Media Highlights: Voice-Based Digital Biomarkers Featured on SVT and P4 Blekinge
Media Highlights: Voice-Based Digital Biomarkers Featured on SVT and P4 Blekinge
Alper
I am pleased to share that my research on voice as a digital biomarker for chronic obstructive pulmonary disease (COPD) has recently been featured in both SVT Nyheter Blekinge and Sveriges Radio P4 Blekinge. Together with colleagues at the Health & Technology Research Lab, Blekinge Institute of Technology (BTH), I…
Prediction of Mini-Mental State Examination Scores for Cognitive Impairment and Machine Learning Analysis of Oral Health and Demographic Data Among Individuals Older Than 60 Years: Cross-Sectional Study
Prediction of Mini-Mental State Examination Scores for Cognitive Impairment and Machine Learning Analysis of Oral Health and Demographic Data Among Individuals Older Than 60 Years: Cross-Sectional Study
Alper Idrisoglu, Johan Flyborg, Sarah Nauman Ghazi, Elina Mikaelsson Midlöv, Helén Dellkvist, Anna Axén, Ana Luiza Dallora
Abstract Background:As the older population grows, so does the prevalence of cognitive impairment, emphasizing the importance of early diagnosis. The Mini-Mental State Examination (MMSE) is vital in identifying cognitive impairment. It is known that degraded oral health correlates with MMSE scores ≤26. Objective:This study aims to explore the potential of…
Vowel segmentation impact on machine learning classification for chronic obstructive pulmonary disease
Vowel segmentation impact on machine learning classification for chronic obstructive pulmonary disease
Alper Idrisoglu, Ana Luiza Dallora Moraes, Abbas Cheddad, Peter Anderberg, Andreas Jakobsson, Johan Sanmartin Berglund
Abstract:Abstract Vowel-based voice analysis is gaining attention as a potential non-invasive tool for COPD classification, offering insights into phonatory function. The growing need for voice data has necessitated the adoption of various techniques, including segmentation, to augment existing datasets for training comprehensive Machine Learning (ML) modelsThis study aims to investigate…
COPDVD: Automated classification of chronic obstructive pulmonary disease on a new collected and evaluated voice dataset
COPDVD: Automated classification of chronic obstructive pulmonary disease on a new collected and evaluated voice dataset
Alper
Authors: Alper Idrisoglu, Ana Luiza Dallora, Abbas Cheddad, Peter Anderberg, Andreas Jakobsson, Johan Sanmartin Berglund Abstract Background Chronic obstructive pulmonary disease (COPD) is a severe condition affecting millions worldwide, leading to numerous annual deaths. The absence of significant symptoms in its early stages promotes high underdiagnosis rates for the affected…
Use of machine learning and voice for multiclass classification of Parkinson’s disease, chronic obstructive pulmonary disease, and
Use of machine learning and voice for multiclass classification of Parkinson’s disease, chronic obstructive pulmonary disease, and
Alper Idrisoglu, Anders Behrens
Abstract:Parkinson’s disease (PD) and chronic obstructive pulmonary disease (COPD) are prevalent conditions with substantial impact on quality of life and health care systems. Both disorders affect voice production through different physiological mechanisms, yet neither condition has a widely adopted objective…
Vowel segmentation impact on machine learning classification for chronic obstructive pulmonary disease
Vowel segmentation impact on machine learning classification for chronic obstructive pulmonary disease
Alper Idrisoglu, Ana Luiza Dallora Moraes, Abbas Cheddad, Peter Anderberg, Andreas Jakobsson, Johan Sanmartin Berglund
Abstract:Abstract Vowel-based voice analysis is gaining attention as a potential non-invasive tool for COPD classification, offering insights into phonatory function. The growing need for voice data has necessitated the adoption of various techniques, including segmentation, to augment existing datasets for…