Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification

• URL: http://arxiv.org/abs/2012.04936v1
• Date: Wed, 9 Dec 2020 09:39:48 GMT
• Title: Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification
• Authors: Yadong Zhang and Xin Chen
• Abstract summary: In the time-series analysis, the time series motifs and the order patterns in time series can reveal general temporal patterns and dynamic features. TMF is a simple and effective time-series image encoding method based on triadic time series motifs. The dynamic features can be extracted from the TMF images with the transfer learning pre-trained convolutional neural network (CNN) models. The patterns revealed by symmetrized Gradient-weighted Class Mapping have a clear clinical interpretation.
• Score: 3.026059658770843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the time-series analysis, the time series motifs and the order patterns in time series can reveal general temporal patterns and dynamic features. Triadic Motif Field (TMF) is a simple and effective time-series image encoding method based on triadic time series motifs. Electrocardiography (ECG) signals are time-series data widely used to diagnose various cardiac anomalies. The TMF images contain the features characterizing the normal and Atrial Fibrillation (AF) ECG signals. Considering the quasi-periodic characteristics of ECG signals, the dynamic features can be extracted from the TMF images with the transfer learning pre-trained convolutional neural network (CNN) models. With the extracted features, the simple classifiers, such as the Multi-Layer Perceptron (MLP), the logistic regression, and the random forest, can be applied for accurate anomaly detection. With the test dataset of the PhysioNet Challenge 2017 database, the TMF classification model with the VGG16 transfer learning model and MLP classifier demonstrates the best performance with the 95.50% ROC-AUC and 88.43% F1 score in the AF classification. Besides, the TMF classification model can identify AF patients in the test dataset with high precision. The feature vectors extracted from the TMF images show clear patient-wise clustering with the t-distributed Stochastic Neighbor Embedding technique. Above all, the TMF classification model has very good clinical interpretability. The patterns revealed by symmetrized Gradient-weighted Class Activation Mapping have a clear clinical interpretation at the beat and rhythm levels.

Related papers

• CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis [46.56667527672019]
  We introduce a Cross-Modal Temporal Pattern Discovery (CTPD) framework, designed to efficiently extract meaningful cross-modal temporal patterns from multimodal EHR data. Our approach introduces shared initial temporal pattern representations which are refined using slot attention to generate temporal semantic embeddings.
  arXiv  Detail & Related papers  (2024-11-01T15:54:07Z)
• Interpretable Pre-Trained Transformers for Heart Time-Series Data [15.377534937558744]
  We create two pre-trained general purpose cardiac models, PPG-PT and ECG-PT. We highlight that individual attention heads respond to specific physiologically relevent features, such as the dicrotic notch in PPG and the P-wave in ECG. These pre-trained models are straightforward to fine-tune for tasks such as classification of atrial fibrillation (AF), and beat detection in photoplethysmography.
  arXiv  Detail & Related papers  (2024-07-30T12:22:03Z)
• Improving Diffusion Models for ECG Imputation with an Augmented Template Prior [43.6099225257178]
  noisy and poor-quality recordings are a major issue for signals collected using mobile health systems. Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models. We present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
  arXiv  Detail & Related papers  (2023-10-24T11:34:15Z)
• Classification of lung cancer subtypes on CT images with synthetic pathological priors [41.75054301525535]
  Cross-scale associations exist in the image patterns between the same case's CT images and its pathological images. We propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on CT images.
  arXiv  Detail & Related papers  (2023-08-09T02:04:05Z)
• Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus [46.1292414445895]
  Paranasal anomalies can present with a wide range of morphological features. Current approaches to paranasal anomaly classification are constrained to identifying a single anomaly at a time. We investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary (MS) and MS with polyps or cysts.
  arXiv  Detail & Related papers  (2023-03-31T09:23:27Z)
• A Deep Learning Network for the Classification of Intracardiac Electrograms in Atrial Tachycardia [4.62891362095648]
  Key technology enabling the success of catheter ablation treatment for atrial tachycardia is activation mapping. This is a time-consuming and error-prone procedure, due to the difficulty in identifying the signal activation peaks for fractionated signals. This work presents a Deep Learning approach for the automated classification of EGM signals into three different types.
  arXiv  Detail & Related papers  (2022-06-02T09:56:27Z)
• 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)
• Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG Data [0.0]
  This thesis builds Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) classifiers based on state-of-the-art models. Both global and local interpretability methods are exploited to understand the interaction between dependent and independent variables. It was found that Grad-Cam was the most effective interpretability technique at explaining predictions of proposed CNN and LSTM models.
  arXiv  Detail & Related papers  (2022-05-05T17:29:33Z)
• ECG Heartbeat Classification Using Multimodal Fusion [13.524306011331303]
  We propose two computationally efficient multimodal fusion frameworks for ECG heart beat classification. In MFF, we extracted features from penultimate layer of CNNs and fused them to get unique and interdependent information. We achieved classification accuracy of 99.7% and 99.2% on arrhythmia and MI classification, respectively.
  arXiv  Detail & Related papers  (2021-07-21T03:48:35Z)
• ECG Heart-beat Classification Using Multimodal Image Fusion [13.524306011331303]
  We present a novel Image Fusion Model (IFM) for ECG heart-beat classification. We first convert the heart beats of ECG into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF)
  arXiv  Detail & Related papers  (2021-05-28T01:31:35Z)
• Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling [121.50704279659253]
  We propose a cross-verified feature disentangling strategy to disentangle the physiological features with non-physiological representations. We then use the distilled physiological features for robust multi-task physiological measurements. The disentangled features are finally used for the joint prediction of multiple physiological signals like average HR values and r signals.
  arXiv  Detail & Related papers  (2020-07-16T09:39:17Z)

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.