias.

A flexible energy management approach for smart healthcare on the internet of patients (IoP).

Considering the importance of biosensors on the Internet of the patient body that collect vital signs and transmit them to the coordinator, energy consumption and network lifetime are essential challenges in these networks. This paper, it has been tried to present a method based on adapting sampling rate through patient's risk and discovered pattern by employing an intelligence method based on adaptive neuro-fuzzy inference system, interpolation function, and a biosensor patron. It causes restricting sensed and transmitted data to the coordinator.

Gland Segmentation in Histopathology Images Using Deep Networks and Handcrafted Features.

Histopathology images contain essential information for medical diagnosis and prognosis of cancerous disease. Segmentation of glands in histopathology images is a primary step for analysis and diagnosis of an unhealthy patient. Due to the widespread application and the great success of deep neural networks in intelligent medical diagnosis and histopathology, we propose a modified version of LinkNet for gland segmentation and recognition of malignant cases.

Increasing the quality of reconstructed signal in compressive sensing utilizing Kronecker technique.

Quality of reconstruction of signals sampled using compressive sensing (CS) algorithm depends on the compression factor and the length of the measurement. A simple method to pre-process data before reconstruction of compressively sampled signals using Kronecker technique that improves the quality of recovery is proposed. This technique reduces the mutual coherence between the projection matrix and the sparsifying basis, leading to improved reconstruction of the compressed signal.

Reversible Image Watermarking for Health Informatics Systems Using Distortion Compensation in Wavelet Domain.

Reversible image watermarking guaranties restoration of both original cover and watermark logo from the watermarked image. Capacity and distortion of the image under reversible watermarking are two important parameters. In this study, a reversible watermarking is investigated by focusing on increasing the embedding capacity and reducing the distortion in medical images.