EDITH :ECG biometrics aided by Deep learning for reliable Individual auTHentication

• URL: http://arxiv.org/abs/2102.08026v1
• Date: Tue, 16 Feb 2021 08:45:17 GMT
• Title: EDITH :ECG biometrics aided by Deep learning for reliable Individual auTHentication
• Authors: Nabil Ibtehaz, Muhammad E. H. Chowdhury, Amith Khandakar, Serkan Kiranyaz, M. Sohel Rahman, Anas Tahir, Yazan Qiblawey, and Tawsifur Rahman
• Abstract summary: We present, EDITH, a deep learning-based framework for ECG biometrics authentication system. We have evaluated EDITH using 4 commonly used datasets and outperformed the prior works using less number of beats. Siamese architecture manages to reduce the identity verification Equal Error Rate (EER) to 1.29%.
• Score: 4.572194444732638
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, physiological signal based authentication has shown great promises,for its inherent robustness against forgery. Electrocardiogram (ECG) signal, being the most widely studied biosignal, has also received the highest level of attention in this regard. It has been proven with numerous studies that by analyzing ECG signals from different persons, it is possible to identify them, with acceptable accuracy. In this work, we present, EDITH, a deep learning-based framework for ECG biometrics authentication system. Moreover, we hypothesize and demonstrate that Siamese architectures can be used over typical distance metrics for improved performance. We have evaluated EDITH using 4 commonly used datasets and outperformed the prior works using less number of beats. EDITH performs competitively using just a single heartbeat (96-99.75% accuracy) and can be further enhanced by fusing multiple beats (100% accuracy from 3 to 6 beats). Furthermore, the proposed Siamese architecture manages to reduce the identity verification Equal Error Rate (EER) to 1.29%. A limited case study of EDITH with real-world experimental data also suggests its potential as a practical authentication system.

Related papers

• EKGNet: A 10.96{\mu}W Fully Analog Neural Network for Intra-Patient Arrhythmia Classification [79.7946379395238]
  We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification. We propose EKGNet, a hardware-efficient and fully analog arrhythmia classification architecture that archives high accuracy with low power consumption.
  arXiv  Detail & Related papers  (2023-10-24T02:37:49Z)
• DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection [49.196182908826565]
  Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input.
  arXiv  Detail & Related papers  (2023-09-07T13:43:46Z)
• Unleashing the Power of Electrocardiograms: A novel approach for Patient Identification in Healthcare Systems with ECG Signals [0.696125353550498]
  This paper presents a novel approach for patient identification in healthcare systems using electrocardiogram signals. A convolutional neural network is used to classify users based on images extracted from ECG signals.
  arXiv  Detail & Related papers  (2023-02-13T17:14:55Z)
• A lightweight hybrid CNN-LSTM model for ECG-based arrhythmia detection [0.0]
  This paper introduces a light deep learning approach for high accuracy detection of 8 different cardiac arrhythmias and normal rhythm. A trained model for arrhythmia classification using diverse ECG signals were successfully developed and tested.
  arXiv  Detail & Related papers  (2022-08-29T05:01:04Z)
• GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction [20.8603653664403]
  We propose a physiologically-inspired data augmentation method to improve performance and increase the robustness of heart disease detection based on ECG signals. We obtain augmented samples by perturbing the data distribution towards other classes along the geodesic in Wasserstein space. Learning from 12-lead ECG signals, our model is able to distinguish five categories of cardiac conditions.
  arXiv  Detail & Related papers  (2022-08-02T03:14:13Z)
• ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation [0.0]
  We perform extensive analysis and comparison of different scenarios in ECG biometric recognition. We present ECGXtractor, a robust Deep Learning technology trained with an in-house large-scale database. We evaluate the system performance over four different databases.
  arXiv  Detail & Related papers  (2022-04-08T10:53:11Z)
• Generalizing electrocardiogram delineation: training convolutional neural networks with synthetic data augmentation [63.51064808536065]
  Existing databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves has two main contributions. First, a pseudo-synthetic data generation algorithm was developed, based in probabilistically composing ECG traces given "pools" of fundamental segments, as cropped from the original databases, and a set of rules for their arrangement into coherent synthetic traces. Second, two novel segmentation-based loss functions have been developed, which attempt at enforcing the prediction of an exact number of independent structures and at producing closer segmentation boundaries by focusing on a reduced number of samples.
  arXiv  Detail & Related papers  (2021-11-25T10:11:41Z)
• Atrial Fibrillation Detection and ECG Classification based on CNN-BiLSTM [3.1372269816123994]
  It is challenging to visually detect heart disease from the electrocardiographic (ECG) signals. Implementing an automated ECG signal detection system can help diagnosis arrhythmia in order to improve the accuracy of diagnosis.
  arXiv  Detail & Related papers  (2020-11-12T04:20:56Z)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)
• ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks [69.25956542388653]
  Deep learning (DL) algorithms are gaining weight in academic and industrial settings. We demonstrate DL can be successfully applied to low interpretative tasks by embedding ECG detection and delineation onto a segmentation framework. The model was trained using PhysioNet's QT database, comprised of 105 ambulatory ECG recordings.
  arXiv  Detail & Related papers  (2020-05-11T16:29:12Z)
• Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review [62.490310870300746]
  The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
  arXiv  Detail & Related papers  (2019-12-28T02:44:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.