IEEE transactions on biomedical circuits and systems最新文献_第8页

IEEE Circuits and Systems Society Information IEEE电路与系统学会信息

IEEE transactions on biomedical circuits and systems Pub Date : 2025-04-02 DOI: 10.1109/TBCAS.2025.3551796

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A 9.68nW 57.51dB SNDR SAR ADC With Dual Bypass Windows Based on Non-Binary Split Capacitors for Biomedical Applications 基于非二进制分割电容的双旁路窗口的9.68 nW 57.51dB SNDR SAR ADC。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-04-02 DOI: 10.1109/TBCAS.2025.3557241

Kangkang Sun;Jingjing Liu;Feng Yan;Haoning Sun;Yafei Zhang;Yuan Ren;Linfei Huang;Yao Pi;Wanqing Wu;Jian Guan

{"title":"A 9.68nW 57.51dB SNDR SAR ADC With Dual Bypass Windows Based on Non-Binary Split Capacitors for Biomedical Applications","authors":"Kangkang Sun;Jingjing Liu;Feng Yan;Haoning Sun;Yafei Zhang;Yuan Ren;Linfei Huang;Yao Pi;Wanqing Wu;Jian Guan","doi":"10.1109/TBCAS.2025.3557241","DOIUrl":"10.1109/TBCAS.2025.3557241","url":null,"abstract":"The paper proposes a low-power Successive Approximation Register (SAR) Analog-to-Digital Conversion (ADC) with dual bypass windows based on non-binary split capacitors. To reduce the power consumption, the bypass windows constituted by the split capacitors can maximize the coverage of biological signals both in the resting state and excited state. When the signal falls within the designated window, unnecessary conversion cycles are skipped. This process is mainly judged and controlled by digital circuits, which is highly robust and does not require calibration. Meanwhile, a low-power dynamic CMOS comparator is proposed, which can effectively reduce the voltage variation of the latch node during the comparator's operation, further reducing power consumption. The proposed SAR ADC, based on a 180nm process, measures a power consumption of 9.68nW at a supply voltage of 0.6V and a sampling rate of 5.21kS/s. The signal-to-noise-and-distortion ratio (SNDR) and the spur-free dynamic range (SFDR) are measured at 57.51dB and 71.68dB, respectively. It also achieves an effective number of bits (ENOB) of 9.26 bits and a Walden figure-of-merit (FoM) of 2.9 fJ/conv.-step. The proposed SAR ADC is also verified by collected electromyogram (EMG), electrocardiogram (ECG), and electroencephalogram (EEG) signals. The average power consumption for quantifying EMG signals is 7.95 nW, providing an attractive solution for low-power SAR ADCs in biomedical applications.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 6","pages":"1091-1104"},"PeriodicalIF":4.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Transactions on Biomedical Circuits and Systems Publication Information IEEE生物医学电路和系统汇刊信息

IEEE transactions on biomedical circuits and systems Pub Date : 2025-04-02 DOI: 10.1109/TBCAS.2025.3551714

引用次数: 0

Wearable Epilepsy Seizure Detection on FPGA With Spiking Neural Networks 基于脉冲神经网络的FPGA可穿戴癫痫发作检测。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-03-30 DOI: 10.1109/TBCAS.2025.3575327

Paola Busia;Gianluca Leone;Andrea Matticola;Luigi Raffo;Paolo Meloni

{"title":"Wearable Epilepsy Seizure Detection on FPGA With Spiking Neural Networks","authors":"Paola Busia;Gianluca Leone;Andrea Matticola;Luigi Raffo;Paolo Meloni","doi":"10.1109/TBCAS.2025.3575327","DOIUrl":"10.1109/TBCAS.2025.3575327","url":null,"abstract":"The development of epilepsy monitoring solutions suitable for everyday use is a very challenging task, where different constraints should be combined, resulting from the required accuracy standards, the unobtrusiveness of the monitoring device, and the efficiency of real-time operation. Considering the time-varying nature of the electroencephalography signal (EEG), Spiking Neural Networks (SNNs) represent a promising solution to model the evolution of the brain state based on the history of the previously processed signal. This work proposes an extremely lightweight SNN-based seizure detection solution, utilizing a simple encoding scheme to ensure high levels of sparsity. Despite the reduced complexity, the model provides a detection performance comparable with the state-of-the-art SNN-based approaches on the evaluated data from the CHB-MIT dataset, reaching a 96% area under the curve (AUC) and allowing 99.3% average accuracy, with the detection of 100% of the examined seizure events and a false alarm rate of 0.3 false positives per hour. The suitability for real-time inference execution on wearable monitoring devices was assessed on SYNtzulu, demonstrating 0.5 <inline-formula><tex-math>$mu$</tex-math></inline-formula>s inference time with 4.55 nJ energy consumption.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 6","pages":"1175-1186"},"PeriodicalIF":4.9,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11018438","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144188720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A 44µW Two-Electrode ECG Acquisition ASIC With Hybrid Motion Artifact Removal and Power-Efficient R-Peak Detection 一种44μW混合运动伪影去除和高效r峰检测的双电极心电采集ASIC。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-03-28 DOI: 10.1109/TBCAS.2025.3556256

Tianxiang Qu;Xuecheng Yang;Biao Tang;Xiao Li;Min Chen;Zhiliang Hong;Xiaoyang Zeng;Jiawei Xu

{"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":"10.1109/TBCAS.2025.3556256","url":null,"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 <italic>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 <italic>R-peak detection algorithm to calculate real-time heart rate more accurately and efficiently.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 6","pages":"1078-1090"},"PeriodicalIF":4.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IEEE Circuits and Systems Society Information IEEE电路与系统学会信息

IEEE transactions on biomedical circuits and systems Pub Date : 2025-03-28 DOI: 10.1109/TBCAS.2025.3568993

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IEEE Transactions on Biomedical Circuits and Systems Publication Information IEEE生物医学电路和系统汇刊信息

IEEE transactions on biomedical circuits and systems Pub Date : 2025-03-28 DOI: 10.1109/TBCAS.2025.3568990

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A 28nm Fully Integrated End-to-End Genome Analysis Accelerator for Next-Generation Sequencing 一个28纳米完全集成的端到端基因组分析加速器，用于下一代测序。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-03-27 DOI: 10.1109/TBCAS.2025.3555579

Yi-Chung Wu;Yen-Lung Chen;Chung-Hsuan Yang;Chao-Hsi Lee;Wen-Ching Chen;Liang-Yi Lin;Nian-Shyang Chang;Chun-Pin Lin;Chi-Shi Chen;Jui-Hung Hung;Chia-Hsiang Yang

{"title":"A 28nm Fully Integrated End-to-End Genome Analysis Accelerator for Next-Generation Sequencing","authors":"Yi-Chung Wu;Yen-Lung Chen;Chung-Hsuan Yang;Chao-Hsi Lee;Wen-Ching Chen;Liang-Yi Lin;Nian-Shyang Chang;Chun-Pin Lin;Chi-Shi Chen;Jui-Hung Hung;Chia-Hsiang Yang","doi":"10.1109/TBCAS.2025.3555579","DOIUrl":"10.1109/TBCAS.2025.3555579","url":null,"abstract":"This paper presents the first end-to-end next-generation sequencing (NGS) data analysis accelerator for short-read mapping, haplotype calling, variant calling, and genotyping. It supports both single-end and paired-end short-reads (or reads) and uses the FM-index, a compact index data structure, for exact-match in short-read mapping. For inexact match part of short-read mapping, a dynamic programming array is proposed to determine the mapping results. To reduce the workload of short-read mapping, a rapid similarity calculation is designed. A rescue technique is also adopted to increase the overall sensitivity. In haplotype calling, a parallel <inline-formula><tex-math>$k$</tex-math></inline-formula>-mer processing engine can construct the <italic>de Bruijn graph and assemble the haplotypes. The variant calling step determines variants between a subject and a reference genome sequence with a variant discovery engine. Lastly, genotype likelihood is computed in parallel by a genotype likelihood computing engine, which outputs genotypes of all discovered variants and corresponding Phred-scaled likelihood (PL) values. This work completes end-to-end data analysis for the 50<inline-formula><tex-math>$boldsymbol{times}$</tex-math></inline-formula> PrecisionFDA dataset in an average of 28.2 minutes. It achieves a 3-to-59<inline-formula><tex-math>$boldsymbol{times}$</tex-math></inline-formula> higher throughput than the existing solutions with higher precision (99.79%) and sensitivity (99.03%). The chip also achieves a 935<inline-formula><tex-math>$boldsymbol{times}$</tex-math></inline-formula> higher energy efficiency than the Illumina DRAGEN FPGA acceleration system.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 6","pages":"1105-1119"},"PeriodicalIF":4.9,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A 96 dB Input Dynamic Range Galvanic Skin Response Readout IC With 3.5 pArms Input-Referred Noise for Mental Stress Monitoring 一种96 dB输入动态范围皮肤电响应读出IC， 3.5 pArms输入参考噪声，用于精神压力监测。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-03-26 DOI: 10.1109/TBCAS.2025.3573614

Yi-Jie Lin;Lin Chou;Kun-Ju Tsai;Yu-Te Liao

引用次数: 0

A Wearable Ultra-Low-Power System for EEG-Based Speech-Imagery Interfaces 基于脑电图语音图像接口的可穿戴超低功耗系统。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-03-23 DOI: 10.1109/TBCAS.2025.3573027

Thorir Mar Ingolfsson;Victor Kartsch;Luca Benini;Andrea Cossettini

{"title":"A Wearable Ultra-Low-Power System for EEG-Based Speech-Imagery Interfaces","authors":"Thorir Mar Ingolfsson;Victor Kartsch;Luca Benini;Andrea Cossettini","doi":"10.1109/TBCAS.2025.3573027","DOIUrl":"10.1109/TBCAS.2025.3573027","url":null,"abstract":"Speech imagery—the process of mentally simulating speech without vocalization—is a promising approach for brain-computer interfaces (BCIs), enabling assistive communication for individuals with speech impairments or to enhance privacy. However, existing EEG-based speech imagery systems remain impractical for use outside specialized laboratories due to their reliance on high-channel-count and resource-intensive machine learning models running on external computing platforms. In this work, we present the first end-to-end demonstration of EEG-based speech imagery decoding on a low-channel, ultra-low-power wearable device. Building on our previous work on vowel imagery, we introduce an extended framework leveraging the BioGAP platform and <sc>VowelNet, a lightweight neural network optimized for embedded speech imagery classification. In particular, we demonstrate state-of-the-art accuracy in the classification of an expanded vocabulary comprising vowels, commands, and rest states (13 classes) with a subject-specific training approach, achieving up to 50.0% for one subject (42.8% average) in multi-class classification. We deploy our model on an embedded biosignal acquisition and processing platform (BioGAP), based on the GAP9 processor, for real-time inference with minimal power consumption (25.93 mW). Our system achieves continuous execution for more than 21 hours on a small LiPo battery while maintaining classification latencies of 40.9 ms. Finally, we also explore the benefits of applying Continual Learning techniques to progressively improve the system’s performance throughout its operational lifetime, and we demonstrate that electrodes located on the temporal area contribute the most to the overall accuracy. This work marks a significant step toward practical, real-time, and unobtrusive speech imagery BCIs, unlocking new opportunities for covert communication and assistive technologies.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 4","pages":"743-755"},"PeriodicalIF":4.9,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144133226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0