Heart rate variability for medical decision support systems: A review
Abstract Heart Rate Variability (HRV) is a good predictor of human health because the heart
rhythm is modulated by a wide range of physiological processes. This statement embodies …
rhythm is modulated by a wide range of physiological processes. This statement embodies …
Anomaly detection framework for wearables data: a perspective review on data concepts, data analysis algorithms and prospects
Wearable devices use sensors to evaluate physiological parameters, such as the heart rate,
pulse rate, number of steps taken, body fat and diet. The continuous monitoring of …
pulse rate, number of steps taken, body fat and diet. The continuous monitoring of …
Trends, technologies, and key challenges in smart and connected healthcare
Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at
an alarming rate, according to the American Heart Association's Heart Attack and Stroke …
an alarming rate, according to the American Heart Association's Heart Attack and Stroke …
Multifractal foundations of biomarker discovery for heart disease and stroke
M Mangalam, A Sadri, J Hayano, E Watanabe… - Scientific reports, 2023 - nature.com
Any reliable biomarker has to be specific, generalizable, and reproducible across
individuals and contexts. The exact values of such a biomarker must represent similar health …
individuals and contexts. The exact values of such a biomarker must represent similar health …
Artificial intelligence for cardiac diseases diagnosis and prediction using ECG images on embedded systems
L Mhamdi, O Dammak, F Cottin, IB Dhaou - Biomedicines, 2022 - mdpi.com
The electrocardiogram (ECG) provides essential information about various human cardiac
conditions. Several studies have investigated this topic in order to detect cardiac …
conditions. Several studies have investigated this topic in order to detect cardiac …
A fractal interpolation approach to improve neural network predictions for difficult time series data
S Raubitzek, T Neubauer - Expert Systems with Applications, 2021 - Elsevier
Deep Learning methods, such as Long Short-Term Memory (LSTM) neural networks prove
capable of predicting real-life time series data. Crucial for this technique to work is a …
capable of predicting real-life time series data. Crucial for this technique to work is a …
Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries
Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely
diagnosis of mTBI is crucial in making'go/no-go'decision in order to prevent repeated injury …
diagnosis of mTBI is crucial in making'go/no-go'decision in order to prevent repeated injury …
[PDF][PDF] Utilizing artificial intelligence to enhance health equity among patients with heart failure
AE Johnson, LPC Brewer, MR Echols, S Mazimba… - Heart failure clinics, 2022 - Elsevier
Patients with heart failure (HF) are heterogeneous with various intrapersonal and
interpersonal characteristics contributing to clinical outcomes. Bias, structural racism, and …
interpersonal characteristics contributing to clinical outcomes. Bias, structural racism, and …
Heart rate modeling and prediction using autoregressive models and deep learning
Physiological time series are affected by many factors, making them highly nonlinear and
nonstationary. As a consequence, heart rate time series are often considered difficult to …
nonstationary. As a consequence, heart rate time series are often considered difficult to …
A disentangled VAE-BILSTM model for heart rate anomaly detection
Cardiovascular diseases (CVDs) remain a leading cause of death globally. According to the
American Heart Association, approximately 19.1 million deaths were attributed to CVDs in …
American Heart Association, approximately 19.1 million deaths were attributed to CVDs in …