Human activity recognition systems are developed as part of a framework to enable
continuous monitoring of human behaviours in the area of ambient assisted living, sports …

Recent technological advancements in data acquisition tools allowed life scientists to
acquire multimodal data from different biological application domains. Categorized in three …

Rapid advances in hardware-based technologies during the past decades have opened up
new possibilities for life scientists to gather multimodal data in various application domains …

Activity detection and classification using different sensor modalities have emerged as
revolutionary technology for real-time and autonomous monitoring in behaviour analysis …

We present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-
inspired machine learning approach known as Echo State Networks. Our classifier has a low …

This paper introduces a novel deep learning-based algorithm that integrates a long short-
term memory (LSTM)-based auto-encoder (AE) network with support vector machine (SVM) …

Deep neural networks represent, nowadays, the most effective machine learning technology
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …

Electrocardiogram (ECG) serves as the gold standard for noninvasive diagnosis of several
types of heart disorders. In this study, a novel hybrid approach of deep neural network …

Timely prediction of cardiovascular diseases with the help of a computer-aided diagnosis
system minimizes the mortality rate of cardiac disease patients. Cardiac arrhythmia detection …

The success of deep learning over the traditional machine learning techniques in handling
artificial intelligence application tasks such as image processing, computer vision, object …