PULSAR: Graph based Positive Unlabeled Learning with Multi Stream Adaptive Convolutions for Parkinson's Disease Recognition

• URL: http://arxiv.org/abs/2312.05780v2
• Date: Fri, 16 Feb 2024 14:48:47 GMT
• Title: PULSAR: Graph based Positive Unlabeled Learning with Multi Stream Adaptive Convolutions for Parkinson's Disease Recognition
• Authors: Md. Zarif Ul Alam, Md Saiful Islam, Ehsan Hoque, M Saifur Rahman
• Abstract summary: Parkinsons disease (PD) is a neuro-degenerative disorder that affects movement, speech, and coordination. We present PULSAR, a novel method to screen for PD from webcam-recorded videos of finger-tapping. We used an adaptive graph convolutional neural network to dynamically learn the temporal graph specific to the finger-tapping task.
• Score: 1.9482539692051932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Parkinson's disease (PD) is a neuro-degenerative disorder that affects movement, speech, and coordination. Timely diagnosis and treatment can improve the quality of life for PD patients. However, access to clinical diagnosis is limited in low and middle income countries (LMICs). Therefore, development of automated screening tools for PD can have a huge social impact, particularly in the public health sector. In this paper, we present PULSAR, a novel method to screen for PD from webcam-recorded videos of the finger-tapping task from the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS). PULSAR is trained and evaluated on data collected from 382 participants (183 self-reported as PD patients). We used an adaptive graph convolutional neural network to dynamically learn the spatio temporal graph edges specific to the finger-tapping task. We enhanced this idea with a multi stream adaptive convolution model to learn features from different modalities of data critical to detect PD, such as relative location of the finger joints, velocity and acceleration of tapping. As the labels of the videos are self-reported, there could be cases of undiagnosed PD in the non-PD labeled samples. We leveraged the idea of Positive Unlabeled (PU) Learning that does not need labeled negative data. Our experiments show clear benefit of modeling the problem in this way. PULSAR achieved 80.95% accuracy in validation set and a mean accuracy of 71.29% (2.49% standard deviation) in independent test, despite being trained with limited amount of data. This is specially promising as labeled data is scarce in health care sector. We hope PULSAR will make PD screening more accessible to everyone. The proposed techniques could be extended for assessment of other movement disorders, such as ataxia, and Huntington's disease.

Related papers

• PSBD: Prediction Shift Uncertainty Unlocks Backdoor Detection [57.571451139201855]
  Prediction Shift Backdoor Detection (PSBD) is a novel method for identifying backdoor samples in deep neural networks. PSBD is motivated by an intriguing Prediction Shift (PS) phenomenon, where poisoned models' predictions on clean data often shift away from true labels towards certain other labels. PSBD identifies backdoor training samples by computing the Prediction Shift Uncertainty (PSU), the variance in probability values when dropout layers are toggled on and off during model inference.
  arXiv  Detail & Related papers  (2024-06-09T15:31:00Z)
• DDxT: Deep Generative Transformer Models for Differential Diagnosis [51.25660111437394]
  We show that a generative approach trained with simpler supervised and self-supervised learning signals can achieve superior results on the current benchmark. The proposed Transformer-based generative network, named DDxT, autoregressively produces a set of possible pathologies, i.e., DDx, and predicts the actual pathology using a neural network.
  arXiv  Detail & Related papers  (2023-12-02T22:57:25Z)
• A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals [8.526741765074677]
  This study presents a deep learning-based model for the diagnosis of Parkinson's disease (PD) using resting state electroencephalogram (EEG) signal. The model is designed using a hybrid model, consists of convolutional neural network (CNN), bidirectional gated recurrent unit (Bi-GRU) and attention mechanism. The results show that the proposed model can accurately diagnose PD with high performance on both the training and hold-out datasets.
  arXiv  Detail & Related papers  (2023-08-14T20:06:19Z)
• Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones [75.23250968928578]
  We present a method for predicting the medication status of Parkinson's disease patients using the public mPower dataset. The proposed method shows promising results in predicting three medication statuses objectively.
  arXiv  Detail & Related papers  (2022-07-26T02:08:08Z)
• Subgroup discovery of Parkinson's Disease by utilizing a multi-modal smart device system [63.20765930558542]
  We used smartwatches and smartphones to collect multi-modal data from 504 participants, including PD patients, DD and HC. We were able to show that by combining various modalities, classification accuracy improved and further PD clusters were discovered.
  arXiv  Detail & Related papers  (2022-05-12T08:59:57Z)
• FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss [58.58979566599889]
  We propose a novel self-supervised learning (FedMed) for brain image synthesis. An affine transform loss (ATL) was formulated to make use of severely distorted images without violating privacy legislation. The proposed method demonstrates advanced performance in both the quality of synthesized results under a severely misaligned and unpaired data setting.
  arXiv  Detail & Related papers  (2022-01-29T13:45:39Z)
• Linear Prediction Residual for Efficient Diagnosis of Parkinson's Disease from Gait [1.713291434132985]
  Parkinson's Disease (PD) is a chronic and progressive neurological disorder that results in rigidity, tremors and postural instability. There is no definite medical test to diagnose PD and diagnosis is mostly a clinical exercise. We propose LPGNet, a fast and accurate method to diagnose PD from gait.
  arXiv  Detail & Related papers  (2021-07-09T20:23:54Z)
• An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery [0.0]
  Parkinson's disease (PD) is a degenerative and progressive neurological condition. Early diagnosis can improve treatment for patients and is performed through dopaminergic imaging techniques like the SPECT DaTscan. In this study, we propose a machine learning model that accurately classifies any given DaTscan as having Parkinson's disease or not.
  arXiv  Detail & Related papers  (2020-08-01T10:44:03Z)
• Automated machine vision enabled detection of movement disorders from hand drawn spirals [0.0]
  This study uses a dataset of scanned pen and paper drawings and a convolutional neural network (CNN) to perform classification between Parkinson's disease (PD) and Essential tremor (ET) The discrimination accuracy of PD from controls was 98.2%. The discrimination accuracy of PD from ET and from controls was 92%.
  arXiv  Detail & Related papers  (2020-06-22T10:21:51Z)
• 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)
• Topological Descriptors for Parkinson's Disease Classification and Regression Analysis [3.2898781698366726]
  We propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson's disease classification and severity assessment. We propose a methodology which incorporates TDA into analyzing Parkinson's disease postural shifts data through the representation of persistence images. We explore the use of the proposed method in an application involving a Parkinson's disease dataset comprised of healthy-elderly, healthy-young and Parkinson's disease patients.
  arXiv  Detail & Related papers  (2020-04-15T22:59:07Z)

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.