Voice Pathology Detection Using Machine Learning Algorithms Based on Different Voice Databases

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 103937 - 103937

Published: Jan. 1, 2025

Language: Английский

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A data ensemble-based approach for detecting vocal disorders using replicated acoustic biomarkers from electroglottography

Sensing and Bio-Sensing Research, Journal Year: 2025, Volume and Issue: 47, P. 100741 - 100741

Published: Jan. 16, 2025

Language: Английский

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Applications of Artificial Intelligence in Neurological Voice Disorders

World Journal of Otorhinolaryngology - Head and Neck Surgery, Journal Year: 2025, Volume and Issue: unknown

Published: April 22, 2025

Language: Английский

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Exploring Vocal Challenges In Individuals With Parkinson's Disease: Insights From The Social And Family Sphere

Journal Of Advanced Zoology, Journal Year: 2024, Volume and Issue: unknown, P. 269 - 276

Published: Feb. 22, 2024

Background: Parkinson's disease (PD) is a multifaceted neurodegenerative condition manifesting in adulthood, characterized by spectrum of motor and non-motor symptoms. The alteration the voice individuals with PD can significantly impact their communication dynamics surrounding environment, influencing perceptions those around them. Objective: This study aims to assess perception vocal difficulties from perspective socio-family environment. Methodology: A cross-sectional descriptive observational was conducted involving 17 relatives disease, comprising family members friends acting as primary caregivers city Lahore. Data were collected through in-person surveys, utilizing an adapted version VHI-30 questionnaire. Results: Of participants, 71.2% aged over 40, 76.5% being women, spending more than 5 hours daily afflicted PD. significant 76.4% acknowledged specific related PD-affected member, considering functional, physical, emotional criteria. Within this group, 29.4% reported mild moderate difficulties, while 47% severe very difficulties. Conclusion: Family exhibit varying degrees regarding challenges faced disease. Recognizing on aspects crucial. Inclusive therapeutic interventions that involve environment are essential for effectively addressing posed

Language: Английский

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Deep Learning-Based Method for Detecting Parkinson using 1D Convolutional Neural Networks and Improved Jellyfish Algorithms

International journal of electrical and computer engineering systems, Journal Year: 2024, Volume and Issue: 15(6), P. 515 - 522

Published: June 7, 2024

Parkinson's disease (PD) is a common that predominantly impacts the motor scheme of neural central scheme. While primary symptoms overlap with those other conditions, an accurate diagnosis typically relies on extensive neurological, psychiatric, and physical examinations. Consequently, numerous autonomous diagnostic assistance systems, based machine learning (ML) methodologies, have emerged to assist in evaluating patients PD. This work proposes novel deep learning-based classification using voice recordings people into normal, idiopathic Parkinson, familial Parkinson. The improved jellyfish algorithm (IJFA) utilized for hyper-parameter selection (HPS) 1D convolutional network (1D-CNN). proposed technique makes use significant elements 1D-CNN filter-based feature models. Because their strong performance dealing noisy data, algorithms Relief, mRMR, Fisher Score were chosen as top choices. Using just 62 characteristics, combination relief features was able discriminate between people. competence IJFA method determined through specific metrics. attains total accuracy 98.6%, which comparatively better than existing techniques. model produced around 9.5% improvements accuracy, respectively, when compared data obtained without dimensionality reduction.

Language: Английский

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Review of voice biomarkers in the screening of neurodegenerative diseases

Interdisciplinary Nursing Research, Journal Year: 2024, Volume and Issue: 3(3), P. 190 - 198

Published: Sept. 1, 2024

Abstract Neurodegenerative diseases significantly impact patients and their families, making early identification crucial for improving patients’ quality of life reducing care burdens. Current screening methods neurodegenerative diseases, such as dementia mild cognitive impairment, still rely on subjective assessments or expensive techniques like invasive cerebrospinal fluid analysis magnetic resonance imaging. These factors make challenging. Voice biomarkers present a promising alternative convenient, noninvasive, low-cost tools. With the application development artificial intelligence big data, prediction based voice data have become research focus. This article reviews progress in disease classification. It summarizes relevant studies both single multimodal identifies existing challenges, suggests future directions to enhance contexts.

Language: Английский

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Eeg-Based Bidirectional Recurrent Neural Network in Sleep Apnea Incident Detection

Published: Jan. 1, 2024

Language: Английский

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Improving Voice Pathology Classification Using Artificial Data Generation

Procedia Computer Science, Journal Year: 2024, Volume and Issue: 246, P. 5175 - 5184

Published: Jan. 1, 2024

Language: Английский

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MSDFEN: Multi-scale dynamic feature extraction network for pathological voice detection

Applied Acoustics, Journal Year: 2024, Volume and Issue: 230, P. 110438 - 110438

Published: Nov. 28, 2024

Language: Английский

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