Multi Class Parkinsons Disease Detection Based on Finger Tapping Using Attention-Enhanced CNN BiLSTM

• URL: http://arxiv.org/abs/2510.10121v1
• Date: Sat, 11 Oct 2025 09:02:14 GMT
• Title: Multi Class Parkinsons Disease Detection Based on Finger Tapping Using Attention-Enhanced CNN BiLSTM
• Authors: Abu Saleh Musa Miah, Najmul Hassan, Md Maruf Al Hossain, Yuichi Okuyama, Jungpil Shin,
• Abstract summary: We propose a multi-class Parkinson Disease detection system based on finger tapping using an attention-enhanced CNN BiLSTM.<n>We proposed a hybrid deep learning framework integrating CNN, BiLSTM, and attention mechanisms for multi-class PD severity classification.<n>The model demonstrated strong performance in distinguishing between the five severity classes.
• Score: 2.5227912984325362
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective clinical management and intervention development depend on accurate evaluation of Parkinsons disease (PD) severity. Many researchers have worked on developing gesture-based PD recognition systems; however, their performance accuracy is not satisfactory. In this study, we propose a multi-class Parkinson Disease detection system based on finger tapping using an attention-enhanced CNN BiLSTM. We collected finger tapping videos and derived temporal, frequency, and amplitude based features from wrist and hand movements. Then, we proposed a hybrid deep learning framework integrating CNN, BiLSTM, and attention mechanisms for multi-class PD severity classification from video-derived motion features. First, the input sequence is reshaped and passed through a Conv1D MaxPooling block to capture local spatial dependencies. The resulting feature maps are fed into a BiLSTM layer to model temporal dynamics. An attention mechanism focuses on the most informative temporal features, producing a context vector that is further processed by a second BiLSTM layer. CNN-derived features and attention-enhanced BiLSTM outputs are concatenated, followed by dense and dropout layers, before the final softmax classifier outputs the predicted PD severity level. The model demonstrated strong performance in distinguishing between the five severity classes, suggesting that integrating spatial temporal representations with attention mechanisms can improve automated PD severity detection, making it a promising non-invasive tool to support clinicians in PD monitoring and progression tracking.

Related papers

• A Hybrid CNN and ML Framework for Multi-modal Classification of Movement Disorders Using MRI and Brain Structural Features [3.176001912624283]
  Atypical Parkinsonian Disorders (APD) are a group of neurodegenerative diseases that include progressive supranuclear palsy (PSP) and multiple system atrophy (MSA)<n>In the early stages, overlapping clinical features often lead to misdiagnosis as Parkinson's disease (PD)
  arXiv  Detail & Related papers  (2026-02-05T11:57:20Z)
• Digital FAST: An AI-Driven Multimodal Framework for Rapid and Early Stroke Screening [0.7136933021609076]
  This study presents a fast, non-invasive multimodal deep learning framework for automatic binary stroke screening based on data collected during the F.A.S.T. assessment.<n>The proposed approach integrates complementary information from facial expressions, speech signals, and upper-body movements to enhance diagnostic robustness.
  arXiv  Detail & Related papers  (2026-01-17T03:35:39Z)
• MEET-Sepsis: Multi-Endogenous-View Enhanced Time-Series Representation Learning for Early Sepsis Prediction [50.74903497515021]
  Sepsis is a life-threatening infectious syndrome associated with high mortality in intensive care units (ICUs)<n>Early and accurate sepsis prediction (SP) is critical for timely intervention, yet remains challenging due to subtle early manifestations and rapidly escalating mortality.<n>This paper introduces a Multi-Endogenous-view Representation Enhancement (MERE) mechanism to construct enriched feature views, coupled with a Cascaded Dual-convolution Time-series Attention (CDTA) module for temporal representation learning.<n>The proposed MEET-Sepsis framework achieves competitive prediction accuracy using only 20% of the ICU monitoring time required by SOTA methods, significantly advancing
  arXiv  Detail & Related papers  (2025-10-13T13:38:29Z)
• Brain2Vec: A Deep Learning Framework for EEG-Based Stress Detection Using CNN-LSTM-Attention [0.0]
  Brain2Vec is a new deep learning tool that classifies stress states from raw EEG recordings.<n>These findings demonstrate Brain2Vec's potential for integration into wearable stress monitoring platforms and personalized healthcare systems.
  arXiv  Detail & Related papers  (2025-06-12T12:57:19Z)
• PhysLLM: Harnessing Large Language Models for Cross-Modal Remote Physiological Sensing [49.243031514520794]
  Large Language Models (LLMs) excel at capturing long-range signals due to their text-centric design.<n>PhysLLM achieves state-the-art accuracy and robustness, demonstrating superior generalization across lighting variations and motion scenarios.
  arXiv  Detail & Related papers  (2025-05-06T15:18:38Z)
• Electromyography-Based Gesture Recognition: Hierarchical Feature Extraction for Enhanced Spatial-Temporal Dynamics [0.7083699704958353]
  We propose a lightweight squeeze-excitation deep learning-based multi stream spatial temporal dynamics time-varying feature extraction approach.<n>The proposed model was tested on the Ninapro DB2, DB4, and DB5 datasets, achieving accuracy rates of 96.41%, 92.40%, and 93.34%, respectively.
  arXiv  Detail & Related papers  (2025-04-04T07:11:12Z)
• GS-TransUNet: Integrated 2D Gaussian Splatting and Transformer UNet for Accurate Skin Lesion Analysis [44.99833362998488]
  We present a novel approach that combines 2D Gaussian splatting with the Transformer UNet architecture for automated skin cancer diagnosis.<n>Our findings illustrate significant advancements in the precision of segmentation and classification.<n>This integration sets new benchmarks in the field and highlights the potential for further research into multi-task medical image analysis methodologies.
  arXiv  Detail & Related papers  (2025-02-23T23:28:47Z)
• Motion-enhanced Cardiac Anatomy Segmentation via an Insertable Temporal Attention Module [5.796175310950299]
  We propose a novel, computationally efficient Temporal Attention Module (TAM) that offers robust motion enhancement.<n>TAM's uniqueness is that it is a lightweight, plug-and-play module that can be inserted into a broad range of segmentation networks.<n>Experiments on multiple 2D and 3D cardiac ultrasound and MRI datasets confirm that TAM consistently improves segmentation across a range of networks.
  arXiv  Detail & Related papers  (2025-01-24T21:35:24Z)
• L-SFAN: Lightweight Spatially-focused Attention Network for Pain Behavior Detection [44.016805074560295]
  Chronic Low Back Pain (CLBP) afflicts millions globally, significantly impacting individuals' well-being and imposing economic burdens on healthcare systems. While artificial intelligence (AI) and deep learning offer promising avenues for analyzing pain-related behaviors to improve rehabilitation strategies, current models, including convolutional neural networks (CNNs), have limitations. We introduce hbox EmoL-SFAN, a lightweight CNN architecture incorporating 2D filters designed to capture the spatial-temporal interplay of data from motion capture and surface electromyography sensors.
  arXiv  Detail & Related papers  (2024-06-07T12:01:37Z)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• Estimating Motor Symptom Presence and Severity in Parkinson's Disease from Wrist Accelerometer Time Series using ROCKET and InceptionTime [4.1125736844411644]
  We investigate InceptionTime and RandOm Convolutional KErnel Transform as promising for Parkinson's symptom monitoring.<n>InceptionTime's high learning capacity is well-suited to modeling complex movement patterns, while ROCKET is suited to small datasets.<n>Our findings indicate that all approaches can learn to estimate tremor severity and bradykinesia presence with moderate performance but encounter challenges in detecting dyskinesia.
  arXiv  Detail & Related papers  (2023-04-21T22:38:44Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• 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.