The effect of a students led caregiving support program on depression, sleep quality and quality of life of the caregivers and patients with stroke.

Purpose: We aimed to provide care support to stroke patients and their caregivers and investigate this support's impact on the psychosocial characteristics of patients', caregivers', and volunteer students' depression, quality of life, and sleep quality.

Material/methods: Volunteer students received caregiving training and provided support to caregivers at patients' homes. Caregivers received care support through the project for four sessions, once a week.

Deep Learning on Computerized Analysis of Chronic Obstructive Pulmonary Disease.

Goal: Chronic obstructive pulmonary disease (COPD) is one of the deadliest diseases in the world. Because COPD is an incurable disease and requires considerable time to be diagnosed even by an experienced specialist, it becomes important to provide analysis abnormalities in simple ways. The aim of the study is comparing multiple machine learning algorithms for the early diagnosis of COPD using multi-channel lung sounds.

ECG based human identification using Second Order Difference Plots.

Background And Objective: ECG is one of the biometric signals that has been studied in peer-reviewed over past years. The developments on the signal analysis methods show that the studies on the ECG would continue unabatedly. It has a common use on cardiac diseases with high rates of classification performances by integrating it with signal analysis methods.

A new approach to early diagnosis of congestive heart failure disease by using Hilbert-Huang transform.

Congestive heart failure (CHF) is a degree of cardiac disease occurring as a result of the heart's inability to pump enough blood for the human body. In recent studies, coronary artery disease (CAD) is accepted as the most important cause of CHF. This study focuses on the diagnosis of both the CHF and the CAD.

Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients.

This paper describes feature extraction methods using higher order statistics (HOS) of wavelet packet decomposition (WPD) coefficients for the purpose of automatic heartbeat recognition. The method consists of three stages. First, the wavelet package coefficients (WPC) are calculated for each different type of ECG beat.

A multi-stage automatic arrhythmia recognition and classification system.

This paper describes an automatic classification system based on combination of diverse features for the purpose of automatic heartbeat recognition. The method consists of three stages. At the first stage, heartbeats are classified into 5 main groups defined by AAMI using optimal feature sets for each main group.