Javascript must be enabled to continue!

An Attention Based Hierarchical LSTM Architecture for ECG Biometric System

The electrocardiogram (ECG) based biometric sys- tem has recently gained popularity. Easy signal acquisition and robustness against falsification are the major advantages of the ECG based biometric system. This biometric system can help automate the subject identification and authentication aspect of personalised healthcare services. In this paper, we have designed a novel attention based hierarchical long short-term memory (LSTM) model to learn the biometric representation correspond- ing to a person. The hierarchical LSTM model proposed in this paper can learn the temporal variation of the ECG signal in different abstractions. This addresses the long term dependency issue of the LSTM network in our application. The attention mechanism of the model learns to capture the ECG complexes that have more biometric information corresponding to each person. These ECG complexes are given more weight to learn better biometric representation. The proposed system is less complex and more efficient as it does not require the detection of any fiducial points. We have evaluated the proposed model for both the person verification and identification problems using two on-the-person ECG databases and two off-the-person ECG databases. The proposed framework is found to perform better than the existing fiducial and non-fiducial point based methods.

Institute of Electrical and Electronics Engineers (IEEE)

Debasish Jyotishi Samarendra Dandapat

2021

Title: An Attention Based Hierarchical LSTM Architecture for ECG Biometric System

Description:

The electrocardiogram (ECG) based biometric sys- tem has recently gained popularity.

Easy signal acquisition and robustness against falsification are the major advantages of the ECG based biometric system.

This biometric system can help automate the subject identification and authentication aspect of personalised healthcare services.

In this paper, we have designed a novel attention based hierarchical long short-term memory (LSTM) model to learn the biometric representation correspond- ing to a person.

The hierarchical LSTM model proposed in this paper can learn the temporal variation of the ECG signal in different abstractions.

This addresses the long term dependency issue of the LSTM network in our application.

The attention mechanism of the model learns to capture the ECG complexes that have more biometric information corresponding to each person.

These ECG complexes are given more weight to learn better biometric representation.

The proposed system is less complex and more efficient as it does not require the detection of any fiducial points.

We have evaluated the proposed model for both the person verification and identification problems using two on-the-person ECG databases and two off-the-person ECG databases.

The proposed framework is found to perform better than the existing fiducial and non-fiducial point based methods.

.

Back

Existing security issues like keys, pins, and passwords employed presently in almost all the fields that have certain limitations like passwords and pins can be easily forgotten; k...

Inter-beat and intra-beat ECG interval analysis based on state space and hidden markov models

Analyse des intervalles ECG inter- et intra-battement sur des modèles d'espace d'état et de Markov cachés Les maladies cardiovasculaires sont l'une des principales ...

Complex Deep Learning Models for Denoising of Human Heart ECG signals

Effective and powerful methods for denoising real electrocardiogram (ECG) signals are important for wearable sensors and devices. Deep Learning (DL) models have been used extensive...

The architecture of differences

Following in the footsteps of the protagonists of the Italian architectural debate is a mark of culture and proactivity. The synthesis deriving from the artistic-humanistic factors...

Development of AI-based method to detect the subtle ECG deviations from the population ECG norm

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Natio...

How Can We Identify the Best Implantation Site for an ECG Event Recorder?

ZELLERHOFF, C., et al.: How Can We Identify the Best Implantation Site for an ECG Event Recorder? The aim of this study was to show how to find the preferable implantation site for...

A KCP-DCNN-Based Two-Step Verification Multimodal Biometric Authentication System featuring QR Code Fabrication

Abstract Starting with for, need change Enhanced authentication performance, the concept of multi-biometrics authentication systems has emerged as a promising solution in t...

Self-Supervised Electrocardiograph De-noising

Abstract The electrocardiogram (ECG) records heart-beats and is potentially life-saving. However, the ECG signals (e.g., recorded by the standard ECG monitoring sys...

Email:
Password:

Email:

An Attention Based Hierarchical LSTM Architecture for ECG Biometric System

Related Results