A survey on wearable sensor modality centred human activity recognition in health care
Increased life expectancy coupled with declining birth rates is leading to an aging
population structure. Aging-caused changes, such as physical or cognitive decline, could …
population structure. Aging-caused changes, such as physical or cognitive decline, could …
Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads
Identification and timely interpretation of changes occurring in the 12 electrocardiogram
(ECG) leads is crucial to identify the types of myocardial infarction (MI). However, manual …
(ECG) leads is crucial to identify the types of myocardial infarction (MI). However, manual …
Explainable AI decision model for ECG data of cardiac disorders
Electrocardiogram (ECG) data is used to monitor the electrical activity of the heart. It is
known that ECG data could help in detecting cardiac (heart) abnormalities. AI-enabled …
known that ECG data could help in detecting cardiac (heart) abnormalities. AI-enabled …
Electrodermal activity (EDA) for treatment of neurological and psychiatric disorder patients: a review
Electro-Dermal Activity (EDA) is the change of electrical properties of the skin with respect to
the sweat excretion, measured at the surface of the skin. By applying a small constant …
the sweat excretion, measured at the surface of the skin. By applying a small constant …
Diagnosis of multiclass tachycardia beats using recurrence quantification analysis and ensemble classifiers
Atrial Fibrillation (A-Fib), Atrial Flutter (AFL) and Ventricular Fibrillation (V-Fib) are fatal
cardiac abnormalities commonly affecting people in advanced age and have indication of …
cardiac abnormalities commonly affecting people in advanced age and have indication of …
Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques
Electrocardiogram (ECG) remains the most reliable and low-cost diagnostic tool to evaluate
the patients with cardiac arrhythmias. Manual diagnosis of arrhythmia beats is very tedious …
the patients with cardiac arrhythmias. Manual diagnosis of arrhythmia beats is very tedious …
A practical system based on CNN-BLSTM network for accurate classification of ECG heartbeats of MIT-BIH imbalanced dataset
A Shoughi, MB Dowlatshahi - 2021 26th international computer …, 2021 - ieeexplore.ieee.org
ECG beats have a key role in the reduction of fatality rate arising from cardiovascular
diseases (CVDs) by using Arrhythmia diagnosis computer-aided systems and get the …
diseases (CVDs) by using Arrhythmia diagnosis computer-aided systems and get the …
Classification of SSVEP signals using neural networks for BCI applications
RP Kumar, SS Vandana, D Tejaswi… - … and Computing for …, 2022 - ieeexplore.ieee.org
Brain-Computer-Interface (BCI) is an exceedingly growing field of research where individual
communicates to the computer, without physical connection. The natural responses to visual …
communicates to the computer, without physical connection. The natural responses to visual …
Automated diagnosis of Coronary Artery Disease using nonlinear features extracted from ECG signals
C Sridhar, UR Acharya, H Fujita… - 2016 IEEE International …, 2016 - ieeexplore.ieee.org
Coronary Artery Disease (CAD) is one of the hazardous heart disease which results in
angina, Myocardial Infarction (MI) and Sudden Cardiac Death (SCD). CAD is a cardiac …
angina, Myocardial Infarction (MI) and Sudden Cardiac Death (SCD). CAD is a cardiac …
Analysing the power of deep learning techniques over the traditional methods using medicare utilisation and provider data
ABSTRACT Deep Learning Technique (DLT) is the sub-branch of Machine Learning (ML)
which assists to learn the data in multiple levels of representation and abstraction and …
which assists to learn the data in multiple levels of representation and abstraction and …