Pub Date : 2025-04-02DOI: 10.1109/TBCAS.2025.3551714
{"title":"IEEE Transactions on Biomedical Circuits and Systems Publication Information","authors":"","doi":"10.1109/TBCAS.2025.3551714","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3551714","url":null,"abstract":"","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 2","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947504","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761580","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}
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 $mu$s inference time with 4.55 nJ energy consumption.
{"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}
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.
运动伪影（MA）、共模干扰（CMI）和不同的电极-组织阻抗（ETI）是造成实际可穿戴心电图采集中心率检测误差的主要因素。在基于双电极的心电图系统中，这些问题会进一步加剧。本文介绍了一种专门用于双电极心电图测量、完全集成了低功耗运动伪影消除（MAR）和心率检测功能的非卧床心电图采集 ASIC。为了减轻因缺乏受试者偏置电极而产生的严重共模干扰，这项工作采用了改进的共模消除方案，以动态功耗抑制高达 40Vpp 的共模干扰。为了解决身体运动引起的过多 MA，提出了一种混合 MAR 技术，将 ETI 和直流电极偏移 (DEO) 信号作为自适应滤波器的输入。这种方法不仅能以高能效的方式防止通道饱和，还能实时准确地提取并抑制 MA，从而确保稳定的心电图输出和准确、高能效的 R 峰检测，即使在有身体运动的情况下也是如此。核心 IA 采用标准 180nm CMOS 工艺制造，输入参考噪声 (IRN) 为 0.62μVrms（1-150Hz），输入阻抗为 1.9GΩ，50Hz 时的总 CDRR (T-CMRR) 为 92dB。在双电极配置中，ASIC 成功抑制了 MA，并获得了具有清晰 QRS 复极的高质量心电图，使内置的 R 峰检测算法能够更准确、更高效地计算实时心率。
{"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 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 <italic>R</i>-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 <italic>R</i>-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}
Pub Date : 2025-03-28DOI: 10.1109/TBCAS.2025.3568993
{"title":"IEEE Circuits and Systems Society Information","authors":"","doi":"10.1109/TBCAS.2025.3568993","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3568993","url":null,"abstract":"","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 3","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11017327","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170969","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}
Pub Date : 2025-03-28DOI: 10.1109/TBCAS.2025.3568990
{"title":"IEEE Transactions on Biomedical Circuits and Systems Publication Information","authors":"","doi":"10.1109/TBCAS.2025.3568990","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3568990","url":null,"abstract":"","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 3","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11017328","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171052","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}
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 $k$-mer processing engine can construct the 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$boldsymbol{times}$ PrecisionFDA dataset in an average of 28.2 minutes. It achieves a 3-to-59$boldsymbol{times}$ higher throughput than the existing solutions with higher precision (99.79%) and sensitivity (99.03%). The chip also achieves a 935$boldsymbol{times}$ higher energy efficiency than the Illumina DRAGEN FPGA acceleration system.
{"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</i> 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}
Pub Date : 2025-03-26DOI: 10.1109/TBCAS.2025.3573614
Yi-Jie Lin;Lin Chou;Kun-Ju Tsai;Yu-Te Liao
This paper presents a low-noise, low-power galvanic skin response (GSR) sensing circuit capable of simultaneously measuring skin conductance level (SCL) and skin conductance response (SCR) for psychological stress monitoring. The circuit incorporates second-order sub-ten-hertz filters that suppresses out-of-band interference and a programmable gain amplifier (PGA) to accommodate signals of varying magnitudes. Additionally, a dynamic range adjustment mechanism optimizes the primary amplifier’s operating range based on real-time SCL readings. The design achieves a 96.4 dB dynamic range with an input-referred noise of only 3.47 pArms within 0.5–5 Hz under optimal conditions. These advancements significantly enhance measurement accuracy and robustness for wearable stress monitoring and real-time biofeedback applications.
{"title":"A 96 dB Input Dynamic Range Galvanic Skin Response Readout IC With 3.5 pArms Input-Referred Noise for Mental Stress Monitoring","authors":"Yi-Jie Lin;Lin Chou;Kun-Ju Tsai;Yu-Te Liao","doi":"10.1109/TBCAS.2025.3573614","DOIUrl":"10.1109/TBCAS.2025.3573614","url":null,"abstract":"This paper presents a low-noise, low-power galvanic skin response (GSR) sensing circuit capable of simultaneously measuring skin conductance level (SCL) and skin conductance response (SCR) for psychological stress monitoring. The circuit incorporates second-order sub-ten-hertz filters that suppresses out-of-band interference and a programmable gain amplifier (PGA) to accommodate signals of varying magnitudes. Additionally, a dynamic range adjustment mechanism optimizes the primary amplifier’s operating range based on real-time SCL readings. The design achieves a 96.4 dB dynamic range with an input-referred noise of only 3.47 pA<sub>rms</sub> within 0.5–5 Hz under optimal conditions. These advancements significantly enhance measurement accuracy and robustness for wearable stress monitoring and real-time biofeedback applications.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 5","pages":"897-907"},"PeriodicalIF":4.9,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144153009","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}
Pub Date : 2025-03-23DOI: 10.1109/TBCAS.2025.3573027
Thorir Mar Ingolfsson;Victor Kartsch;Luca Benini;Andrea Cossettini
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 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.
{"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</small>, 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}
This article presents a low-power and low phase error bioimpedance (BioZ) measurement IC designed for monitoring cardiopulmonary diseases. To compensate for the phase shift introduced along the signal path by current generator (CG), electrodes and sensor analog front-end (AFE), a novel phase shift calibration logic is proposed. Utilizing this calibration logic, a single-channel in-phase demodulation-based impedance measurement scheme is developed. A noise shaping pseudo-sine wave CG with data-weighted averaging (DWA) is used to minimize modulation harmonics. Fabricated in a 0.18µm CMOS process, the chip occupies 0.73 mm2 and consumes between 52.7 to 97.5 µA current from a 1.8V supply. The CG achieves 74.1 dB SFDR and −70dB THD at 15.5 kHz with a 50µApk stimulation current. The chip achieves 2mΩ/√Hz input-referred impedance noise at 1Hz, 91.1 dB SNR (BW = 4 Hz), 36 kΩ input range and less than 0.48$^{circ}$ phase error (0 − 90$^{circ}$, 1 – 20 kHz). On-body BioZ experiments using a 4-electrode configuration demonstrate clear recordings of Impedance Cardiography (ICG) and respiration signals.
{"title":"A 0.48$^{circ}$ Phase Error 91.1 dB SNR Bioimpedance Measurement IC for Monitoring Cardiopulmonary Diseases","authors":"Jiarun Yuan;Yanxing Suo;Qiao Cai;Hui Wang;Yongfu Li;Yong Lian;Yang Zhao","doi":"10.1109/TBCAS.2025.3572374","DOIUrl":"10.1109/TBCAS.2025.3572374","url":null,"abstract":"This article presents a low-power and low phase error bioimpedance (BioZ) measurement IC designed for monitoring cardiopulmonary diseases. To compensate for the phase shift introduced along the signal path by current generator (CG), electrodes and sensor analog front-end (AFE), a novel phase shift calibration logic is proposed. Utilizing this calibration logic, a single-channel in-phase demodulation-based impedance measurement scheme is developed. A noise shaping pseudo-sine wave CG with data-weighted averaging (DWA) is used to minimize modulation harmonics. Fabricated in a 0.18µm CMOS process, the chip occupies 0.73 mm2 and consumes between 52.7 to 97.5 µA current from a 1.8V supply. The CG achieves 74.1 dB SFDR and −70dB THD at 15.5 kHz with a 50µApk stimulation current. The chip achieves 2mΩ/√Hz input-referred impedance noise at 1Hz, 91.1 dB SNR (BW = 4 Hz), 36 kΩ input range and less than 0.48$^{circ}$ phase error (0 − 90$^{circ}$, 1 – 20 kHz). On-body BioZ experiments using a 4-electrode configuration demonstrate clear recordings of Impedance Cardiography (ICG) and respiration signals.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 5","pages":"908-919"},"PeriodicalIF":4.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121784","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}
This paper presents an implantable low-power neural stimulator that generates electrical stimulation pulses based on subject-specific edge-learning of electrode-tissue voltage profiles. The system deploys a low-magnitude constant-current stimulation pulse to create a training dataset, which is subsequently utilized to predict the desired electrode voltage waveforms for higher magnitudes of constant-current stimulation. The predicted waveform dataset has been used to control a custom switched-capacitor output stage, thereby avoiding Vdriver_transistor· Istimulation power loss as in the conventional neural stimulator drivers. The proposed system incorporates on-chip learning and prediction implemented within an ultra-low-power microcontroller, which has been optimized for memory and power-constrained implantable environments. The stimulator output stage reduces power loss by up to 20% as compared to dynamic power supply scaling method, and consumes up to 3.63× lower as compared to conventional constant-current output stages. The intelligent neural interface system has been powered by a wireless inductive energy transfer link and is remotely controlled through a WiFi-based internet network. A custom-developed application interface, compatible with both mobile devices and personal computers, facilitates secure remote adjustments of stimulation parameters. The proposed system has been validated through a combination of in vivo rat peripheral nerve stimulation, in vitro saline tests, and benchtop experiments. These results collectively demonstrate the potential to advance future neural implant technologies by enabling intelligence, safety, energy efficiency, and remotely controllable neural organ modulation.
{"title":"Energy-Efficient Adaptive Neural Stimulator With Waveform Prediction by Sub-Threshold Interrogation of the Electrode-Tissue Interface","authors":"Sudip Nag;Aryasree Remadevi;Jin Che;Matvii Prytula;Hanzhang Xing;Hanrui Xing;Xiaoxuan Xiao;Andreas Constas-Malvanets;Hengjia Zhang;Yinghe Sun;Joshua Olorocisimo;Jose Zariffa;Roman Genov","doi":"10.1109/TBCAS.2025.3570264","DOIUrl":"10.1109/TBCAS.2025.3570264","url":null,"abstract":"This paper presents an implantable low-power neural stimulator that generates electrical stimulation pulses based on subject-specific edge-learning of electrode-tissue voltage profiles. The system deploys a low-magnitude constant-current stimulation pulse to create a training dataset, which is subsequently utilized to predict the desired electrode voltage waveforms for higher magnitudes of constant-current stimulation. The predicted waveform dataset has been used to control a custom switched-capacitor output stage, thereby avoiding <italic>V</i><sub>driver_transistor</sub> <italic>· I</i><sub>stimulation</sub> power loss as in the conventional neural stimulator drivers. The proposed system incorporates on-chip learning and prediction implemented within an ultra-low-power microcontroller, which has been optimized for memory and power-constrained implantable environments. The stimulator output stage reduces power loss by up to 20% as compared to dynamic power supply scaling method, and consumes up to 3.63<italic>×</i> lower as compared to conventional constant-current output stages. The intelligent neural interface system has been powered by a wireless inductive energy transfer link and is remotely controlled through a WiFi-based internet network. A custom-developed application interface, compatible with both mobile devices and personal computers, facilitates secure remote adjustments of stimulation parameters. The proposed system has been validated through a combination of <italic>in vivo</i> rat peripheral nerve stimulation, <italic>in vitro</i> saline tests, and benchtop experiments. These results collectively demonstrate the potential to advance future neural implant technologies by enabling intelligence, safety, energy efficiency, and remotely controllable neural organ modulation.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 6","pages":"1142-1159"},"PeriodicalIF":4.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121873","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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