Detecting Parkinson's Disease From an Online Speech-task

• URL: http://arxiv.org/abs/2009.01231v4
• Date: Tue, 15 Dec 2020 21:08:05 GMT
• Title: Detecting Parkinson's Disease From an Online Speech-task
• Authors: Wasifur Rahman, Sangwu Lee, Md. Saiful Islam, Victor Nikhil Antony, Harshil Ratnu, Mohammad Rafayet Ali, Abdullah Al Mamun, Ellen Wagner, Stella Jensen-Roberts, Max A. Little, Ray Dorsey, and Ehsan Hoque
• Abstract summary: In this paper, we envision a web-based framework that can help anyone, anywhere around the world record a short speech task, and analyze the recorded data to screen for Parkinson's disease (PD) We collected data from 726 unique participants (262 PD, 38% female; 464 non-PD, 65% female; average age: 61) from all over the US and beyond. We extracted both standard acoustic features (MFCC), jitter and shimmer variants, and deep learning based features from the speech data. Our model performed equally well on data collected in controlled lab environment as well as 'in the wild'
• Score: 4.968576908394359
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this paper, we envision a web-based framework that can help anyone, anywhere around the world record a short speech task, and analyze the recorded data to screen for Parkinson's disease (PD). We collected data from 726 unique participants (262 PD, 38% female; 464 non-PD, 65% female; average age: 61) -- from all over the US and beyond. A small portion of the data was collected in a lab setting to compare quality. The participants were instructed to utter a popular pangram containing all the letters in the English alphabet "the quick brown fox jumps over the lazy dog..". We extracted both standard acoustic features (Mel Frequency Cepstral Coefficients (MFCC), jitter and shimmer variants) and deep learning based features from the speech data. Using these features, we trained several machine learning algorithms. We achieved 0.75 AUC (Area Under The Curve) performance on determining presence of self-reported Parkinson's disease by modeling the standard acoustic features through the XGBoost -- a gradient-boosted decision tree model. Further analysis reveal that the widely used MFCC features and a subset of previously validated dysphonia features designed for detecting Parkinson's from verbal phonation task (pronouncing 'ahh') contains the most distinct information. Our model performed equally well on data collected in controlled lab environment as well as 'in the wild' across different gender and age groups. Using this tool, we can collect data from almost anyone anywhere with a video/audio enabled device, contributing to equity and access in neurological care.

Related papers

• A Novel Fusion Architecture for PD Detection Using Semi-Supervised Speech Embeddings [8.996456485141069]
  We present a framework to recognize Parkinson's disease (PD) through an English pangram utterance speech collected using a web application. Our dataset includes a global cohort of 1306 participants, including 392 diagnosed with PD. We used deep learning embeddings derived from semi-supervised models such as Wav2Vec 2.0, WavLM, and ImageBind representing the speech dynamics associated with PD.
  arXiv  Detail & Related papers  (2024-05-21T16:06:51Z)
• NeuroVoz: a Castillian Spanish corpus of parkinsonian speech [36.23298373892936]
  NeuroVoz is composed by 2,903 audio recordings averaging $26.88 pm 3.35$ recordings per participant. This dataset has already underpinned several studies, achieving a benchmark accuracy of 89% in PD speech pattern identification.
  arXiv  Detail & Related papers  (2024-03-04T16:17:39Z)
• 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)
• Decoding speech perception from non-invasive brain recordings [48.46819575538446]
  We introduce a model trained with contrastive-learning to decode self-supervised representations of perceived speech from non-invasive recordings. Our model can identify, from 3 seconds of MEG signals, the corresponding speech segment with up to 41% accuracy out of more than 1,000 distinct possibilities.
  arXiv  Detail & Related papers  (2022-08-25T10:01:43Z)
• 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)
• Parkinson's disease diagnostics using AI and natural language knowledge transfer [0.0]
  Deep learning approach for classification of raw speech recordings in patients with diagnosed PD was proposed. Method was tested on a group of 38 PD patients and 10 healthy persons above the age of 50.
  arXiv  Detail & Related papers  (2022-04-26T19:39:29Z)
• Automatic Dialect Density Estimation for African American English [74.44807604000967]
  We explore automatic prediction of dialect density of the African American English (AAE) dialect. dialect density is defined as the percentage of words in an utterance that contain characteristics of the non-standard dialect. We show a significant correlation between our predicted and ground truth dialect density measures for AAE speech in this database.
  arXiv  Detail & Related papers  (2022-04-03T01:34:48Z)
• Comparative Study of Speech Analysis Methods to Predict Parkinson's Disease [0.0]
  Speech disorders can be used to detect this disease before it degenerates. This work analyzes speech features and machine learning approaches to predict PD. Using all the acoustic features and MFCC, together with SVM produced the highest performance with an accuracy of 98%.
  arXiv  Detail & Related papers  (2021-11-15T04:29:51Z)
• 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.