Detection of Inferior Myocardial Infarction using Shallow Convolutional Neural Networks

10/03/2017
by   Tahsin Reasat, et al.
0

Myocardial Infarction is one of the leading causes of death worldwide. This paper presents a Convolutional Neural Network (CNN) architecture which takes raw Electrocardiography (ECG) signal from lead II, III and AVF and differentiates between inferior myocardial infarction (IMI) and healthy signals. The performance of the model is evaluated on IMI and healthy signals obtained from Physikalisch-Technische Bundesanstalt (PTB) database. A subject-oriented approach is taken to comprehend the generalization capability of the model and compared with the current state of the art. In a subject-oriented approach, the network is tested on one patient and trained on rest of the patients. Our model achieved a superior metrics scores (accuracy= 84.54 benchmark. We also analyzed the discriminating strength of the features extracted by the convolutional layers by means of geometric separability index and euclidean distance and compared it with the benchmark model.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/14/2020

Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

The electrocardiogram (ECG) is one of the most extensively employed sign...
research
12/29/2020

Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network

Noise and low quality of ECG signals acquired from Holter or wearable de...
research
02/25/2023

Myocardial Infarction Detection from ECG: A Gramian Angular Field-based 2D-CNN Approach

This paper presents a novel method for myocardial infarction (MI) detect...
research
02/13/2023

Unleashing the Power of Electrocardiograms: A novel approach for Patient Identification in Healthcare Systems with ECG Signals

Over the course of the past two decades, a substantial body of research ...
research
10/14/2021

Non-contact Atrial Fibrillation Detection from Face Videos by Learning Systolic Peaks

Objective: We propose a non-contact approach for atrial fibrillation (AF...

Please sign up or login with your details

Forgot password? Click here to reset