Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection

• URL: http://arxiv.org/abs/2205.07654v1
• Date: Mon, 16 May 2022 13:18:37 GMT
• Title: Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection
• Authors: Una Pale, Tomas Teijeiro, David Atienza
• Abstract summary: We show how HD computing can be used to perform feature selection by choosing an adequate encoding. This is the first approach to performing feature selection using HD computing in the literature.
• Score: 4.538319875483978
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The healthcare landscape is moving from the reactive interventions focused on symptoms treatment to a more proactive prevention, from one-size-fits-all to personalized medicine, and from centralized to distributed paradigms. Wearable IoT devices and novel algorithms for continuous monitoring are essential components of this transition. Hyperdimensional (HD) computing is an emerging ML paradigm inspired by neuroscience research with various aspects interesting for IoT devices and biomedical applications. Here we explore the not yet addressed topic of optimal encoding of spatio-temporal data, such as electroencephalogram (EEG) signals, and all information it entails to the HD vectors. Further, we demonstrate how the HD computing framework can be used to perform feature selection by choosing an adequate encoding. To the best of our knowledge, this is the first approach to performing feature selection using HD computing in the literature. As a result, we believe it can support the ML community to further foster the research in multiple directions related to feature and channel selection, as well as model interpretability.

Related papers

• Event-Driven Implementation of a Physical Reservoir Computing Framework for superficial EMG-based Gesture Recognition [2.222098162797332]
  This paper explores a novel neuromorphic implementation approach for gesture recognition by extracting spiking information from surface electromyography (sEMG) data in an event-driven manner. The network was designed by implementing a simple-structured and hardware-friendly Physical Reservoir Computing framework called Rotating Neuron Reservoir (RNR) within the domain of Spiking neural network (SNN) The proposed system was validated by an open-access large-scale sEMG database and achieved an average classification accuracy of 74.6% and 80.3%.
  arXiv  Detail & Related papers  (2025-03-10T17:18:14Z)
• End-to-end Stroke imaging analysis, using reservoir computing-based effective connectivity, and interpretable Artificial intelligence [42.52549987351643]
  We propose a reservoir computing-based and directed graph analysis pipeline. The goal of this pipeline is to define an efficient brain representation for connectivity in stroke data. This representation is used within a directed graph convolutional architecture and investigated with explainable artificial intelligence (AI) tools.
  arXiv  Detail & Related papers  (2024-07-17T13:34:05Z)
• Understanding Auditory Evoked Brain Signal via Physics-informed Embedding Network with Multi-Task Transformer [3.261870217889503]
  We propose an innovative multi-task learning model, Physics-informed Embedding Network with Multi-Task Transformer (PEMT-Net) PEMT-Net enhances decoding performance through physics-informed embedding and deep learning techniques. Experiments on a specific dataset demonstrate PEMT-Net's significant performance in multi-task auditory signal decoding.
  arXiv  Detail & Related papers  (2024-06-04T06:53:32Z)
• Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey [53.691704671844406]
  The Internet of things (IoT) can significantly enhance the quality of human life, specifically in healthcare. The human digital twin (HDT) is proposed as an innovative paradigm that can comprehensively characterize the replication of the individual human body. HDT is envisioned to empower IoT-healthcare beyond the application of healthcare monitoring by acting as a versatile and vivid human digital testbed. Recently, generative artificial intelligence (GAI) may be a promising solution because it can leverage advanced AI algorithms to automatically create, manipulate, and modify valuable while diverse data.
  arXiv  Detail & Related papers  (2024-01-22T03:17:41Z)
• Optimized Machine Learning for CHD Detection using 3D CNN-based Segmentation, Transfer Learning and Adagrad Optimization [0.0]
  Coronary Heart Disease (CHD) is one of the main causes of death. We propose a novel framework for predicting the presence of CHD using a combination of machine learning and image processing techniques.
  arXiv  Detail & Related papers  (2023-04-30T06:55:20Z)
• A Convolutional Spiking Network for Gesture Recognition in Brain-Computer Interfaces [0.8122270502556371]
  We propose a simple yet efficient machine learning-based approach for the exemplary problem of hand gesture classification based on brain signals. We demonstrate that this approach generalizes to different subjects with both EEG and ECoG data and achieves superior accuracy in the range of 92.74-97.07%.
  arXiv  Detail & Related papers  (2023-04-21T16:23:40Z)
• Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
  We propose a framework for autonomous robotic navigation for subretinal injection. Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target. Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
  arXiv  Detail & Related papers  (2023-01-17T21:41:21Z)
• Focused Decoding Enables 3D Anatomical Detection by Transformers [64.36530874341666]
  We propose a novel Detection Transformer for 3D anatomical structure detection, dubbed Focused Decoder. Focused Decoder leverages information from an anatomical region atlas to simultaneously deploy query anchors and restrict the cross-attention's field of view. We evaluate our proposed approach on two publicly available CT datasets and demonstrate that Focused Decoder not only provides strong detection results and thus alleviates the need for a vast amount of annotated data but also exhibits exceptional and highly intuitive explainability of results via attention weights.
  arXiv  Detail & Related papers  (2022-07-21T22:17:21Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data [72.1187887376849]
  The selective attention mechanism helps the cognition system focus on task-relevant visual clues by ignoring the presence of distractors. We propose a framework to leverage gaze for medical image analysis tasks with small training data. Our method is demonstrated to achieve superior performance on both 3D tumor segmentation and 2D chest X-ray classification tasks.
  arXiv  Detail & Related papers  (2021-12-02T07:55:25Z)
• EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters [72.19032452642728]
  We propose a novel differentiable EEG decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. We demonstrate the utility of our model towards emotion recognition from EEG signals on the SEED dataset and on a new EEG dataset of unprecedented size. The discovered features align with previous neuroscience studies and offer new insights, such as marked differences in the functional connectivity profile between left and right temporal areas during music listening.
  arXiv  Detail & Related papers  (2021-10-19T14:22:04Z)
• An adaptive cognitive sensor node for ECG monitoring in the Internet of Medical Things [0.7646713951724011]
  The Internet of Medical Things (IoMT) paradigm is becoming mainstream in multiple clinical trials and healthcare procedures. In this work, we explore the implementation of cognitive data analysis algorithm on resource-constrained computing platforms. We have assessed our approach on a use-case using a convolutional neural network to classify electrocardiogram traces.
  arXiv  Detail & Related papers  (2021-06-11T16:49:10Z)
• Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications [3.1366585656972092]
  We provide a tutorial describing how various technologies can be used to develop efficient DL accelerators. We explore how spiking neuromorphic processors can complement their DL counterparts for processing biomedical signals.
  arXiv  Detail & Related papers  (2020-07-11T01:14:08Z)
• Heart Sound Segmentation using Bidirectional LSTMs with Attention [37.62160903348547]
  We propose a novel framework for the segmentation of phonocardiogram (PCG) signals into heart states. We exploit recent advancements in attention based learning to segment the PCG signal. The proposed method attains state-of-the-art performance on multiple benchmarks including both human and animal heart recordings.
  arXiv  Detail & Related papers  (2020-04-02T02:09:11Z)

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.