FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior knowledge

• URL: http://arxiv.org/abs/2310.05848v1
• Date: Fri, 6 Oct 2023 17:20:11 GMT
• Title: FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior knowledge
• Authors: Giacomo Verardo, Magnus Boman, Samuel Bruchfeld, Marco Chiesa, Sabine Koch, Gerald Q. Maguire Jr., Dejan Kostic
• Abstract summary: AutoEncoder models (AE) have been proposed to tackle the anomaly detection task with ML. We replace the decoding part of the AE with a reconstruction head based on prior knowledge of the ECG shape. Our model consistently achieves higher anomaly detection capabilities than state-of-the-art models.
• Score: 6.278174335563065
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Detecting anomalies in electrocardiogram data is crucial to identifying deviations from normal heartbeat patterns and providing timely intervention to at-risk patients. Various AutoEncoder models (AE) have been proposed to tackle the anomaly detection task with ML. However, these models do not consider the specific patterns of ECG leads and are unexplainable black boxes. In contrast, we replace the decoding part of the AE with a reconstruction head (namely, FMM-Head) based on prior knowledge of the ECG shape. Our model consistently achieves higher anomaly detection capabilities than state-of-the-art models, up to 0.31 increase in area under the ROC curve (AUROC), with as little as half the original model size and explainable extracted features. The processing time of our model is four orders of magnitude lower than solving an optimization problem to obtain the same parameters, thus making it suitable for real-time ECG parameters extraction and anomaly detection.

Related papers

• Effort: Efficient Orthogonal Modeling for Generalizable AI-Generated Image Detection [66.16595174895802]
  Existing AI-generated image (AIGI) detection methods often suffer from limited generalization performance. In this paper, we identify a crucial yet previously overlooked asymmetry phenomenon in AIGI detection.
  arXiv  Detail & Related papers  (2024-11-23T19:10:32Z)
• Steam Turbine Anomaly Detection: An Unsupervised Learning Approach Using Enhanced Long Short-Term Memory Variational Autoencoder [3.004788114489393]
  We propose an Enhanced Long Short-Term Memory Variational Autoencoder (ELSTMVAE-DAF-GMM) for precise unsupervised anomaly detection in unlabeled datasets. ELSTMVAE, integrating LSTM with VAE, was used to project high-dimensional time-series data to a low-dimensional phase space. The novel deep advanced features (DAF) hybridize latent embeddings and reconstruction discrepancies from the LSTMVAE model and provide a more comprehensive data representation.
  arXiv  Detail & Related papers  (2024-11-16T10:11:36Z)
• MIXAD: Memory-Induced Explainable Time Series Anomaly Detection [21.208134038200203]
  We introduce MIXAD (Memory Explainable Time Series Atemporally Detection), a model designed for interpretable anomaly detection. We also introduce a novel anomaly scoring method that detects significant shifts in memory activation patterns during anomalies. Our approach not only ensures decent detection performance but also outperforms state-of-the-art baselines by 34.30% and 34.51% in interpretability metrics.
  arXiv  Detail & Related papers  (2024-10-30T06:46:23Z)
• MAPL: Memory Augmentation and Pseudo-Labeling for Semi-Supervised Anomaly Detection [0.0]
  A new meth-odology for detecting surface defects in in-dustrial settings is introduced, referred to as Memory Augmentation and Pseudo-Labeling(MAPL) The methodology first in-troduces an anomaly simulation strategy, which significantly improves the model's ability to recognize rare or unknown anom-aly types. An end-to-end learning framework is employed by MAPL to identify the abnormal regions directly from the input data.
  arXiv  Detail & Related papers  (2024-05-10T02:26:35Z)
• Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [59.34318192698142]
  We introduce a prior-less anomaly generation paradigm and develop an innovative unsupervised anomaly detection framework named GRAD. PatchDiff effectively expose various types of anomaly patterns. experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation.
  arXiv  Detail & Related papers  (2023-12-26T07:08:06Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs [51.98400002538092]
  This paper aims to improve model-based anomaly detection in CPPS by combining the learned timed automaton with a formal knowledge graph about the system. Both the model and the detected anomalies are described in the knowledge graph in order to allow operators an easier interpretation of the model and the detected anomalies.
  arXiv  Detail & Related papers  (2023-08-25T15:25:57Z)
• Two-stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection [4.772323272202286]
  We propose a two-stream decoder network (TSDN) to learn both normal and abnormal features. We also propose a feature normality estimator (FNE) to eliminate abnormal features and prevent high-quality reconstruction of abnormal regions.
  arXiv  Detail & Related papers  (2023-02-20T06:46:09Z)
• Are we certain it's anomalous? [57.729669157989235]
  Anomaly detection in time series is a complex task since anomalies are rare due to highly non-linear temporal correlations. Here we propose the novel use of Hyperbolic uncertainty for Anomaly Detection (HypAD) HypAD learns self-supervisedly to reconstruct the input signal.
  arXiv  Detail & Related papers  (2022-11-16T21:31:39Z)
• Explainable multiple abnormality classification of chest CT volumes with AxialNet and HiResCAM [89.2175350956813]
  We introduce the challenging new task of explainable multiple abnormality classification in volumetric medical images. We propose a multiple instance learning convolutional neural network, AxialNet, that allows identification of top slices for each abnormality. We then aim to improve the model's learning through a novel mask loss that leverages HiResCAM and 3D allowed regions.
  arXiv  Detail & Related papers  (2021-11-24T01:14:33Z)
• Anomaly Detection Based on Multiple-Hypothesis Autoencoder [0.0]
  A model trained with normal data generates a larger restoration error for abnormal data. The restoration area for the input data of AE is limited in the latent space. We propose Multiple-hypothesis Autoencoder(MH-AE) model composed of several decoders.
  arXiv  Detail & Related papers  (2021-07-07T05:09:03Z)

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.