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A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction

Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson’s Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory Stimulation (RAS) can be used t...

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Autores principales: Ashfaque Mostafa, Tahjid, Soltaninejad, Sara, McIsaac, Tara L., Cheng, Irene
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512068/
https://www.ncbi.nlm.nih.gov/pubmed/34640763
http://dx.doi.org/10.3390/s21196446
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author Ashfaque Mostafa, Tahjid
Soltaninejad, Sara
McIsaac, Tara L.
Cheng, Irene
author_facet Ashfaque Mostafa, Tahjid
Soltaninejad, Sara
McIsaac, Tara L.
Cheng, Irene
author_sort Ashfaque Mostafa, Tahjid
collection PubMed
description Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson’s Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory Stimulation (RAS) can be used to help patients recover from FOG and resume normal gait. RAS might be ineffective due to the latency between the start of a FOG event, its detection and initialization of RAS. We propose a system capable of both FOG prediction and detection using signals from tri-axial accelerometer sensors that will be useful in initializing RAS with minimal latency. We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). We also propose three Ensemble Network Architectures that combine all the time frequency representations and DL architectures. Experimental results show that our ensemble architectures significantly improve the performance compared with existing techniques. We also present the results of applying our method trained on a publicly available dataset to data collected from patients using wearable sensors in collaboration with A.T. Still University.
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spelling pubmed-85120682021-10-14 A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction Ashfaque Mostafa, Tahjid Soltaninejad, Sara McIsaac, Tara L. Cheng, Irene Sensors (Basel) Article Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson’s Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory Stimulation (RAS) can be used to help patients recover from FOG and resume normal gait. RAS might be ineffective due to the latency between the start of a FOG event, its detection and initialization of RAS. We propose a system capable of both FOG prediction and detection using signals from tri-axial accelerometer sensors that will be useful in initializing RAS with minimal latency. We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). We also propose three Ensemble Network Architectures that combine all the time frequency representations and DL architectures. Experimental results show that our ensemble architectures significantly improve the performance compared with existing techniques. We also present the results of applying our method trained on a publicly available dataset to data collected from patients using wearable sensors in collaboration with A.T. Still University. MDPI 2021-09-27 /pmc/articles/PMC8512068/ /pubmed/34640763 http://dx.doi.org/10.3390/s21196446 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ashfaque Mostafa, Tahjid
Soltaninejad, Sara
McIsaac, Tara L.
Cheng, Irene
A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction
title A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction
title_full A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction
title_fullStr A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction
title_full_unstemmed A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction
title_short A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction
title_sort comparative study of time frequency representation techniques for freeze of gait detection and prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512068/
https://www.ncbi.nlm.nih.gov/pubmed/34640763
http://dx.doi.org/10.3390/s21196446
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