A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos

• URL: http://arxiv.org/abs/2505.03845v1
• Date: Mon, 05 May 2025 10:58:39 GMT
• Title: A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos
• Authors: Ioannis Kyprakis, Vasileios Skaramagkas, Iro Boura, Georgios Karamanis, Dimitrios I. Fotiadis, Zinovia Kefalopoulou, Cleanthe Spanaki, Manolis Tsiknakis,
• Abstract summary: Parkinson's disease (PD) is a neurodegenerative disorder, manifesting with motor and non-motor symptoms.<n>Depressive symptoms are prevalent in PD, affecting up to 45% of patients.<n>This study explores deep learning (DL) models-ViViT, Video Swin Tiny, and 3D CNN-LSTM with attention layers-to assess the presence and severity of depressive symptoms.
• Score: 1.3723311422775535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parkinson's disease (PD) is a neurodegenerative disorder, manifesting with motor and non-motor symptoms. Depressive symptoms are prevalent in PD, affecting up to 45% of patients. They are often underdiagnosed due to overlapping motor features, such as hypomimia. This study explores deep learning (DL) models-ViViT, Video Swin Tiny, and 3D CNN-LSTM with attention layers-to assess the presence and severity of depressive symptoms, as detected by the Geriatric Depression Scale (GDS), in PD patients through facial video analysis. The same parameters were assessed in a secondary analysis taking into account whether patients were one hour after (ON-medication state) or 12 hours without (OFF-medication state) dopaminergic medication. Using a dataset of 1,875 videos from 178 patients, the Video Swin Tiny model achieved the highest performance, with up to 94% accuracy and 93.7% F1-score in binary classification (presence of absence of depressive symptoms), and 87.1% accuracy with an 85.4% F1-score in multiclass tasks (absence or mild or severe depressive symptoms).

Related papers

• We Care: Multimodal Depression Detection and Knowledge Infused Mental Health Therapeutic Response Generation [41.09752906121257]
  We present the Extended D-vlog dataset, encompassing a collection of 1, 261 YouTube vlogs. We introduce a virtual agent serving as an initial contact for mental health patients, offering Cognitive Behavioral Therapy (CBT)-based responses. Our Mistral model achieved impressive scores of 70.1% and 30.9% for distortion assessment and classification, along with a Bert score of 88.7%.
  arXiv  Detail & Related papers  (2024-06-15T08:41:46Z)
• 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)
• Unmasking Parkinson's Disease with Smile: An AI-enabled Screening Framework [2.4914378616592505]
  This dataset includes 256 individuals with PD, 165 clinically diagnosed, and 91 self-reported. participants used webcams to record themselves mimicking three facial expressions. Facial landmarks are automatically tracked from the recordings to extract features related to hypomimia. Machine learning algorithms are trained on these features to distinguish between individuals with and without PD.
  arXiv  Detail & Related papers  (2023-08-03T18:23:37Z)
• 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)
• 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)
• Intelligent Sight and Sound: A Chronic Cancer Pain Dataset [74.77784420691937]
  This paper introduces the first chronic cancer pain dataset, collected as part of the Intelligent Sight and Sound (ISS) clinical trial. The data collected to date consists of 29 patients, 509 smartphone videos, 189,999 frames, and self-reported affective and activity pain scores. Using static images and multi-modal data to predict self-reported pain levels, early models show significant gaps between current methods available to predict pain.
  arXiv  Detail & Related papers  (2022-04-07T22:14:37Z)
• Perceptual Features as Markers of Parkinson's Disease: The Issue of Clinical Interpretability [0.0]
  Up to 90% of patients with Parkinson's disease (PD) suffer from hypokinetic dysathria (HD) This paper provides large and robust insight into analysis of 5 Czech vowels of 84 PD patients.
  arXiv  Detail & Related papers  (2022-03-21T09:46:48Z)
• A multimodal approach for Parkinson disease analysis [0.0]
  Parkinson's disease (PD) is the second most frequent neurodegenerative disease with prevalence among general population reaching 0.1-1 %. The most obvious symptoms are movement-related; these include tremor, rigidity, slowness of movement and walking difficulties. In this paper we will present an ongoing project that will evaluate if voice and handwriting analysis can be reliable predictors/indicators of swallowing and balance impairments in PD.
  arXiv  Detail & Related papers  (2022-03-10T08:37:33Z)
• Deep Multi-task Learning for Depression Detection and Prediction in Longitudinal Data [50.02223091927777]
  Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. We introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks.
  arXiv  Detail & Related papers  (2020-12-05T05:14:14Z)
• 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)
• 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.