Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction
The diagnosis of diseases is decisive for planning proper treatment and ensuring the well-
being of patients. Human error hinders accurate diagnostics, as interpreting medical …
being of patients. Human error hinders accurate diagnostics, as interpreting medical …
Ecg heartbeat classification: A deep transferable representation
Electrocardiogram (ECG) can be reliably used as a measure to monitor the functionality of
the cardiovascular system. Recently, there has been a great attention towards accurate …
the cardiovascular system. Recently, there has been a great attention towards accurate …
ECG heartbeat classification using multimodal fusion
Electrocardiogram (ECG) is an authoritative source to diagnose and counter critical
cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current …
cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current …
Detecting and interpreting myocardial infarction using fully convolutional neural networks
N Strodthoff, C Strodthoff - Physiological measurement, 2019 - iopscience.iop.org
Objective: We aim to provide an algorithm for the detection of myocardial infarction that
operates directly on ECG data without any preprocessing and to investigate its decision …
operates directly on ECG data without any preprocessing and to investigate its decision …
Artificial neural networks in medicine
JM Haglin, G Jimenez, AEM Eltorai - Health and Technology, 2019 - Springer
In the past several decades, the intricate neural networks of the human brain have inspired
the further development of intelligent systems. Many disciplines, including the complex field …
the further development of intelligent systems. Many disciplines, including the complex field …
A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation
Background Cardiologs® has developed the first electrocardiogram (ECG) algorithm that
uses a deep neural network (DNN) for full 12‑lead ECG analysis, including rhythm, QRS and …
uses a deep neural network (DNN) for full 12‑lead ECG analysis, including rhythm, QRS and …
MyWear: A novel smart garment for automatic continuous vital monitoring
SC Sethuraman, P Kompally… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Wearables are getting large acceptance in the continuous monitoring of health status and
physiological data. Medical devices and their connectivity through Internet along with the …
physiological data. Medical devices and their connectivity through Internet along with the …
Electrocardiogram heartbeat classification for arrhythmias and myocardial infarction
An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is
crucial for remote patient monitoring equipment. An accurate ECG signal classification is …
crucial for remote patient monitoring equipment. An accurate ECG signal classification is …
Ecg heart-beat classification using multimodal image fusion
In this paper, we present a novel Image Fusion Model (IFM) for ECG heart-beat classification
to overcome the weaknesses of existing machine learning techniques that rely either on …
to overcome the weaknesses of existing machine learning techniques that rely either on …
Deep learning for cardiologist-level myocardial infarction detection in electrocardiograms
Myocardial infarction is the leading cause of death worldwide. In this paper, we design
domain-inspired neural network models to detect myocardial infarction. First, we study the …
domain-inspired neural network models to detect myocardial infarction. First, we study the …