A Fast and Robust Non-Sparse Signal Recovery Algorithm for Wearable ECG Telemonitoring Using ADMM-Based Block Sparse Bayesian Learning
Wearable telemonitoring of electrocardiogram (ECG) based on wireless body Area networks (WBAN) is a promising approach in next-generation patient-centric telecardiology solutions. In order to guarantee long-term effective operation of monitoring systems, the power consumption of the sensors must be...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-06-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/7/2021 |
| _version_ | 1817990683799060480 |
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| author | Yunfei Cheng Yalan Ye Mengshu Hou Wenwen He Yunxia Li Xuesong Deng |
| author_facet | Yunfei Cheng Yalan Ye Mengshu Hou Wenwen He Yunxia Li Xuesong Deng |
| author_sort | Yunfei Cheng |
| collection | DOAJ |
| description | Wearable telemonitoring of electrocardiogram (ECG) based on wireless body Area networks (WBAN) is a promising approach in next-generation patient-centric telecardiology solutions. In order to guarantee long-term effective operation of monitoring systems, the power consumption of the sensors must be strictly limited. Compressed sensing (CS) is an effective method to alleviate this problem. However, ECG signals in WBAN are usually non-sparse, and most traditional compressed sensing recovery algorithms have difficulty recovering non-sparse signals. In this paper, we proposed a fast and robust non-sparse signal recovery algorithm for wearable ECG telemonitoring. In the proposed algorithm, the alternating direction method of multipliers (ADMM) is used to accelerate the speed of block sparse Bayesian learning (BSBL) framework. We used the famous MIT-BIH Arrhythmia Database, MIT-BIH Long-Term ECG Database and ECG datasets collected in our practical wearable ECG telemonitoring system to verify the performance of the proposed algorithm. The experimental results show that the proposed algorithm can directly recover ECG signals with a satisfactory accuracy in a time domain without a dictionary matrix. Due to acceleration by ADMM, the proposed algorithm has a fast speed, and also it is robust for different ECG datasets. These results suggest that the proposed algorithm is very promising for wearable ECG telemonitoring. |
| first_indexed | 2024-04-14T01:02:21Z |
| format | Article |
| id | doaj.art-60221d78804445ef8dbe1a4b83fbb24b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T01:02:21Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-60221d78804445ef8dbe1a4b83fbb24b2022-12-22T02:21:21ZengMDPI AGSensors1424-82202018-06-01187202110.3390/s18072021s18072021A Fast and Robust Non-Sparse Signal Recovery Algorithm for Wearable ECG Telemonitoring Using ADMM-Based Block Sparse Bayesian LearningYunfei Cheng0Yalan Ye1Mengshu Hou2Wenwen He3Yunxia Li4Xuesong Deng5School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaWearable telemonitoring of electrocardiogram (ECG) based on wireless body Area networks (WBAN) is a promising approach in next-generation patient-centric telecardiology solutions. In order to guarantee long-term effective operation of monitoring systems, the power consumption of the sensors must be strictly limited. Compressed sensing (CS) is an effective method to alleviate this problem. However, ECG signals in WBAN are usually non-sparse, and most traditional compressed sensing recovery algorithms have difficulty recovering non-sparse signals. In this paper, we proposed a fast and robust non-sparse signal recovery algorithm for wearable ECG telemonitoring. In the proposed algorithm, the alternating direction method of multipliers (ADMM) is used to accelerate the speed of block sparse Bayesian learning (BSBL) framework. We used the famous MIT-BIH Arrhythmia Database, MIT-BIH Long-Term ECG Database and ECG datasets collected in our practical wearable ECG telemonitoring system to verify the performance of the proposed algorithm. The experimental results show that the proposed algorithm can directly recover ECG signals with a satisfactory accuracy in a time domain without a dictionary matrix. Due to acceleration by ADMM, the proposed algorithm has a fast speed, and also it is robust for different ECG datasets. These results suggest that the proposed algorithm is very promising for wearable ECG telemonitoring.http://www.mdpi.com/1424-8220/18/7/2021wireless body area networks (WBAN)electrocardiogram (ECG)compressed sensing (CS)block sparse Bayesian learning (BSBL)alternating direction method of multipliers (ADMM) |
| spellingShingle | Yunfei Cheng Yalan Ye Mengshu Hou Wenwen He Yunxia Li Xuesong Deng A Fast and Robust Non-Sparse Signal Recovery Algorithm for Wearable ECG Telemonitoring Using ADMM-Based Block Sparse Bayesian Learning Sensors wireless body area networks (WBAN) electrocardiogram (ECG) compressed sensing (CS) block sparse Bayesian learning (BSBL) alternating direction method of multipliers (ADMM) |
| title | A Fast and Robust Non-Sparse Signal Recovery Algorithm for Wearable ECG Telemonitoring Using ADMM-Based Block Sparse Bayesian Learning |
| title_full | A Fast and Robust Non-Sparse Signal Recovery Algorithm for Wearable ECG Telemonitoring Using ADMM-Based Block Sparse Bayesian Learning |
| title_fullStr | A Fast and Robust Non-Sparse Signal Recovery Algorithm for Wearable ECG Telemonitoring Using ADMM-Based Block Sparse Bayesian Learning |
| title_full_unstemmed | A Fast and Robust Non-Sparse Signal Recovery Algorithm for Wearable ECG Telemonitoring Using ADMM-Based Block Sparse Bayesian Learning |
| title_short | A Fast and Robust Non-Sparse Signal Recovery Algorithm for Wearable ECG Telemonitoring Using ADMM-Based Block Sparse Bayesian Learning |
| title_sort | fast and robust non sparse signal recovery algorithm for wearable ecg telemonitoring using admm based block sparse bayesian learning |
| topic | wireless body area networks (WBAN) electrocardiogram (ECG) compressed sensing (CS) block sparse Bayesian learning (BSBL) alternating direction method of multipliers (ADMM) |
| url | http://www.mdpi.com/1424-8220/18/7/2021 |
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