Pub Date : 2024-09-13DOI: 10.1109/tbcas.2024.3460388
Xinguo Wang, Songyu Han, Peng Yan, Yang Lin, Chen Wang, Lei Qian, Pujia Xing, Yue Cao, Xinglei Song, Guoxing Wang, Timothy G. Constandinou, Yan Liu
{"title":"A 1024-Channel Simultaneous Electrophysiological and Electrochemical Neural Recording System with In-Pixel Digitization and Crosstalk Compensation","authors":"Xinguo Wang, Songyu Han, Peng Yan, Yang Lin, Chen Wang, Lei Qian, Pujia Xing, Yue Cao, Xinglei Song, Guoxing Wang, Timothy G. Constandinou, Yan Liu","doi":"10.1109/tbcas.2024.3460388","DOIUrl":"https://doi.org/10.1109/tbcas.2024.3460388","url":null,"abstract":"","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"100 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1109/tbcas.2024.3457522
Lan Mei, Thorir Mar Ingolfsson, Cristian Cioflan, Victor Kartsch, Andrea Cossettini, Xiaying Wang, Luca Benini
{"title":"An Ultra-Low Power Wearable BMI System with Continual Learning Capabilities","authors":"Lan Mei, Thorir Mar Ingolfsson, Cristian Cioflan, Victor Kartsch, Andrea Cossettini, Xiaying Wang, Luca Benini","doi":"10.1109/tbcas.2024.3457522","DOIUrl":"https://doi.org/10.1109/tbcas.2024.3457522","url":null,"abstract":"","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"1 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-09DOI: 10.1109/tbcas.2024.3456552
Matteo Antonio Scrugli, Gianluca Leone, Paola Busia, Luigi Raffo, Paolo Meloni
{"title":"Real-Time sEMG Processing with Spiking Neural Networks on a Low-Power 5K-LUT FPGA","authors":"Matteo Antonio Scrugli, Gianluca Leone, Paola Busia, Luigi Raffo, Paolo Meloni","doi":"10.1109/tbcas.2024.3456552","DOIUrl":"https://doi.org/10.1109/tbcas.2024.3456552","url":null,"abstract":"","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"26 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-16DOI: 10.1109/tbcas.2024.3401858
Paola Busia, Matteo Antonio Scrugli, Victor Jean-Baptiste Jung, Luca Benini, Paolo Meloni
{"title":"A Tiny Transformer for Low-Power Arrhythmia Classification on Microcontrollers","authors":"Paola Busia, Matteo Antonio Scrugli, Victor Jean-Baptiste Jung, Luca Benini, Paolo Meloni","doi":"10.1109/tbcas.2024.3401858","DOIUrl":"https://doi.org/10.1109/tbcas.2024.3401858","url":null,"abstract":"","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"138 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141060952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-03DOI: 10.1109/tbcas.2024.3396191
Saeideh Pahlavan, Shahin Jafarabadi-Ashtiani, S. Abdollah Mirbozorgi
{"title":"Maze-Based Scalable Wireless Power Transmission Experimental Arena for Freely Moving Small Animals Applications","authors":"Saeideh Pahlavan, Shahin Jafarabadi-Ashtiani, S. Abdollah Mirbozorgi","doi":"10.1109/tbcas.2024.3396191","DOIUrl":"https://doi.org/10.1109/tbcas.2024.3396191","url":null,"abstract":"","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"13 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140827636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-03DOI: 10.1109/tbcas.2024.3396115
Daniel Valencia, Patrick P. Mercier, Amir Alimohammad
{"title":"An Efficient Brain-Switch for Asynchronous Brain-Computer Interfaces","authors":"Daniel Valencia, Patrick P. Mercier, Amir Alimohammad","doi":"10.1109/tbcas.2024.3396115","DOIUrl":"https://doi.org/10.1109/tbcas.2024.3396115","url":null,"abstract":"","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"45 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140827833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-22DOI: 10.1109/TBCAS.2023.3307188
Zhixing Gao, Yuqi Wang, Xingchen Xu, Chaohong Zhang, Zhiwei Dai, Haiying Zhang, Jun Zhang, Hao Yang
Abnormalities in cardiac function arise irregularly and typically involve multimodal electrical, mechanical vibrations, and acoustics alterations. This paper proposes an Electro-Mechano-Acoustic (EMA) activity model for mapping the complete macroscopic cardiac function to refine the systematic interpretation of cardiac multimodal assessment. We abstract this activity pattern and build the mapping system by analyzing the functional comparison of the heart pump and Electronic Fuel Injection (EFI) system from the multimodal characteristics of the heart. Electrocardiogram (ECG), seismocardiogram (SCG) & Ultra-Low Frequency seismocardiogram (ULF-SCG), and Phonocardiogram (PCG) are selected to implement the EMA mapping respectively. First, a novel low-frequency cardiograph compound sensor capable of extracting both SCG and ULF-SCG is proposed, which is integrated with ECG and PCG modules on a single hardware device for portable dynamic acquisition. Afterward, a multimodal signal processing chain further analyses the acquired synchronized signals, and the extracted ULF-SCG is shown to indicate changes in heart volume. In particular, the proposed method based on waveform curvature is used to extract 9 feature points of the SCG signal, and the overall recognition accuracy reaches over 90% in the data collected by EMA portable device. Ultimately, we integrate the portable device and signal processing chains to form the EMA cardiovascular mapping system (EMACMS). As a next-generation system solution for cardiac daily dynamic monitoring, which can map the mechanical coupling and electromechanical coupling process, extract multi-characteristic heart rate variability (HRV), and enable extraction of important time intervals of cardiac activity to assess cardiac function.
心脏功能异常是不规则出现的，通常涉及多模态电、机械振动和声学改变。本文提出了一种电子-机械-声学（EMA）活动模型，用于映射完整的宏观心脏功能，以完善心脏多模态评估的系统解释。我们从心脏的多模态特征中分析了心脏泵和电子燃油喷射（EFI）系统的功能比较，从而抽象出这种活动模式并建立了映射系统。分别选择心电图（ECG）、地震心电图（SCG）和超低频地震心电图（ULF-SCG）以及声心电图（PCG）来实现 EMA 映射。首先，提出了一种新型低频心电图复合传感器，能够同时提取 SCG 和 ULF-SCG，并将其与 ECG 和 PCG 模块集成在单个硬件设备上，用于便携式动态采集。之后，多模态信号处理链会进一步分析采集到的同步信号，提取的超低频-SCG 可显示心脏容积的变化。其中，基于波形曲率的拟议方法用于提取 SCG 信号的 9 个特征点，在 EMA 便携式设备采集的数据中，整体识别准确率达到 90% 以上。最终，我们将便携式设备和信号处理链整合为 EMA 心血管图谱系统（EMACMS）。作为下一代心脏日常动态监测系统解决方案，该系统可绘制机械耦合和机电耦合过程图，提取多特征心率变异性（HRV），并能提取心脏活动的重要时间间隔以评估心脏功能。
{"title":"A Portable Cardiac Dynamic Monitoring System in the Framework of Electro-Mechano-Acoustic Mapping.","authors":"Zhixing Gao, Yuqi Wang, Xingchen Xu, Chaohong Zhang, Zhiwei Dai, Haiying Zhang, Jun Zhang, Hao Yang","doi":"10.1109/TBCAS.2023.3307188","DOIUrl":"10.1109/TBCAS.2023.3307188","url":null,"abstract":"<p><p>Abnormalities in cardiac function arise irregularly and typically involve multimodal electrical, mechanical vibrations, and acoustics alterations. This paper proposes an Electro-Mechano-Acoustic (EMA) activity model for mapping the complete macroscopic cardiac function to refine the systematic interpretation of cardiac multimodal assessment. We abstract this activity pattern and build the mapping system by analyzing the functional comparison of the heart pump and Electronic Fuel Injection (EFI) system from the multimodal characteristics of the heart. Electrocardiogram (ECG), seismocardiogram (SCG) & Ultra-Low Frequency seismocardiogram (ULF-SCG), and Phonocardiogram (PCG) are selected to implement the EMA mapping respectively. First, a novel low-frequency cardiograph compound sensor capable of extracting both SCG and ULF-SCG is proposed, which is integrated with ECG and PCG modules on a single hardware device for portable dynamic acquisition. Afterward, a multimodal signal processing chain further analyses the acquired synchronized signals, and the extracted ULF-SCG is shown to indicate changes in heart volume. In particular, the proposed method based on waveform curvature is used to extract 9 feature points of the SCG signal, and the overall recognition accuracy reaches over 90% in the data collected by EMA portable device. Ultimately, we integrate the portable device and signal processing chains to form the EMA cardiovascular mapping system (EMACMS). As a next-generation system solution for cardiac daily dynamic monitoring, which can map the mechanical coupling and electromechanical coupling process, extract multi-characteristic heart rate variability (HRV), and enable extraction of important time intervals of cardiac activity to assess cardiac function.</p>","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"PP ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10081420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-22DOI: 10.1109/TBCAS.2019.2930215
Ni Wang, Jun Zhou, Guanghai Dai, Jiahui Huang, Yuxiang Xie
Wearable intelligent ECG monitoring devices can perform automatic ECG diagnosis in real time and send out alert signal together with abnormal ECG signal for doctor's further analysis. This provides a means for the patient to identify their heart problem as early as possible and go to doctors for medical treatment. For such system the key requirements include high accuracy and low power consumption. However, the existing wearable intelligent ECG monitoring schemes suffer from high power consumption in both ECG diagnosis and transmission in order to achieve high accuracy. In this work, we have proposed an energy-efficient wearable intelligent ECG monitor scheme with two-stage end-to-end neural network and diagnosis-based adaptive compression. Compared to the state-of-the-art schemes, it significantly reduces the power consumption in ECG diagnosis and transmission while maintaining high accuracy.
{"title":"Energy-Efficient Intelligent ECG Monitoring for Wearable Devices","authors":"Ni Wang, Jun Zhou, Guanghai Dai, Jiahui Huang, Yuxiang Xie","doi":"10.1109/TBCAS.2019.2930215","DOIUrl":"https://doi.org/10.1109/TBCAS.2019.2930215","url":null,"abstract":"Wearable intelligent ECG monitoring devices can perform automatic ECG diagnosis in real time and send out alert signal together with abnormal ECG signal for doctor's further analysis. This provides a means for the patient to identify their heart problem as early as possible and go to doctors for medical treatment. For such system the key requirements include high accuracy and low power consumption. However, the existing wearable intelligent ECG monitoring schemes suffer from high power consumption in both ECG diagnosis and transmission in order to achieve high accuracy. In this work, we have proposed an energy-efficient wearable intelligent ECG monitor scheme with two-stage end-to-end neural network and diagnosis-based adaptive compression. Compared to the state-of-the-art schemes, it significantly reduces the power consumption in ECG diagnosis and transmission while maintaining high accuracy.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"13 1","pages":"1112-1121"},"PeriodicalIF":5.1,"publicationDate":"2019-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2019.2930215","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62967101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/TBCAS.2016.2622738
Xilin Liu, Milin Zhang, A. Richardson, T. Lucas, J. van der Spiegel
This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18$mu$ m CMOS technology, occupying a silicon area of 3.7 mm$^2$. The chip dissipates 56 $mu$W/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.
本文提出了一种双向脑机接口(BMI)微系统，用于闭环神经科学研究，特别是在自由行为动物身上的实验。片上系统(SoC)由16通道神经记录前端、神经特征提取单元、16通道可编程神经刺激器后端、通道内可编程闭环控制器、全局模数转换器(ADC)和外围电路组成。所提出的神经特征提取单元包括1)实现64步自然对数域频率调谐的超低功耗神经能量提取单元，以及2)具有时幅窗鉴别器的电流模式动作电位(AP)检测单元。一个可编程的比例-积分-导数(PID)控制器已集成在每个通道，使各种闭环操作。所实现的ADC包括一个用于神经特征输出和/或局部场电位(LFP)输出数字化的10位电压模式连续逼近寄存器(SAR) ADC，以及一个用于动作电位输出数字化的8位电流模式SAR ADC。该多模式刺激器可编程为在任意通道配置中执行单极或双极、对称或不对称电荷平衡刺激，最大电流为4 mA。该芯片采用0.18$mu$ m CMOS技术制造，占据了3.7 mm$^2$的硅面积。芯片平均耗散56 $mu$W/ch。系统集成了带蓝牙模块的通用低功耗微控制器，提供无线链路和SoC配置。本文提出的方法、电路技术和系统拓扑可以广泛应用于相关的神经生理学研究，特别是闭环BMI实验。
{"title":"Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control","authors":"Xilin Liu, Milin Zhang, A. Richardson, T. Lucas, J. van der Spiegel","doi":"10.1109/TBCAS.2016.2622738","DOIUrl":"https://doi.org/10.1109/TBCAS.2016.2622738","url":null,"abstract":"This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18<inline-formula><tex-math notation=\"LaTeX\">$mu$</tex-math></inline-formula> m CMOS technology, occupying a silicon area of 3.7 mm<inline-formula><tex-math notation=\"LaTeX\">$^2$</tex-math></inline-formula>. The chip dissipates 56 <inline-formula><tex-math notation=\"LaTeX\">$mu$</tex-math></inline-formula>W/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"11 1","pages":"729-742"},"PeriodicalIF":5.1,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2016.2622738","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62966491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号