Arrhythmias are anomalies in the heartbeat rhythm that occur occasionally in people's lives. These arrhythmias can lead to potentially deadly consequences, putting your life in jeopardy. As a result, arrhythmia identification and classification are a...
Pain is a reliable indicator of health issues; it affects patients' quality of life when not well managed. The current methods in the clinical application undergo biases and errors; moreover, such methods do not facilitate continuous pain monitoring....
Stroke is the second leading cause of death globally after ischemic heart disease, also a risk factor of cardioembolic stroke. Thus, we postulate that heartbeats encapsulate vital signals related to stroke. With the rapid advancement of deep neural n...
BACKGROUND: The diagnosis of Brugada syndrome by 12-lead electrocardiography (ECG) is challenging because the diagnostic type 1 pattern is often transient.
Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date-the electrocardiogram (ECG). The application of artificial intelligence-enabled ECG (AI-ECG) has res...
The primary purpose of this work is to analyze the ability of N-BEATS architecture for the problem of prediction and classification of electrocardiogram (ECG) signals. To achieve this, performance comparison with various types of other SotA (state-of...
Within the PhysioNet/Computing in Cardiology Challenge 2021, we focused on the design of a machine learning algorithm to identify cardiac abnormalities from electrocardiogram recordings (ECGs) with a various number of leads and to assess the diagnost...
Electrocardiographic (ECG) Heart Age conveying cardiovascular risk has been estimated by both Bayesian and artificial intelligence approaches. We hypothesised that explainable measures from the 10-s 12-lead ECG could successfully predict Bayesian 5-m...
This paper proposes a new method of impact classification for a Structural Health Monitoring system through the use of Self-Attention, the central building block of the Transformer neural network. As a topical and highly promising neural network arch...
Arrhythmia detection algorithms based on deep learning are attracting considerable interest due to their vital role in the diagnosis of cardiac abnormalities. Despite this interest, deep feature representation for ECG is still challenging and intrigu...
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