Efficient ECG-based Atrial Fibrillation Detection via Parameterised Hypercomplex Neural Networks

• URL: http://arxiv.org/abs/2211.02678v3
• Date: Mon, 11 Sep 2023 11:36:04 GMT
• Title: Efficient ECG-based Atrial Fibrillation Detection via Parameterised Hypercomplex Neural Networks
• Authors: Leonie Basso, Zhao Ren, Wolfgang Nejdl
• Abstract summary: Atrial fibrillation (AF) is the most common cardiac arrhythmia and associated with a high risk for serious conditions like stroke. Wearable devices embedded with automatic and timely AF assessment from electrocardiograms (ECGs) has shown to be promising in preventing life-threatening situations. Deep neural networks have demonstrated superiority in model performance, their use on wearable devices is limited by the trade-off between model performance and complexity.
• Score: 11.964843902569925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Atrial fibrillation (AF) is the most common cardiac arrhythmia and associated with a high risk for serious conditions like stroke. The use of wearable devices embedded with automatic and timely AF assessment from electrocardiograms (ECGs) has shown to be promising in preventing life-threatening situations. Although deep neural networks have demonstrated superiority in model performance, their use on wearable devices is limited by the trade-off between model performance and complexity. In this work, we propose to use lightweight convolutional neural networks (CNNs) with parameterised hypercomplex (PH) layers for AF detection based on ECGs. The proposed approach trains small-scale CNNs, thus overcoming the limited computing resources on wearable devices. We show comparable performance to corresponding real-valued CNNs on two publicly available ECG datasets using significantly fewer model parameters. PH models are more flexible than other hypercomplex neural networks and can operate on any number of input ECG leads.

Related papers

• Prototyping an End-to-End Multi-Modal Tiny-CNN for Cardiovascular Sensor Patches [35.899401205042615]
  We propose a convolutional neural network with early fusion of data to solve a binary classification problem.<n>Our approach reduces memory footprint and compute cost by three orders of magnitude compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2025-10-21T14:23:20Z)
• Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons [69.73249913506042]
  This paper investigates a wireless split computing architecture that employs resonate-and-fire (RF) neurons to process time-domain signals directly.<n>By resonating at tunable frequencies, RF neurons extract time-localized spectral features while maintaining low spiking activity.<n> Experimental results show that the proposed RF-SNN architecture achieves comparable accuracy to conventional LIF-SNNs and ANNs.
  arXiv  Detail & Related papers  (2025-06-24T21:14:59Z)
• Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning [1.8946099300030472]
  EEG-GraphAdapter (EGA) is a parameter-efficient fine-tuning (PEFT) approach to address these challenges. EGA is integrated into pre-trained temporal backbone models as a GNN-based module. It improves performance by up to 16.1% in the F1-score compared with the backbone BENDR model.
  arXiv  Detail & Related papers  (2024-11-25T07:30:52Z)
• ConvexECG: Lightweight and Explainable Neural Networks for Personalized, Continuous Cardiac Monitoring [43.23305904110984]
  ConvexECG is an explainable and resource-efficient method for reconstructing six-lead electrocardiograms from single-lead data. We demonstrate that ConvexECG achieves accuracy comparable to larger neural networks while significantly reducing computational overhead.
  arXiv  Detail & Related papers  (2024-09-19T06:14:30Z)
• Hybrid Spiking Neural Networks for Low-Power Intra-Cortical Brain-Machine Interfaces [42.72938925647165]
  Intra-cortical brain-machine interfaces (iBMIs) have the potential to dramatically improve the lives of people with paraplegia. Current iBMIs suffer from scalability and mobility limitations due to bulky hardware and wiring. We are investigating hybrid spiking neural networks for embedded neural decoding in wireless iBMIs.
  arXiv  Detail & Related papers  (2024-09-06T17:48:44Z)
• L-SFAN: Lightweight Spatially-focused Attention Network for Pain Behavior Detection [44.016805074560295]
  Chronic Low Back Pain (CLBP) afflicts millions globally, significantly impacting individuals' well-being and imposing economic burdens on healthcare systems. While artificial intelligence (AI) and deep learning offer promising avenues for analyzing pain-related behaviors to improve rehabilitation strategies, current models, including convolutional neural networks (CNNs), have limitations. We introduce hbox EmoL-SFAN, a lightweight CNN architecture incorporating 2D filters designed to capture the spatial-temporal interplay of data from motion capture and surface electromyography sensors.
  arXiv  Detail & Related papers  (2024-06-07T12:01:37Z)
• REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates [54.96885726053036]
  This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis. By leveraging a combination of graph neural networks and recurrent structures, REST efficiently captures both non-Euclidean geometry and temporal dependencies within EEG data. Our model demonstrates high accuracy in both seizure detection and classification tasks.
  arXiv  Detail & Related papers  (2024-06-03T16:30:19Z)
• 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)
• Digital twinning of cardiac electrophysiology models from the surface ECG: a geodesic backpropagation approach [39.36827689390718]
  We introduce a novel method, Geodesic-BP, to solve the inverse eikonal problem. We show that Geodesic-BP can reconstruct a simulated cardiac activation with high accuracy in a synthetic test case. Given the future shift towards personalized medicine, Geodesic-BP has the potential to help in future functionalizations of cardiac models.
  arXiv  Detail & Related papers  (2023-08-16T14:57:12Z)
• How neural networks learn to classify chaotic time series [77.34726150561087]
  We study the inner workings of neural networks trained to classify regular-versus-chaotic time series. We find that the relation between input periodicity and activation periodicity is key for the performance of LKCNN models.
  arXiv  Detail & Related papers  (2023-06-04T08:53:27Z)
• HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN [3.8791511769387625]
  We have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. The proposed HARDC fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features. Our results indicate that an automated and highly computed method to classify multiple types of arrhythmia signals holds considerable promise.
  arXiv  Detail & Related papers  (2023-03-06T13:26:29Z)
• 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)
• Effective classification of ecg signals using enhanced convolutional neural network in iot [0.0]
  This paper proposes a routing system for IoT healthcare platforms based on Dynamic Source Routing (DSR) and Routing by Energy and Link Quality (REL) Deep-ECG will employ a deep CNN to extract important characteristics, which will then be compared using simple and fast distance functions. The results show that the proposed strategy outperforms others in terms of classification accuracy.
  arXiv  Detail & Related papers  (2022-02-08T13:37:23Z)
• Multistage Pruning of CNN Based ECG Classifiers for Edge Devices [9.223908421919733]
  Convolutional neural network (CNN) based deep learning has been used successfully to detect anomalous beats in ECG. The computational complexity of existing CNN models prohibits them from being implemented in low-powered edge devices. This paper presents a novel multistage pruning technique that reduces CNN model complexity with negligible loss in performance.
  arXiv  Detail & Related papers  (2021-08-31T17:51:15Z)
• Learning Frequency-aware Dynamic Network for Efficient Super-Resolution [56.98668484450857]
  This paper explores a novel frequency-aware dynamic network for dividing the input into multiple parts according to its coefficients in the discrete cosine transform (DCT) domain. In practice, the high-frequency part will be processed using expensive operations and the lower-frequency part is assigned with cheap operations to relieve the computation burden. Experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures.
  arXiv  Detail & Related papers  (2021-03-15T12:54:26Z)
• 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)
• 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)
• Multi-Lead ECG Classification via an Information-Based Attention Convolutional Neural Network [1.1720399305661802]
  One-dimensional convolutional neural networks (CNN) have proven to be effective in pervasive classification tasks. We implement the Residual connection and design a structure which can learn the weights from the information contained in different channels in the input feature map. An indicator named mean square deviation is introduced to monitor the performance of a particular model segment in the classification task.
  arXiv  Detail & Related papers  (2020-03-25T02:28:04Z)

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.