Shuyu Liu
Research Subjects
Research Interests
Shuyu Liu has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Hongming Chen
School of Information Engineering, Zhejiang Ocean University, ZhouShan, China
1 shared publication - Jiawen Deng
The Key Laboratory of Integrated Microsystems, Peking University Shenzhen Graduate School, Shenzhen 518000, China
1 shared publication - Jie Yang
School of Software and Microelectronics, Peking University, Beijing, China
1 shared publication - Jieru Ma
School of Integrated Circuits, Peking University, Beijing 100871, China
1 shared publication - Xing Zhang
School of Integrated Circuits Peking University Beijing China
1 shared publication - Xin’an Wang
School of Software & Microelectronics, Peking University, Beijing, China
1 shared publication
Latest Publications
- Open Access
- 29 December 2023
- Published by MDPI AG in Applied Sciences
- Vol. 14 (1) , 342
An Energy-Efficient ECG Processor Based on HDWT and a Hybrid Classifier for Arrhythmia Detection
Cardiac arrhythmia (CA) is a severe cardiac disorder that results in a significant number of fatalities worldwide each year. Conventional electrocardiography (ECG) devices are often unable to detect arrhythmia symptoms during patients’ hospital visits due to their intermittent nature. This paper presents a wearable ECG processor for cardiac arrhythmia (CA) detection. The processor utilizes a Hilbert transform-based R-peak detection engine for R-peak detection, a Haar discrete wavelet transform (HDWT) unit for feature extraction, and a Hybrid ECG classifier that combines linear methods and Non-Linear Support Vector Machines (NLSVM) classifiers to distinguish between normal and abnormal heartbeats. The processor is fabricated by the CMOS 110 nm process with an area of 1.34 mm2 and validated with the MIT_BIH Database. The whole design consumes 4.08 μW with an average classification accuracy of 97.34%.
- 29 December 2023
- Published by MDPI AG in Applied Sciences
- Vol. 14 (1) , 342
Open AccessAn Energy-Efficient ECG Processor Based on HDWT and a Hybrid Classifier for Arrhythmia Detection
Cardiac arrhythmia (CA) is a severe cardiac disorder that results in a significant number of fatalities worldwide each year. Conventional electrocardiography (ECG) devices are often unable to detect arrhythmia symptoms during patients’ hospital visits due to their intermittent nature. This paper presents a wearable ECG processor for cardiac arrhythmia (CA) detection. The processor utilizes a Hilbert transform-based R-peak detection engine for R-peak detection, a Haar discrete wavelet transform (HDWT) unit for feature extraction, and a Hybrid ECG classifier that combines linear methods and Non-Linear Support Vector Machines (NLSVM) classifiers to distinguish between normal and abnormal heartbeats. The processor is fabricated by the CMOS 110 nm process with an area of 1.34 mm2 and validated with the MIT_BIH Database. The whole design consumes 4.08 μW with an average classification accuracy of 97.34%.