A Low-Power ECG Processor ASIC Based on an Artificial Neural Network for Arrhythmia Detection
The early detection of arrhythmia can effectively reduce the risk of serious heart diseases and save time for treatment. Many healthcare devices have been widely used for electrocardiogram (ECG) monitoring. However, most of them can only complete simple two-classes detection and have unacceptable ha...
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| Format: | Article |
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MDPI AG
2023-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/17/9591 |
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| author | Chen Zhang Junfeng Chang Yujiang Guan Qiuping Li Xin’an Wang Xing Zhang |
| author_facet | Chen Zhang Junfeng Chang Yujiang Guan Qiuping Li Xin’an Wang Xing Zhang |
| author_sort | Chen Zhang |
| collection | DOAJ |
| description | The early detection of arrhythmia can effectively reduce the risk of serious heart diseases and save time for treatment. Many healthcare devices have been widely used for electrocardiogram (ECG) monitoring. However, most of them can only complete simple two-classes detection and have unacceptable hardware overhead and energy consumption. For achieving accurate and low-power arrhythmia detection, a novel ECG processor application specific integrated circuit (ASIC) is proposed in this paper, which can perform the prediction of five types of cardiac arrhythmias and heart rate monitoring. To realize hardware-efficient R-peak detection, an ECG pre-processing engine based on a first derivative and moving average comparison method is proposed. Efficient arrhythmia detection is realized by the proposed low-power classification engine, which is based on a carefully designed lightweight artificial neural network (ANN) with good prediction accuracy. The hardware reuse strategy is used to implement the hardware logic of ANN, where computations are executed by only one processing unit (PU), which is controlled by a flexible finite state machine (FSM). Also, the weights of ANN are configurable to facilitate model updates. We validate the functionality of the design using real-world ECG data. The proposed ECG processor is implemented using 55 nm CMOS technology, occupying an area of 0.33 mm<sup>2</sup>. This design consumes 12.88 μW at a 100 kHz clock frequency, achieving a classification accuracy of 96.69%. The comparison results with previous work indicate that our design has advantages in detection performance and power consumption, providing a good solution for low-power and low-cost ECG monitoring. |
| first_indexed | 2024-03-10T23:28:37Z |
| format | Article |
| id | doaj.art-623ef3e06c54478c953f2676b34ada78 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:28:37Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-623ef3e06c54478c953f2676b34ada782023-11-19T07:49:08ZengMDPI AGApplied Sciences2076-34172023-08-011317959110.3390/app13179591A Low-Power ECG Processor ASIC Based on an Artificial Neural Network for Arrhythmia DetectionChen Zhang0Junfeng Chang1Yujiang Guan2Qiuping Li3Xin’an Wang4Xing Zhang5The Key Laboratory of Integrated Microsystems, Peking University Shenzhen Graduate School, Shenzhen 518055, ChinaShenzhen Semiconductor Industry Association (SZSIA), Shenzhen 518052, ChinaThe Key Laboratory of Integrated Microsystems, Peking University Shenzhen Graduate School, Shenzhen 518055, ChinaThe Key Laboratory of Integrated Microsystems, Peking University Shenzhen Graduate School, Shenzhen 518055, ChinaThe Key Laboratory of Integrated Microsystems, Peking University Shenzhen Graduate School, Shenzhen 518055, ChinaThe Key Laboratory of Integrated Microsystems, Peking University Shenzhen Graduate School, Shenzhen 518055, ChinaThe early detection of arrhythmia can effectively reduce the risk of serious heart diseases and save time for treatment. Many healthcare devices have been widely used for electrocardiogram (ECG) monitoring. However, most of them can only complete simple two-classes detection and have unacceptable hardware overhead and energy consumption. For achieving accurate and low-power arrhythmia detection, a novel ECG processor application specific integrated circuit (ASIC) is proposed in this paper, which can perform the prediction of five types of cardiac arrhythmias and heart rate monitoring. To realize hardware-efficient R-peak detection, an ECG pre-processing engine based on a first derivative and moving average comparison method is proposed. Efficient arrhythmia detection is realized by the proposed low-power classification engine, which is based on a carefully designed lightweight artificial neural network (ANN) with good prediction accuracy. The hardware reuse strategy is used to implement the hardware logic of ANN, where computations are executed by only one processing unit (PU), which is controlled by a flexible finite state machine (FSM). Also, the weights of ANN are configurable to facilitate model updates. We validate the functionality of the design using real-world ECG data. The proposed ECG processor is implemented using 55 nm CMOS technology, occupying an area of 0.33 mm<sup>2</sup>. This design consumes 12.88 μW at a 100 kHz clock frequency, achieving a classification accuracy of 96.69%. The comparison results with previous work indicate that our design has advantages in detection performance and power consumption, providing a good solution for low-power and low-cost ECG monitoring.https://www.mdpi.com/2076-3417/13/17/9591arrhythmia detectionECG processorlow powerclassification engineartificial neural network (ANN) |
| spellingShingle | Chen Zhang Junfeng Chang Yujiang Guan Qiuping Li Xin’an Wang Xing Zhang A Low-Power ECG Processor ASIC Based on an Artificial Neural Network for Arrhythmia Detection Applied Sciences arrhythmia detection ECG processor low power classification engine artificial neural network (ANN) |
| title | A Low-Power ECG Processor ASIC Based on an Artificial Neural Network for Arrhythmia Detection |
| title_full | A Low-Power ECG Processor ASIC Based on an Artificial Neural Network for Arrhythmia Detection |
| title_fullStr | A Low-Power ECG Processor ASIC Based on an Artificial Neural Network for Arrhythmia Detection |
| title_full_unstemmed | A Low-Power ECG Processor ASIC Based on an Artificial Neural Network for Arrhythmia Detection |
| title_short | A Low-Power ECG Processor ASIC Based on an Artificial Neural Network for Arrhythmia Detection |
| title_sort | low power ecg processor asic based on an artificial neural network for arrhythmia detection |
| topic | arrhythmia detection ECG processor low power classification engine artificial neural network (ANN) |
| url | https://www.mdpi.com/2076-3417/13/17/9591 |
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