Hierarchical Embedded System Based on FPGA for Classification of Respiratory Diseases
Objectives: This research aims to design and develop a hierarchical embedded system that utilizes respiratory sound features for diagnosing COPD and other respiratory disorders. The system is engineered to achieve high accuracy and efficiency while minimizing energy consumption, making it suitable f...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11014092/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849761335761436672 |
|---|---|
| author | Trong-Thanh Han Kien Le Trung Phuong Nguyen Anh Anh Do Trung |
| author_facet | Trong-Thanh Han Kien Le Trung Phuong Nguyen Anh Anh Do Trung |
| author_sort | Trong-Thanh Han |
| collection | DOAJ |
| description | Objectives: This research aims to design and develop a hierarchical embedded system that utilizes respiratory sound features for diagnosing COPD and other respiratory disorders. The system is engineered to achieve high accuracy and efficiency while minimizing energy consumption, making it suitable for deployment in mobile devices or embedded systems. Methods: Lung sounds are segmented into individual breathing cycles. Thirty-nine main respiratory features in the time and frequency domains are extracted and organized into four layers. The Random Forest algorithm and Artificial Neural Network are fine-tuned with the dataset and applied to disease classification. The first layer contains recorded information, while the second and third layers contain features extracted from fixed-length sound segments classified via Random Forest. The fourth layer utilizes Wavelet Transform to convert breathing patterns into Spectrogram images, which the Artificial Neural Network processes for disease diagnosis. The system is implemented on the Xilinx PYNQ-Ultra96-V2 FPGA development board. Results: The system achieves the highest accuracy of 98.81% for five disease classes: COPD, Healthy, URTI, Bronchitis, and Pneumonia, and saves 52.5% of energy consumption compared to CPU-GPU-based traditional methods. Conclusion: This study demonstrates the effectiveness of the proposed method in diagnosing COPD and other respiratory disorders. The hierarchical embedded system is designed with high accuracy and energy efficiency, with potential real-world applications to support clinical diagnosis. |
| format | Article |
| id | doaj-art-8be72e67e07b4b78940212708668fbff |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8be72e67e07b4b78940212708668fbff2025-08-20T03:06:04ZengIEEEIEEE Access2169-35362025-01-0113930179303210.1109/ACCESS.2025.357316211014092Hierarchical Embedded System Based on FPGA for Classification of Respiratory DiseasesTrong-Thanh Han0https://orcid.org/0000-0002-8512-9529Kien Le Trung1https://orcid.org/0009-0003-6132-5546Phuong Nguyen Anh2Anh Do Trung3https://orcid.org/0000-0002-9467-3198School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, VietnamSchool of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, VietnamSchool of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, VietnamDepartment of Science and Technology Management and International Cooperation, Posts and Telecommunications Institute of Technology, Hanoi, VietnamObjectives: This research aims to design and develop a hierarchical embedded system that utilizes respiratory sound features for diagnosing COPD and other respiratory disorders. The system is engineered to achieve high accuracy and efficiency while minimizing energy consumption, making it suitable for deployment in mobile devices or embedded systems. Methods: Lung sounds are segmented into individual breathing cycles. Thirty-nine main respiratory features in the time and frequency domains are extracted and organized into four layers. The Random Forest algorithm and Artificial Neural Network are fine-tuned with the dataset and applied to disease classification. The first layer contains recorded information, while the second and third layers contain features extracted from fixed-length sound segments classified via Random Forest. The fourth layer utilizes Wavelet Transform to convert breathing patterns into Spectrogram images, which the Artificial Neural Network processes for disease diagnosis. The system is implemented on the Xilinx PYNQ-Ultra96-V2 FPGA development board. Results: The system achieves the highest accuracy of 98.81% for five disease classes: COPD, Healthy, URTI, Bronchitis, and Pneumonia, and saves 52.5% of energy consumption compared to CPU-GPU-based traditional methods. Conclusion: This study demonstrates the effectiveness of the proposed method in diagnosing COPD and other respiratory disorders. The hierarchical embedded system is designed with high accuracy and energy efficiency, with potential real-world applications to support clinical diagnosis.https://ieeexplore.ieee.org/document/11014092/FPGArespiratory disease diagnosisrespiratory soundsignal transformation and parallel computing |
| spellingShingle | Trong-Thanh Han Kien Le Trung Phuong Nguyen Anh Anh Do Trung Hierarchical Embedded System Based on FPGA for Classification of Respiratory Diseases IEEE Access FPGA respiratory disease diagnosis respiratory sound signal transformation and parallel computing |
| title | Hierarchical Embedded System Based on FPGA for Classification of Respiratory Diseases |
| title_full | Hierarchical Embedded System Based on FPGA for Classification of Respiratory Diseases |
| title_fullStr | Hierarchical Embedded System Based on FPGA for Classification of Respiratory Diseases |
| title_full_unstemmed | Hierarchical Embedded System Based on FPGA for Classification of Respiratory Diseases |
| title_short | Hierarchical Embedded System Based on FPGA for Classification of Respiratory Diseases |
| title_sort | hierarchical embedded system based on fpga for classification of respiratory diseases |
| topic | FPGA respiratory disease diagnosis respiratory sound signal transformation and parallel computing |
| url | https://ieeexplore.ieee.org/document/11014092/ |
| work_keys_str_mv | AT trongthanhhan hierarchicalembeddedsystembasedonfpgaforclassificationofrespiratorydiseases AT kienletrung hierarchicalembeddedsystembasedonfpgaforclassificationofrespiratorydiseases AT phuongnguyenanh hierarchicalembeddedsystembasedonfpgaforclassificationofrespiratorydiseases AT anhdotrung hierarchicalembeddedsystembasedonfpgaforclassificationofrespiratorydiseases |