Results 21 to 25 of about 154 (25)

Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction [PDF]

arXiv, 2019
Our study concerns with automated predicting of congestive heart failure (CHF) through the analysis of electrocardiography (ECG) signals. A novel machine learning approach, regularized hessenberg decomposition based extreme learning machine (R-HessELM), and feature models; squared, circled, inclined and grid entropy measurement were introduced and used
arxiv  

Spatio-Temporal Correlation of Epileptic Seizures with The Electrocardiography Brain Perfusion Index [PDF]

arXiv
The Electrocardiography Brain Perfusion index (EBPi) is a novel electrocardiography (ECG)-based metric that may function as a proxy for cerebral blood flow (CBF). We investigated the spatio-temporal correlation between EBPi and epileptic seizure events.
arxiv  

Pre-Hospital Management of Acute Myocardial Infarction Using Tele-Electrocardiography System [PDF]

arXiv, 2018
A comprehensive survey revealed that many patients of Acute Myocardial Infarction reach to the hospital so late to deliver an effective treatment. This leads to poor treatment outcome which increases the mortality rates. Multiple reasons such as lack of diagnostic facilities in the rural health care centers and absence of cardiologists in Emergency ...
arxiv  

Enhancing Electrocardiography Data Classification Confidence: A Robust Gaussian Process Approach (MuyGPs) [PDF]

arXiv
Analyzing electrocardiography (ECG) data is essential for diagnosing and monitoring various heart diseases. The clinical adoption of automated methods requires accurate confidence measurements, which are largely absent from existing classification methods. In this paper, we present a robust Gaussian Process classification hyperparameter training model (
arxiv  

PiEEG-16 to Measure 16 EEG Channels with Raspberry Pi for Brain-Computer Interfaces and EEG devices [PDF]

arXiv
This article introduces a cost-effective gateway into the fascinating world of neuroscience: the PIEEG-16, a versatile shield for RaspberryPi designed to measure 16 channels of various biosignals, including EEG (electroencephalography), EMG (electromyography), and ECG (electrocardiography) without any data transfer over the network (Wi-Fi, Bluetooth ...
arxiv