Electrocardiogram Recognization Based on Variational AutoEncoder

Chen, Shaojie; Meng, Zhaopeng; Qing, Zhao

doi:10.5772/intechopen.76434

Cited by 8 publications

(6 citation statements)

References 27 publications

(30 reference statements)

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“…The VAE is a likelihood model and a higher log-likelihood estimation indicates a good performance of the VAE (Chen et al 2018), which, however, is only evaluated on a static distribution. In this section, we develop a novel lower bound to the sample log-likelihood, which can be used to analyze the VAE's performance on a non-stationary data distribution.…”

Section: Theoretical Framework Forgetting Analysis Of a Single Vae Modelmentioning

confidence: 99%

Lifelong Variational Autoencoder via Online Adversarial Expansion Strategy

Borş

2023

AAAI

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The Variational Autoencoder (VAE) suffers from a significant loss of information when trained on a non-stationary data distribution. This loss in VAE models, called catastrophic forgetting, has not been studied theoretically before. We analyse the forgetting behaviour of a VAE in continual generative modelling by developing a new lower bound on the data likelihood, which interprets the forgetting process as an increase in the probability distance between the generator's distribution and the evolved data distribution. The proposed bound shows that a VAE-based dynamic expansion model can achieve better performance if its capacity increases appropriately considering the shift in the data distribution. Based on this analysis, we propose a novel expansion criterion that aims to preserve the information diversity among the VAE components, while ensuring that it acquires more knowledge with fewer parameters. Specifically, we implement this expansion criterion from the perspective of a multi-player game and propose the Online Adversarial Expansion Strategy (OAES), which considers all previously learned components as well as the currently updated component as multiple players in a game, while an adversary model evaluates their performance. The proposed OAES can dynamically estimate the discrepancy between each player and the adversary without accessing task information. This leads to the gradual addition of new components while ensuring the knowledge diversity among all of them. We show theoretically and empirically that the proposed extension strategy can enable a VAE model to achieve the best performance given an appropriate model size.

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Section: Theoretical Framework Forgetting Analysis Of a Single Vae Modelmentioning

confidence: 99%

Lifelong Variational Autoencoder via Online Adversarial Expansion Strategy

Borş

2023

AAAI

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“…The perfect biometric modality 3 should possess a very low intra-subject variability besides having both a very high inter-subject variability and stability over time [234].…”

Section: ) Intra-subject Variabilitymentioning

confidence: 99%

“…Researchers have already started to address the problem of 1 A biometric modality refers to a system built to identify a particular biometric trait [3], [5]. Notably, a biometric modality combines a biometric trait, sensor type, and algorithms for extracting and processing the digital representations of the trait.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Uwaechia

Ramli

2021

IEEE Access

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Electrocardiogram (ECG) has extremely discriminative characteristics in the biometric field and has recently received significant interest as a promising biometric trait. However, ECG signals are susceptible to several types of noises, such as baseline wander, powerline interference, and high/lowfrequency noises, making it challenging to realize biometric identification systems precisely and robustly. Therefore, ECG signal denoising is a major pre-processing step and plays a crucial role in ECG-based biometric human identification. ECG signal analysis for biometric recognition can combine several steps, such as preprocessing, feature extraction, feature selection, feature transformation, and classification which is a very challenging task. Moreover, the selected success measures and proper structure of the ECG signal database play significant roles in biometric system analysis, considering that publicly available databases are essential by the research community to evaluate the performance of their proposed algorithms. In this survey, we review most of the techniques employed for the ECG as biometrics for human authentication. Firstly, we present an overview and discussion on ECG signal preprocessing, feature extraction, feature selection, and feature transformation for ECG-based biometric systems. Secondly, we present a survey of the available ECG databases to evaluate and compare the acquisition protocol, acquisition hardware, and acquisition resolution (bits) for ECG-based biometric systems. Thirdly, we also present a survey on different techniques, including deep learning methods: deep supervised learning, deep semi-supervised learning, and deep unsupervised learning, for ECG signal classification. Lastly, we present the state-of-art approaches of information fusion in multimodal biometric systems.

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“…Detecting distortions in ECG signals are difficult to find due to noise from disturbances, such baseline wandering, muscle shaking, and electrode movement. The VAE has been used to distinguish these ECG signals under noise conditions [ 137 ]. They used three data sets: AHA ECG database, the APNEA ECG database, and CHFDB ECG database.…”

Section: Applicationsmentioning

confidence: 99%

An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications

Singh

Ogunfunmi

2021

Entropy

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Autoencoders are a self-supervised learning system where, during training, the output is an approximation of the input. Typically, autoencoders have three parts: Encoder (which produces a compressed latent space representation of the input data), the Latent Space (which retains the knowledge in the input data with reduced dimensionality but preserves maximum information) and the Decoder (which reconstructs the input data from the compressed latent space). Autoencoders have found wide applications in dimensionality reduction, object detection, image classification, and image denoising applications. Variational Autoencoders (VAEs) can be regarded as enhanced Autoencoders where a Bayesian approach is used to learn the probability distribution of the input data. VAEs have found wide applications in generating data for speech, images, and text. In this paper, we present a general comprehensive overview of variational autoencoders. We discuss problems with the VAEs and present several variants of the VAEs that attempt to provide solutions to the problems. We present applications of variational autoencoders for finance (a new and emerging field of application), speech/audio source separation, and biosignal applications. Experimental results are presented for an example of speech source separation to illustrate the powerful application of variants of VAE: VAE, β-VAE, and ITL-AE. We conclude the paper with a summary, and we identify possible areas of research in improving performance of VAEs in particular and deep generative models in general, of which VAEs and generative adversarial networks (GANs) are examples.

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Electrocardiogram Recognization Based on Variational AutoEncoder

Cited by 8 publications

References 27 publications

Lifelong Variational Autoencoder via Online Adversarial Expansion Strategy

Lifelong Variational Autoencoder via Online Adversarial Expansion Strategy

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications

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