Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features
A novel identification algorithm using a deep learning approach was developed in this study to classify neurodegenerative diseases (NDDs) based on the vertical ground reaction force (vGRF) signal. The irregularity of NDD vGRF signals caused by gait abnormalities can indicate different force pattern...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/11/7/902 |
| _version_ | 1797527557163188224 |
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| author | Febryan Setiawan Che-Wei Lin |
| author_facet | Febryan Setiawan Che-Wei Lin |
| author_sort | Febryan Setiawan |
| collection | DOAJ |
| description | A novel identification algorithm using a deep learning approach was developed in this study to classify neurodegenerative diseases (NDDs) based on the vertical ground reaction force (vGRF) signal. The irregularity of NDD vGRF signals caused by gait abnormalities can indicate different force pattern variations compared to a healthy control (HC). The main purpose of this research is to help physicians in the early detection of NDDs, efficient treatment planning, and monitoring of disease progression. The detection algorithm comprises a preprocessing process, a feature transformation process, and a classification process. In the preprocessing process, the five-minute vertical ground reaction force signal was divided into 10, 30, and 60 s successive time windows. In the feature transformation process, the time–domain vGRF signal was modified into a time–frequency spectrogram using a continuous wavelet transform (CWT). Then, feature enhancement with principal component analysis (PCA) was utilized. Finally, a convolutional neural network, as a deep learning classifier, was employed in the classification process of the proposed detection algorithm and evaluated using leave-one-out cross-validation (LOOCV) and <i>k</i>-fold cross-validation (<i>k</i>-fold CV, <i>k</i> = 5). The proposed detection algorithm can effectively differentiate gait patterns based on a time–frequency spectrogram of a vGRF signal between HC subjects and patients with neurodegenerative diseases. |
| first_indexed | 2024-03-10T09:45:23Z |
| format | Article |
| id | doaj.art-c5d3cf73301b488fa9c2afb0e48dcb34 |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-10T09:45:23Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-c5d3cf73301b488fa9c2afb0e48dcb342023-11-22T03:20:27ZengMDPI AGBrain Sciences2076-34252021-07-0111790210.3390/brainsci11070902Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network FeaturesFebryan Setiawan0Che-Wei Lin1Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan 701, TaiwanDepartment of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan 701, TaiwanA novel identification algorithm using a deep learning approach was developed in this study to classify neurodegenerative diseases (NDDs) based on the vertical ground reaction force (vGRF) signal. The irregularity of NDD vGRF signals caused by gait abnormalities can indicate different force pattern variations compared to a healthy control (HC). The main purpose of this research is to help physicians in the early detection of NDDs, efficient treatment planning, and monitoring of disease progression. The detection algorithm comprises a preprocessing process, a feature transformation process, and a classification process. In the preprocessing process, the five-minute vertical ground reaction force signal was divided into 10, 30, and 60 s successive time windows. In the feature transformation process, the time–domain vGRF signal was modified into a time–frequency spectrogram using a continuous wavelet transform (CWT). Then, feature enhancement with principal component analysis (PCA) was utilized. Finally, a convolutional neural network, as a deep learning classifier, was employed in the classification process of the proposed detection algorithm and evaluated using leave-one-out cross-validation (LOOCV) and <i>k</i>-fold cross-validation (<i>k</i>-fold CV, <i>k</i> = 5). The proposed detection algorithm can effectively differentiate gait patterns based on a time–frequency spectrogram of a vGRF signal between HC subjects and patients with neurodegenerative diseases.https://www.mdpi.com/2076-3425/11/7/902gait analysisneuro-degenerative diseasestime–frequency spectrogramdeep learningvertical ground reaction force signal |
| spellingShingle | Febryan Setiawan Che-Wei Lin Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features Brain Sciences gait analysis neuro-degenerative diseases time–frequency spectrogram deep learning vertical ground reaction force signal |
| title | Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features |
| title_full | Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features |
| title_fullStr | Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features |
| title_full_unstemmed | Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features |
| title_short | Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features |
| title_sort | identification of neurodegenerative diseases based on vertical ground reaction force classification using time frequency spectrogram and deep learning neural network features |
| topic | gait analysis neuro-degenerative diseases time–frequency spectrogram deep learning vertical ground reaction force signal |
| url | https://www.mdpi.com/2076-3425/11/7/902 |
| work_keys_str_mv | AT febryansetiawan identificationofneurodegenerativediseasesbasedonverticalgroundreactionforceclassificationusingtimefrequencyspectrogramanddeeplearningneuralnetworkfeatures AT cheweilin identificationofneurodegenerativediseasesbasedonverticalgroundreactionforceclassificationusingtimefrequencyspectrogramanddeeplearningneuralnetworkfeatures |