PPINtonus: Early Detection of Parkinson's Disease Using Deep-Learning Tonal Analysis

• URL: http://arxiv.org/abs/2406.02608v1
• Date: Mon, 3 Jun 2024 01:07:42 GMT
• Title: PPINtonus: Early Detection of Parkinson's Disease Using Deep-Learning Tonal Analysis
• Authors: Varun Reddy,
• Abstract summary: PPINtonus is a system for the early detection of Parkinson's Disease. It uses deep-learning tonal analysis to provide an alternative to neurological examinations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: PPINtonus is a system for the early detection of Parkinson's Disease (PD) utilizing deep-learning tonal analysis, providing a cost-effective and accessible alternative to traditional neurological examinations. Partnering with the Parkinson's Voice Project (PVP), PPINtonus employs a semi-supervised conditional generative adversarial network to generate synthetic data points, enhancing the training dataset for a multi-layered deep neural network. Combined with PRAAT phonetics software, this network accurately assesses biomedical voice measurement values from a simple 120-second vocal test performed with a standard microphone in typical household noise conditions. The model's performance was validated using a confusion matrix, achieving an impressive 92.5 \% accuracy with a low false negative rate. PPINtonus demonstrated a precision of 92.7 \%, making it a reliable tool for early PD detection. The non-intrusive and efficient methodology of PPINtonus can significantly benefit developing countries by enabling early diagnosis and improving the quality of life for millions of PD patients through timely intervention and management.

Related papers

• Early Recognition of Parkinson's Disease Through Acoustic Analysis and Machine Learning [0.0]
  Parkinson's Disease (PD) is a progressive neurodegenerative disorder that significantly impacts both motor and non-motor functions, including speech. This paper provides a comprehensive review of methods for PD recognition using speech data, highlighting advances in machine learning and data-driven approaches. Various classification algorithms are explored, including logistic regression, SVM, and neural networks, with and without feature selection. Our findings indicate that specific acoustic features and advanced machine-learning techniques can effectively differentiate between individuals with PD and healthy controls.
  arXiv  Detail & Related papers  (2024-07-22T23:24:02Z)
• Evaluating Echo State Network for Parkinson's Disease Prediction using Voice Features [1.2289361708127877]
  This study aims to develop a diagnostic model capable of achieving both high accuracy and minimizing false negatives. Various machine learning methods, including Echo State Networks (ESN), Random Forest, k-nearest Neighbors, Support Vector, Extreme Gradient Boosting, and Decision Tree, are employed and thoroughly evaluated. ESN consistently maintains a false negative rate of less than 8% in 83% of cases.
  arXiv  Detail & Related papers  (2024-01-28T14:39:43Z)
• Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms [0.0]
  This study explores the potential of vocal feature alterations in PD patients as a means of early disease prediction. utilizing a variety of advanced machine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost, and Support Vector Machine. LightGBM exhibited a remarkable sensitivity of 100% and specificity of 94.43%, surpassing other machine learning algorithms in accuracy and AUC scores.
  arXiv  Detail & Related papers  (2023-11-09T15:21:10Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Early Detection of Parkinson's Disease using Motor Symptoms and Machine Learning [0.0]
  This work aims at focusing on early-occurring, common symptoms, such as motor and gait related parameters, to arrive at a quantitative analysis on the feasibility of an economical and a robust wearable device. A subset of the Parkinson's Progression Markers Initiative (PPMI), PPMI Gait dataset has been utilised for feature-selection. Identified influential features has then been used to test real-time data for early detection of Parkinson Syndrome, with a model accuracy of 91.9%.
  arXiv  Detail & Related papers  (2023-04-18T19:13:05Z)
• Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection [70.86672569101536]
  Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and to delay further progression. This paper investigates the use of prompt-based fine-tuning of PLMs that consistently uses AD classification errors as the training objective function.
  arXiv  Detail & Related papers  (2022-10-29T09:18:41Z)
• 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)
• Pose-based Tremor Classification for Parkinson's Disease Diagnosis from Video [13.6403722052414]
  Parkinson's disease (PD) is a progressive neurodegenerative disorder that results in a variety of motor dysfunction symptoms. Parkinson's tremor is one of the most predominant symptoms of PD with strong generalizability. We propose SPAPNet, which only requires consumer-grade non-intrusive video recording of camera-facing human movements as input to provide undiagnosed patients with low-cost PT classification results as a PD warning sign.
  arXiv  Detail & Related papers  (2022-07-14T11:32:42Z)
• Reducing a complex two-sided smartwatch examination for Parkinson's Disease to an efficient one-sided examination preserving machine learning accuracy [63.20765930558542]
  We have recorded participants performing technology-based assessments in a prospective study to research Parkinson's Disease (PD) This study provided the largest PD sample size of two-hand synchronous smartwatch measurements.
  arXiv  Detail & Related papers  (2022-05-11T09:12:59Z)
• 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)

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.