Alzheimer's disease detection in PSG signals

• URL: http://arxiv.org/abs/2404.03549v1
• Date: Thu, 4 Apr 2024 15:56:23 GMT
• Title: Alzheimer's disease detection in PSG signals
• Authors: Lorena Gallego-Viñarás, Juan Miguel Mira-Tomás, Anna Michela-Gaeta, Gerard Pinol-Ripoll, Ferrán Barbé, Pablo M. Olmos, Arrate Muñoz-Barrutia,
• Abstract summary: Alzheimers disease (AD) and sleep disorders exhibit a close association, where disruptions in sleep patterns often precede the onset of early-stage AD. This study delves into the potential of utilizing sleep-related electroencephalography (EEG) signals for the early detection of AD. Our primary focus is on exploring semi-supervised Deep Learning techniques for the classification of EEG signals due to the limited data availability.
• Score: 2.8691549050152965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Alzheimer's disease (AD) and sleep disorders exhibit a close association, where disruptions in sleep patterns often precede the onset of Mild Cognitive Impairment (MCI) and early-stage AD. This study delves into the potential of utilizing sleep-related electroencephalography (EEG) signals acquired through polysomnography (PSG) for the early detection of AD. Our primary focus is on exploring semi-supervised Deep Learning techniques for the classification of EEG signals due to the clinical scenario characterized by the limited data availability. The methodology entails testing and comparing the performance of semi-supervised SMATE and TapNet models, benchmarked against the supervised XCM model, and unsupervised Hidden Markov Models (HMMs). The study highlights the significance of spatial and temporal analysis capabilities, conducting independent analyses of each sleep stage. Results demonstrate the effectiveness of SMATE in leveraging limited labeled data, achieving stable metrics across all sleep stages, and reaching 90% accuracy in its supervised form. Comparative analyses reveal SMATE's superior performance over TapNet and HMM, while XCM excels in supervised scenarios with an accuracy range of 92 - 94%. These findings underscore the potential of semi-supervised models in early AD detection, particularly in overcoming the challenges associated with the scarcity of labeled data. Ablation tests affirm the critical role of spatio-temporal feature extraction in semi-supervised predictive performance, and t-SNE visualizations validate the model's proficiency in distinguishing AD patterns. Overall, this research contributes to the advancement of AD detection through innovative Deep Learning approaches, highlighting the crucial role of semi-supervised learning in addressing data limitations.

Related papers

• Federated Anomaly Detection for Early-Stage Diagnosis of Autism Spectrum Disorders using Serious Game Data [0.0]
  This study presents a novel semi-supervised approach for ASD detection using AutoEncoder-based Machine Learning (ML) methods. Our approach utilizes data collected manually through a serious game specifically designed for this purpose. Since the sensitive data collected by the gamified application are susceptible to privacy leakage, we developed a Federated Learning framework.
  arXiv  Detail & Related papers  (2024-10-25T23:00:12Z)
• Towards Within-Class Variation in Alzheimer's Disease Detection from Spontaneous Speech [60.08015780474457]
  Alzheimer's Disease (AD) detection has emerged as a promising research area that employs machine learning classification models. We identify within-class variation as a critical challenge in AD detection: individuals with AD exhibit a spectrum of cognitive impairments. We propose two novel methods: Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe), targeting two problems respectively.
  arXiv  Detail & Related papers  (2024-09-22T02:06:05Z)
• Deep Learning-based Classification of Dementia using Image Representation of Subcortical Signals [4.17085180769512]
  Alzheimer's disease (AD) and Frontotemporal dementia (FTD) are the common forms of dementia, each with distinct progression patterns. This study aims to develop a deep learning-based classification system for dementia by analyzing scout time-series signals from deep brain regions.
  arXiv  Detail & Related papers  (2024-08-20T13:11:43Z)
• An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease [13.213387075528017]
  Alzheimer's disease (AD) is the most prevalent form of dementia with a progressive decline in cognitive abilities. In this study, we leveraged structural and functional MRI to investigate the disease-induced grey matter and functional network connectivity changes. We propose a novel deep learning-based classification framework where generative module employing Cycle GANs was adopted to impute missing data within the latent space.
  arXiv  Detail & Related papers  (2024-06-19T07:31:47Z)
• Intelligent Diagnosis of Alzheimer's Disease Based on Machine Learning [24.467566885575998]
  This study is based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. It aims to explore early detection and disease progression in Alzheimer's disease (AD)
  arXiv  Detail & Related papers  (2024-02-13T15:43:30Z)
• Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection [69.53626024091076]
  Alzheimer's disease (AD) is particularly prominent in older adults. Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations. This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features.
  arXiv  Detail & Related papers  (2023-03-14T16:03:28Z)
• Exploring linguistic feature and model combination for speech recognition based automatic AD detection [61.91708957996086]
  Speech based automatic AD screening systems provide a non-intrusive and more scalable alternative to other clinical screening techniques. Scarcity of specialist data leads to uncertainty in both model selection and feature learning when developing such systems. This paper investigates the use of feature and model combination approaches to improve the robustness of domain fine-tuning of BERT and Roberta pre-trained text encoders.
  arXiv  Detail & Related papers  (2022-06-28T05:09:01Z)
• Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso [102.84915019938413]
  Non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG) offer promise of non-invasive techniques. The problem of source localization, or source imaging, poses however a high-dimensional statistical inference challenge. We propose an ensemble of desparsified multi-task Lasso (ecd-MTLasso) to deal with this problem.
  arXiv  Detail & Related papers  (2020-09-29T21:17:16Z)
• Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data [75.23250968928578]
  Parkinsons Disease is a neurological disorder and prevalent in elderly people. Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests. We propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild.
  arXiv  Detail & Related papers  (2020-09-25T01:50:15Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)
• Deep Recurrent Model for Individualized Prediction of Alzheimer's Disease Progression [4.034948808542701]
  Alzheimer's disease (AD) is one of the major causes of dementia and is characterized by slow progression over several years. We propose a novel computational framework that can predict the phenotypic measurements of MRI biomarkers and trajectories of clinical status.
  arXiv  Detail & Related papers  (2020-05-06T08:08:00Z)

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.