ECG-guided individual identification via PPG

• URL: http://arxiv.org/abs/2501.01983v1
• Date: Mon, 30 Dec 2024 08:56:23 GMT
• Title: ECG-guided individual identification via PPG
• Authors: Riling Wei, Hanjie Chen, Kelu Yao, Chuanguang Yang, Jun Wang, Chao Li,
• Abstract summary: Photoplethsmography-based individual identification aims at recognizing humans via intrinsic computational cardiovascular activities.<n>This paper introduces electrocardiogram (ECG) signals as a novel modality to enhance the density of input information.<n>A novel cross-modal knowledge distillation framework is implemented to propagate knowledge from ECG modality to PPG modality without incurring additional demands at the inference phase.
• Score: 17.307427732752455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Photoplethsmography (PPG)-based individual identification aiming at recognizing humans via intrinsic cardiovascular activities has raised extensive attention due to its high security and resistance to mimicry. However, this kind of technology witnesses unpromising results due to the limitation of low information density. To this end, electrocardiogram (ECG) signals have been introduced as a novel modality to enhance the density of input information. Specifically, a novel cross-modal knowledge distillation framework is implemented to propagate discriminate knowledge from ECG modality to PPG modality without incurring additional computational demands at the inference phase. Furthermore, to ensure efficient knowledge propagation, Contrastive Language-Image Pre-training (CLIP)-based knowledge alignment and cross-knowledge assessment modules are proposed respectively. Comprehensive experiments are conducted and results show our framework outperforms the baseline model with the improvement of 2.8% and 3.0% in terms of overall accuracy on seen- and unseen individual recognitions.

Related papers

• Revisiting Medical Image Retrieval via Knowledge Consolidation [46.6989555659494]
  We propose a novel method to consolidate knowledge of hierarchical features and functions. We introduce Depth-aware Representation Fusion (DaRF) and Structure-aware Contrastive Hashing (SCH) Our method achieves a 5.6-38.9% improvement in mean Average Precision on the anatomical radiology dataset.
  arXiv  Detail & Related papers  (2025-03-12T13:16:42Z)
• DiffuSETS: 12-lead ECG Generation Conditioned on Clinical Text Reports and Patient-Specific Information [13.680337221159506]
  Heart disease remains a significant threat to human health. Scarcity of high-quality ECG data, driven by privacy concerns and limited medical resources, creates a pressing need for effective ECG signal generation. We propose DiffuSETS, a novel framework capable of generating ECG signals with high semantic alignment and fidelity.
  arXiv  Detail & Related papers  (2025-01-10T12:55:34Z)
• CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Information [61.1904164368732]
  We propose CognitionCapturer, a unified framework that fully leverages multimodal data to represent EEG signals.<n>Specifically, CognitionCapturer trains Modality Experts for each modality to extract cross-modal information from the EEG modality.<n>The framework does not require any fine-tuning of the generative models and can be extended to incorporate more modalities.
  arXiv  Detail & Related papers  (2024-12-13T16:27:54Z)
• Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation [41.82319894067087]
  We propose an inter-intra period-aware ECG representation learning approach. Considering ECGs of atrial fibrillation patients exhibit the irregularity in RR intervals and the absence of P-waves, we develop specific pre-training tasks for interperiod and intraperiod representations. Our approach demonstrates remarkable AUC performances on the BTCH dataset, textiti.e., 0.953/0.996 for paroxysmal/persistent atrial fibrillation detection.
  arXiv  Detail & Related papers  (2024-10-08T10:03:52Z)
• AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans [43.06293430764841]
  This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions.
  arXiv  Detail & Related papers  (2024-07-02T16:44:00Z)
• ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text [14.06147507373525]
  This study introduces a new multimodal contrastive pretaining framework that aims to improve the quality and robustness of learned representations of 12-lead ECG signals. Our framework comprises two key components, including Cardio Query Assistant (CQA) and ECG Semantics Integrator(ESI)
  arXiv  Detail & Related papers  (2024-05-26T06:45:39Z)
• KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs [46.24838619931438]
  KG-TREAT synergizes large-scale observational patient data with biomedical knowledge graphs to enhance treatment effect estimation. KG-TREAT incorporates two pre-training tasks to ensure a thorough grounding and contextualization of patient data and KGs. Evaluation on four downstream TEE tasks shows KG-TREAT's superiority over existing methods, with an average improvement of 7% in Area under the ROC Curve (AUC) and 9% in Influence-based Precision of Estimating Heterogeneous Effects (IF-PEHE)
  arXiv  Detail & Related papers  (2024-03-06T15:37:22Z)
• PPG-to-ECG Signal Translation for Continuous Atrial Fibrillation Detection via Attention-based Deep State-Space Modeling [11.617950008187366]
  Photoplethysmography ( PPG) is a cost-effective and non-invasive technique that utilizes optical methods to measure cardiac physiology. Here, we propose a subject-independent attention-based deep state-space model (ADSSM) to translate PPG signals to corresponding ECG waveforms.
  arXiv  Detail & Related papers  (2023-09-27T03:07:46Z)
• 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)
• ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning [20.465733855762835]
  Electrocardiogram (ECG) monitoring is one of the most powerful technique of cardiovascular disease (CVD) early identification. Classic rule-based algorithms are now completely outperformed by deep learning based methods. We propose a multi-resolution model that can sustain high-resolution low-level semantic information throughout.
  arXiv  Detail & Related papers  (2023-04-10T15:19:00Z)
• ECG-Based Patient Identification: A Comprehensive Evaluation Across Health and Activity Conditions [0.0]
  This paper presents a novel approach for patient identification in healthcare systems using electrocardiogram signals. A convolutional neural network (CNN) is employed to classify users based on electrocardiomatrices, a specific type of image derived from ECG signals. The proposed identification system is evaluated in multiple databases, achieving up to 99.84% accuracy on healthy subjects, 97.09% on patients with cardiovascular diseases, and 97.89% on mixed populations including both healthy and arrhythmic patients.
  arXiv  Detail & Related papers  (2023-02-13T17:14:55Z)
• Deep Feature Mining via Attention-based BiLSTM-GCN for Human Motor Imagery Recognition [9.039355687614076]
  This paper presents a novel deep learning approach designed towards remarkably accurate and responsive motor imagery (MI) recognition based on scalp EEG. BiLSTM with the Attention mechanism manages to derive relevant features from raw EEG signals. The 0.4-second detection framework has shown effective and efficient prediction based on individual and group-wise training, with 98.81% and 94.64% accuracy, respectively.
  arXiv  Detail & Related papers  (2020-05-02T10:03:40Z)

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.