Classification of ECG signals by dot Residual LSTM Network with data augmentation for anomaly detection

Nazi, Zabir Al; Biswas, Ananna; Rayhan, M. A.; Abir, Tasnim Azad

doi:10.1109/iccit48885.2019.9038287

Cited by 20 publications

(14 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the ECG-ID dataset, the accuracy for all techniques was quite similar, and the proposed method outperformed the other methods for the F1 score, which achieved 98.84%. For the MIT-BIH ECG dataset, Fan Liu et al [40] achieved a higher accuracy compared to other techniques [24,40,41]; however, our proposed method significantly outperformed theirs. Moreover, they still need to first identify the heartbeat, which is intensive in terms of the algorithm engineering process, compared to our random signal segmentation.…”

Section: Acc =mentioning

confidence: 69%

Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication

2021

View full text Add to dashboard Cite

In this report, the study of non-fiducial based approaches for Electrocardiogram(ECG) biometric authentication is examined, and several excessive techniques are proposed to perform comparative experiments for evaluating the best possible approach for all the classification tasks. Non-fiducial methods are designed to extract the discriminative information of a signal without annotating fiducial points. However, this process requires peak detection to identify a heartbeat signal. Based on recent studies that usually rely on heartbeat segmentation, QRS detection is required, and the process can be complicated for ECG signals for which the QRS complex is absent. Thus, many studies only conduct biometric authentication tasks on ECG signals with QRS complexes, and are hindered by similar limitations. To overcome this issue, we proposed a data-independent acquisition method to facilitate highly generalizable signal processing and feature learning processes. This is achieved by enhancing random segmentation to avoid complicated fiducial feature extraction, along with auto-correlation to eliminate the phase difference due to random segmentation. Subsequently, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) deep networks is utilized to automatically learn the features associated with the signal and to perform an authentication task. The experimental results suggest that the proposed data-independent approach using a BLSTM network achieves a relatively high classification accuracy for every dataset relative to the compared techniques. Moreover, it exhibited a significantly higher accuracy rate in experiments using ECG signals without the QRS complex. The results also revealed that data-dependent methods can only perform well for specified data types and amendments of data variations, whereas the presented approach can also be considered for generalization to other quasi-periodical biometric signal-based classification tasks in future studies.

show abstract

Section: Acc =mentioning

confidence: 69%

Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication

2021

View full text Add to dashboard Cite

show abstract

“…Romdhane et al [45] proposed a CNN-based method and an optimized loss function for data augmentation. Nazi et al [46] proposed a classification framework based on the dot Residual LSTM network. Conditional Variational AutoEncoder (CVAE) and LSTM network are used to increase training samples to solve data imbalance.…”

Section: Data Augmentation Methodsmentioning

confidence: 99%

CAB: Classifying Arrhythmias based on Imbalanced Sensor Data

2021

KSII TIIS

View full text Add to dashboard Cite

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

show abstract

“…The use of AE architectures is nothing more than the evolution of data generation algorithms to produce more and better data, which means that, better, they are varied and therefore the standard deviation with respect to the original data is perfect. To precisely control the deviation of the data, VAE arises as the evolution of AE to generate better synthetic data, as shown in [81] where VAE is used to generate data for anomaly detection problems with LSTM. Or this other work [82], in which they use a dataset augmented with VAE to improve the recognition of human activity with LSTM.…”

Section: Data Augmentation Through Vaementioning

confidence: 99%

Data Augmentation techniques in time series domain: a survey and taxonomy

Iglesias

Talavera

González-Prieto

et al. 2023

Neural Comput & Applic

View full text Add to dashboard Cite

With the latest advances in deep learning-based generative models, it has not taken long to take advantage of their remarkable performance in the area of time series. Deep neural networks used to work with time series heavily depend on the size and consistency of the datasets used in training. These features are not usually abundant in the real world, where they are usually limited and often have constraints that must be guaranteed. Therefore, an effective way to increase the amount of data is by using data augmentation techniques, either by adding noise or permutations and by generating new synthetic data. This work systematically reviews the current state of the art in the area to provide an overview of all available algorithms and proposes a taxonomy of the most relevant research. The efficiency of the different variants will be evaluated as a central part of the process, as well as the different metrics to evaluate the performance and the main problems concerning each model will be analysed. The ultimate aim of this study is to provide a summary of the evolution and performance of areas that produce better results to guide future researchers in this field.

show abstract

Classification of ECG signals by dot Residual LSTM Network with data augmentation for anomaly detection

Cited by 20 publications

References 14 publications

Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication

Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication

CAB: Classifying Arrhythmias based on Imbalanced Sensor Data

Data Augmentation techniques in time series domain: a survey and taxonomy

Contact Info

Product

Resources

About