Topological Descriptors for Parkinson's Disease Classification and
Regression Analysis
- URL: http://arxiv.org/abs/2004.07384v2
- Date: Wed, 6 May 2020 04:09:16 GMT
- Title: Topological Descriptors for Parkinson's Disease Classification and
Regression Analysis
- Authors: Afra Nawar, Farhan Rahman, Narayanan Krishnamurthi, Anirudh Som and
Pavan Turaga
- Abstract summary: 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.
- Score: 3.2898781698366726
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: At present, the vast majority of human subjects with neurological disease are
still diagnosed through in-person assessments and qualitative analysis of
patient data. In this paper, 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. An automated, stable, and
accurate method to evaluate Parkinson's would be significant in streamlining
diagnoses of patients and providing families more time for corrective measures.
We propose a methodology which incorporates TDA into analyzing Parkinson's
disease postural shifts data through the representation of persistence images.
Studying the topology of a system has proven to be invariant to small changes
in data and has been shown to perform well in discrimination tasks. The
contributions of the paper are twofold. We propose a method to 1) classify
healthy patients from those afflicted by disease and 2) diagnose the severity
of disease. 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. Our code is available at
https://github.com/itsmeafra/Sublevel-Set-TDA.
Related papers
- Determining the severity of Parkinson's disease in patients using a
multi task neural network [0.7499722271664147]
Parkinson's disease is easy to diagnose when it is advanced, but difficult to diagnose in its early stages.
This study analyzes a set of variables that can be easily extracted from voice analysis.
A success rate of 99.15% has been achieved in the problem of predicting whether a person suffers from severe Parkinson's disease or non-severe Parkinson's disease.
arXiv Detail & Related papers (2024-02-08T08:55:34Z) - Predicting Parkinson's disease evolution using deep learning [1.4610685586329806]
Parkinson's disease is a neurological condition that occurs in nearly 1% of the world's population.
There is not a single blood test or biomarker available to diagnose Parkinson's disease.
No AI tools have been designed to identify the stage of progression.
arXiv Detail & Related papers (2023-12-28T10:30:54Z) - PULSAR: Graph based Positive Unlabeled Learning with Multi Stream
Adaptive Convolutions for Parkinson's Disease Recognition [1.9482539692051932]
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.
arXiv Detail & Related papers (2023-12-10T05:56:20Z) - Deep Learning Predicts Prevalent and Incident Parkinson's Disease From
UK Biobank Fundus Imaging [13.132022790511005]
Parkinson's disease is the world's fastest-growing neurological disorder.
Current diagnostic methods are expensive and have limited availability.
We highlight retinal fundus imaging, often termed a window to the brain, as a diagnostic screening modality for Parkinson's disease.
arXiv Detail & Related papers (2023-02-13T22:30:16Z) - 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) - Personalized pathology test for Cardio-vascular disease: Approximate
Bayesian computation with discriminative summary statistics learning [48.7576911714538]
We propose a platelet deposition model and an inferential scheme to estimate the biologically meaningful parameters using approximate computation.
This work opens up an unprecedented opportunity of personalized pathology test for CVD detection and medical treatment.
arXiv Detail & Related papers (2020-10-13T15:20:21Z) - 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) - Learning-based Computer-aided Prescription Model for Parkinson's
Disease: A Data-driven Perspective [61.70045118068213]
We build a dataset by collecting symptoms of PD patients, and their prescription drug provided by neurologists.
Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug.
For the new coming patients, we could recommend (predict) suitable prescription drug on their observed symptoms by our prescription model.
arXiv Detail & Related papers (2020-07-31T14:34:35Z) - Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing
Parkinson's Disease Motor Severity [39.51722822896373]
Parkinson's disease (PD) is a progressive neurological disorder affecting motor function.
Physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson's Disease Rating Scale.
We propose a computer vision-based model that observes non-intrusive video recordings of individuals, extracts their 3D body skeletons, tracks them through time, and classifies the movements according to the MDS-UPDRS gait scores.
arXiv Detail & Related papers (2020-07-17T11:49:30Z) - Analysis and Evaluation of Handwriting in Patients with Parkinson's
Disease Using kinematic, Geometrical, and Non-linear Features [0.0]
Handwriting analysis can help in supporting the diagnosis and in monitoring the progress of Parkinson's disease.
This paper aims to evaluate the importance of different groups of features to model handwriting deficits that appear due to Parkinson's disease.
arXiv Detail & Related papers (2020-02-13T09:54:41Z) - Learning Dynamic and Personalized Comorbidity Networks from Event Data
using Deep Diffusion Processes [102.02672176520382]
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals.
In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition.
We develop deep diffusion processes to model "dynamic comorbidity networks"
arXiv Detail & Related papers (2020-01-08T15:47:08Z)
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.