{"title":"A 44μW Two-Electrode ECG Acquisition ASIC with Hybrid Motion Artifact Removal and Power-Efficient R-Peak Detection.","authors":"Tianxiang Qu, Xuecheng Yang, Biao Tang, Xiao Li, Min Chen, Zhiliang Hong, Xiaoyang Zeng, Jiawei Xu","doi":"10.1109/TBCAS.2025.3556256","DOIUrl":null,"url":null,"abstract":"<p><p>Motion artifacts (MA), common-mode interference (CMI), and varying electrode-tissue impedance (ETI) are the main factors that cause heart rate detection errors in practical wearable ECG acquisition. These problems are further exacerbated in two-electrode based ECG systems. This article presents an ambulatory ECG acquisition ASIC with fully integrated, low power motion artifacts removal (MAR) and heart rate detection, specifically for two-electrode ECG measurement. To alleviate the significant CMI due to the absence of subject bias electrode, this work utilizes an improved common-mode cancellation scheme to suppress CMI up to 40V<sub>pp</sub> with dynamic power consumption. To address excessive MA caused by the body movement, a hybrid MAR technique is proposed, where both ETI and DC electrode offset (DEO) signals are incorporated as inputs to the adaptive filter. This approach not only prevents channel saturation in a power-efficient manner, but also accurately extracts MA and suppresses it in real time, thereby ensuring stable ECG outputs and accurate, power-efficient R-peak detection even in the presence of body movements. Fabricated in a standard 180nm CMOS process, the core IA achieves an input referred noise (IRN) of 0.62μV<sub>rms</sub> (1-150Hz), an input impedance of 1.9GΩ and a total-CMRR (T-CMRR) of 92dB at 50Hz. In a two-electrode configuration, the ASIC successfully suppresses the MA and obtains a high-quality ECG with well-identified QRS complex, enabling the built-in R-peak detection algorithm to calculate real-time heart rate more accurately and efficiently.</p>","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on biomedical circuits and systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TBCAS.2025.3556256","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Motion artifacts (MA), common-mode interference (CMI), and varying electrode-tissue impedance (ETI) are the main factors that cause heart rate detection errors in practical wearable ECG acquisition. These problems are further exacerbated in two-electrode based ECG systems. This article presents an ambulatory ECG acquisition ASIC with fully integrated, low power motion artifacts removal (MAR) and heart rate detection, specifically for two-electrode ECG measurement. To alleviate the significant CMI due to the absence of subject bias electrode, this work utilizes an improved common-mode cancellation scheme to suppress CMI up to 40Vpp with dynamic power consumption. To address excessive MA caused by the body movement, a hybrid MAR technique is proposed, where both ETI and DC electrode offset (DEO) signals are incorporated as inputs to the adaptive filter. This approach not only prevents channel saturation in a power-efficient manner, but also accurately extracts MA and suppresses it in real time, thereby ensuring stable ECG outputs and accurate, power-efficient R-peak detection even in the presence of body movements. Fabricated in a standard 180nm CMOS process, the core IA achieves an input referred noise (IRN) of 0.62μVrms (1-150Hz), an input impedance of 1.9GΩ and a total-CMRR (T-CMRR) of 92dB at 50Hz. In a two-electrode configuration, the ASIC successfully suppresses the MA and obtains a high-quality ECG with well-identified QRS complex, enabling the built-in R-peak detection algorithm to calculate real-time heart rate more accurately and efficiently.