In this article, we propose a lightweight and competitively accurate heart rhythm abnormality
classification model based on classical convolutional neural networks in deep neural …

Electrocardiogram (ECG) classification is an important process in identifying arrhythmia, and
neural network models have been widely used in this field. However, these models are often …

Although convolutional neural networks (CNNs) can be used to classify electrocardiogram
(ECG) beats in the diagnosis of cardiovascular disease, ECG signals are typically processed …

Representation of a signal in a sparse way is a useful and popular methodology in signal-
processing applications. Among several widely used sparse transforms, dictionary learning …

Body sensor network (BSN) is largely utilized in IoMT to attain easier access of patient's data
remotely without much cost by connecting the various bio-sensors. However, there is a …

Background Automatic sleep stage classification is essential for long-term sleep monitoring.
Wearable devices show more advantages than polysomnography for home use. In this …

There are various kinds of brain monitoring techniques, including local field potential, near-
infrared spectroscopy, magnetic resonance imaging (MRI), positron emission tomography …

Arrhythmia is a cardiac condition generated by the abnormal electrical activity of the heart,
and an electrocardiogram (ECG) is a tool utilized by the cardiologists for determining the …

An emerging research direction considers the inverse problem of inferring
electrocardiogram (ECG) from photoplethysmogram (PPG) to bring about the synergy …

Holter systems record the electrocardiogram (ECG), which is used to identify beat families
according to their origin and severity. Many systems have been proposed using signal …