Deep Learning (DL) has recently become a topic of study in different applications including
healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a�…

In the era of Internet-of-things, many things can stay connected; however, biological
systems, including those necessary for human health, remain unable to stay connected to�…

This article presents a new deep learning approach for cardiac arrhythmia (17 classes)
detection based on long-duration electrocardiography (ECG) signal analysis�…

In the recent decade, deep learning, a subset of artificial intelligence and machine learning,
has been used to identify patterns in big healthcare datasets for disease phenotyping, event�…

The electrocardiogram (ECG) signal basically corresponds to the electrical activity of the
heart. In the literature, the ECG signal has been analyzed and utilized for various purposes�…

This newly inaugurated research database for 12-lead electrocardiogram signals was
created under the auspices of Chapman University and Shaoxing People's Hospital�…

Feature extraction and representation has significant impact on the performance of any
machine learning method. Mel Frequency Cepstrum Coefficient (MFCC) is designed to�…

Electrocardiogram (ECG) recordings are indicative for the state of the human heart.
Automatic analysis of these recordings can be performed using various computational�…

Learning temporal patterns in time series remains a challenging task up until today.
Particularly for anomaly detection in time series, it is essential to learn the underlying�…

Physiological signal processing plays a key role in next-generation human-machine
interfaces as physiological signals provide rich cognition-and health-related information�…