Enhancing Electrocardiogram Signal Analysis Using NLP-Inspired Techniques: A Novel Approach with Embedding and Self-Attention

• URL: http://arxiv.org/abs/2407.11102v1
• Date: Mon, 15 Jul 2024 12:20:15 GMT
• Title: Enhancing Electrocardiogram Signal Analysis Using NLP-Inspired Techniques: A Novel Approach with Embedding and Self-Attention
• Authors: Prapti Ganguly, Wazib Ansar, Amlan Chakrabarti,
• Abstract summary: We propose a novel ECG analysis technique, based on embedding and self attention, to capture the spatial as well as the temporal dependencies of the ECG data. An accuracy of 91% was achieved with a good F1-score for all the disease classes.
• Score: 2.7651063843287718
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A language is made up of an infinite/finite number of sentences, which in turn is composed of a number of words. The Electrocardiogram (ECG) is the most popular noninvasive medical tool for studying heart function and diagnosing various irregular cardiac rhythms. Intuitive inspection of the ECG reveals a marked similarity between ECG signals and the spoken language. As a result, the ECG signal may be thought of as a series of heartbeats (similar to sentences in a spoken language), with each heartbeat consisting of a collection of waves (similar to words in a sentence) with varying morphologies. Just as natural language processing (NLP) is used to help computers comprehend and interpret human natural language, it is conceivable to create NLP-inspired algorithms to help computers comprehend the electrocardiogram data more efficiently. In this study, we propose a novel ECG analysis technique, based on embedding and self attention, to capture the spatial as well as the temporal dependencies of the ECG data. To generate the embedding, an encoder-decoder network was proposed to capture the temporal dependencies of the ECG signal and perform data compression. The compressed and encoded data was fed to the embedding layer as its weights. Finally, the proposed CNN-LSTM-Self Attention classifier works on the embedding layer and classifies the signal as normal or anomalous. The approach was tested using the PTB-xl dataset, which is severely imbalanced. Our emphasis was to appropriately recognise the disease classes present in minority numbers, in order to limit the detection of False Negative cases. An accuracy of 91% was achieved with a good F1-score for all the disease classes. Additionally, the the size of the model was reduced by 34% due to compression, making it suitable for deployment in real time applications

Related papers

• PulseNet: Deep Learning ECG-signal classification using random augmentation policy and continous wavelet transform for canines [46.09869227806991]
  evaluating canine electrocardiograms (ECG) require skilled veterinarians. Current availability of veterinary cardiologists for ECG interpretation and diagnostic support is limited. We implement a deep convolutional neural network (CNN) approach for classifying canine electrocardiogram sequences as either normal or abnormal.
  arXiv  Detail & Related papers  (2023-05-17T09:06:39Z)
• Two-stream Network for ECG Signal Classification [3.222802562733787]
  This paper explores an effective algorithm for automatic classifications of multi-classes of heartbeat types based on ECG. A two-stream architecture is used in this paper and presents an enhanced version of ECG recognition based on this. Results on the MIT-BIH Arrhythmia Database demonstrate that the proposed algorithm performs an accuracy of 99.38%.
  arXiv  Detail & Related papers  (2022-10-05T08:14:51Z)
• 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)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification [78.120927891455]
  State-of-the-art brain-to-text systems have achieved great success in decoding language directly from brain signals using neural networks. In this paper, we extend the problem to open vocabulary Electroencephalography(EEG)-To-Text Sequence-To-Sequence decoding and zero-shot sentence sentiment classification on natural reading tasks. Our model achieves a 40.1% BLEU-1 score on EEG-To-Text decoding and a 55.6% F1 score on zero-shot EEG-based ternary sentiment classification, which significantly outperforms supervised baselines.
  arXiv  Detail & Related papers  (2021-12-05T21:57:22Z)
• 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)
• ECG-Based Heart Arrhythmia Diagnosis Through Attentional Convolutional Neural Networks [9.410102957429705]
  We propose Attention-Based Convolutional Neural Networks (ABCNN) to work on the raw ECG signals and automatically extract the informative dependencies for accurate arrhythmia detection. Our main task is to find the arrhythmia from normal heartbeats and, at the meantime, accurately recognize the heart diseases from five arrhythmia types. The experimental results show that the proposed ABCNN outperforms the widely used baselines.
  arXiv  Detail & Related papers  (2021-08-18T14:55:46Z)
• Noise-Resilient Automatic Interpretation of Holter ECG Recordings [67.59562181136491]
  We present a three-stage process for analysing Holter recordings with robustness to noisy signal. First stage is a segmentation neural network (NN) with gradientdecoder architecture which detects positions of heartbeats. Second stage is a classification NN which will classify heartbeats as wide or narrow. Third stage is a boosting decision trees (GBDT) on top of NN features that incorporates patient-wise features.
  arXiv  Detail & Related papers  (2020-11-17T16:15:49Z)
• 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)
• A Graph-constrained Changepoint Detection Approach for ECG Segmentation [5.209323879611983]
  We introduce a novel graph-based optimal changepoint detection (GCCD) method for reliable detection of R-peak positions without employing any preprocessing step. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed method achieves overall sensitivity Sen = 99.76, positive predictivity PPR = 99.68, and detection error rate DER = 0.55.
  arXiv  Detail & Related papers  (2020-04-24T23:41:41Z)

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.