A Novel Approach for Atrial Fibrillation-related Obstructive Sleep Apnea Detection Using Enhanced Single-Lead ECG Features with Customized Deep Learning Algorithm.

Study Objectives: Atrial fibrillation (AF) and obstructive sleep apnea (OSA) are interrelated conditions that substantially increase the risk of cardiovascular complications. However, concurrent detection of these conditions remains a critical unmet need in clinical practice. Current home sleep apnea test (HSAT) devices often fail to detect arrhythmias essential for diagnosing OSA-associated AF due to limited ECG monitoring capabilities, and their integration with continuous positive airway pressure (CPAP) data for treatment optimization remains underutilized.

Sleep Apnea Classification Algorithm Development Using a Machine-Learning Framework and Bag-of-Features Derived from Electrocardiogram Spectrograms.

Background: Heart rate variability (HRV) and electrocardiogram (ECG)-derived respiration (EDR) have been used to detect sleep apnea (SA) for decades. The present study proposes an SA-detection algorithm using a machine-learning framework and bag-of-features (BoF) derived from an ECG spectrogram.

Methods: This study was verified using overnight ECG recordings from 83 subjects with an average apnea-hypopnea index (AHI) 29.

Implementation of a Deep Learning Algorithm Based on Vertical Ground Reaction Force Time-Frequency Features for the Detection and Severity Classification of Parkinson's Disease.

Conventional approaches to diagnosing Parkinson's disease (PD) and rating its severity level are based on medical specialists' clinical assessment of symptoms, which are subjective and can be inaccurate. These techniques are not very reliable, particularly in the early stages of the disease. A novel detection and severity classification algorithm using deep learning approaches was developed in this research to classify the PD severity level based on vertical ground reaction force (vGRF) signals.

Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time-Frequency Spectrogram and Deep Learning Neural Network Features.

A novel identification algorithm using a deep learning approach was developed in this study to classify neurodegenerative diseases (NDDs) based on the vertical ground reaction force (vGRF) signal. The irregularity of NDD vGRF signals caused by gait abnormalities can indicate different force pattern variations compared to a healthy control (HC). The main purpose of this research is to help physicians in the early detection of NDDs, efficient treatment planning, and monitoring of disease progression.

Evaluation of Vertical Ground Reaction Forces Pattern Visualization in Neurodegenerative Diseases Identification Using Deep Learning and Recurrence Plot Image Feature Extraction.

To diagnose neurodegenerative diseases (NDDs), physicians have been clinically evaluating symptoms. However, these symptoms are not very dependable-particularly in the early stages of the diseases. This study has therefore proposed a novel classification algorithm that uses a deep learning approach to classify NDDs based on the recurrence plot of gait vertical ground reaction force (vGRF) data.