A systematic review of Automated prediction of sudden cardiac deathusing ECG signals.

Sudden Cardiac Death (SCD) stands as a life-threatening cardiac event capable of swiftly claiming lives. Researchers have devised numerous models aimed at automatically predicting SCD through a combination of diverse feature extraction techniques and classifiers. We did a rigorous review of research publications ranging from 2011 to 2023, with a specific focus on the automated prediction of SCD, a growing health concern on a global scale.

Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert-Huang and wavelet transforms with explainable vision transformer and CNN models.

Background And Objective: Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. However, current methods predict SCD only a few minutes before its onset, limiting intervention time.

Automated explainable wavelet-based sleep scoring system for a population suspected with insomnia, apnea and periodic leg movement.

Sleep is an integral and vital component of human life, contributing significantly to overall health and well-being, but a considerable number of people worldwide experience sleep disorders. Sleep disorder diagnosis heavily depends on accurately classifying sleep stages. Traditionally, this classification has been performed manually by trained sleep technologists that visually inspect polysomnography records.

A systematic review on automatic identification of insomnia.

Insomnia is a prevalent sleep disorder characterized by difficulties in initiating sleep or experiencing non-restorative sleep. It is a multifaceted condition that impacts both the quantity and quality of an individual's sleep. Recent advancements in machine learning (ML), and deep learning (DL) have enabled automated sleep analysis using physiological signals.

A Study to Design a Learning Tool "Virtual Patient" for Functional Diagnosis and Clinical Reasoning of Respiratory Dysfunction in the Undergraduate Physiotherapy Curriculum.

The aim of the present study was to develop and design software-based "virtual patient" for learning functional diagnosis with clinical reasoning of respiratory dysfunction based on need analysis and perception of faculty and student on utility in the undergraduate physiotherapy curriculum. The objective of the study was to design a framework of a respiratory case scenario that includes personal details, history taking, physical examination, differential diagnosis, investigations, functional impairment, and diagnosis, design a prototype of the virtual patient case scenario using software in a virtual environment created in oculus quest, obtain faculty and student feedback, and analyze the feedback. The result of the study obtained on feedback analysis suggests that the virtual patient case scenario (prototype) contains the relevant information in an organized and sequenced manner.

An automatic sleep-scoring system in elderly women with osteoporosis fractures using frequency localized finite orthogonal quadrature Fejer Korovkin kernels.

Healthy sleep signifies a good physical and mental state of the body. However, factors such as inappropriate work schedules, medical complications, and others can make it difficult to get enough sleep, leading to various sleep disorders. The identification of these disorders requires sleep stage classification.

Automated classification of cyclic alternating pattern sleep phases in healthy and sleep-disordered subjects using convolutional neural network.

Sleep contributes to more than a third of a person's life, making sleep monitoring essential for overall well-being. Cyclic alternating patterns (CAP) are crucial in monitoring sleep quality and associated illnesses such as insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, etc. However, traditionally medical specialists practice manual division techniques of CAP phases which are sensitive to human weariness and inaccuracies.

Automated phase classification in cyclic alternating patterns in sleep stages using Wigner-Ville Distribution based features.

Sleep is one of the most important body mechanisms responsible for the proper functioning of human body. Cyclic alternating patterns (CAP) play an indispensable role in the analysis of sleep quality and related disorders like nocturnal front lobe epilepsy, insomnia, narcolepsy etc. The traditional manual segregation methods of CAP phases by the medical experts are prone to human fatigue and errors which may lead to inaccurate diagnosis of sleep stages.