IEEE transactions on biomedical circuits and systems最新文献_第9页

A Time-Varying Equivalent Circuit Modeling and Measuring Approach for Intracardiac Communication in Leadless Pacemakers 无引线起搏器心内通信的时变等效电路建模和测量方法

IEEE transactions on biomedical circuits and systems Pub Date : 2024-02-01 DOI: 10.1109/TBCAS.2024.3360997

Ziliang Wei;Han Wang;Dongming Li;Mang I Vai;Sio Hang Pun;Jiejie Yang;Min Du;Yueming Gao

{"title":"A Time-Varying Equivalent Circuit Modeling and Measuring Approach for Intracardiac Communication in Leadless Pacemakers","authors":"Ziliang Wei;Han Wang;Dongming Li;Mang I Vai;Sio Hang Pun;Jiejie Yang;Min Du;Yueming Gao","doi":"10.1109/TBCAS.2024.3360997","DOIUrl":"10.1109/TBCAS.2024.3360997","url":null,"abstract":"Intracardiac wireless communication is crucial for the development of multi-chamber leadless cardiac pacemakers (LCP). However, the time-varying characteristics of intracardiac channel pose major challenges. As such, mastering the dynamic conduction properties of the intracardiac channel and modeling the equivalent time-varying channel are imperative for realizing LCP multi-chamber pacing. In this article, we present a limiting volume variational approach based on the electrical properties of cardiac tissues and trends in chamber volume variation. This approach was used to establish a quasi-static and a continuous time-varying equivalent circuit model of an intracardiac channel. An equivalence analysis was conducted on the model, and a discrete time-varying equivalent circuit phantom grounded on the cardiac cycle was subsequently established. Moreover, an ex vivo cardiac experimental platform was developed for verification. Results indicate that in the frequency domain, the congruence between phantom and ex vivo experimental outcomes is as high as 94.3%, affirming the reliability of the equivalent circuit model. In the time domain, the correlation is up to 75.3%, corroborating its effectiveness. The proposed time-varying equivalent circuit model exhibits stable and standardized dynamic attributes, serving as a powerful tool for addressing time-varying challenges and simplifying in vivo or ex vivo experiments.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139673917","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 36-nW Electrocardiogram Anomaly Detector Based on a 1.5-bit Non-Feedback Delta Quantizer for Always-on Cardiac Monitoring 基于 1.5 位非反馈德尔塔量化器的 36-nW 心电图异常检测器，用于始终在线的心脏监护。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-01-31 DOI: 10.1109/TBCAS.2024.3360886

Ning Pu;Nan Wu;Syed Muhammad Abubakar;Yihuai Yang;Xinpeng Liu;Sining Pan;Yanshu Guo;Wen Jia;Zhihua Wang;Hanjun Jiang

{"title":"A 36-nW Electrocardiogram Anomaly Detector Based on a 1.5-bit Non-Feedback Delta Quantizer for Always-on Cardiac Monitoring","authors":"Ning Pu;Nan Wu;Syed Muhammad Abubakar;Yihuai Yang;Xinpeng Liu;Sining Pan;Yanshu Guo;Wen Jia;Zhihua Wang;Hanjun Jiang","doi":"10.1109/TBCAS.2024.3360886","DOIUrl":"10.1109/TBCAS.2024.3360886","url":null,"abstract":"An always-on electrocardiogram (ECG) anomaly detector (EAD) with ultra-low power (ULP) consumption is proposed for continuous cardiac monitoring applications. The detector is featured with a 1.5-bit non-feedback delta quantizer (DQ) based feature extractor, followed by a multiplier-less convolutional neural network (CNN) engine, which eliminates the traditional high-resolution analog-to-digital converter (ADC) in conventional signal processing systems. The DQ uses a computing-in-capacitor (CIC) subtractor to quantize the sample-to-sample difference of ECG signal into 1.5-bit ternary codes, which is insensitive to low-frequency baseline wandering. The subsequent event-driven classifier is composed of a low-complexity coarse detector and a systolic-array-based CNN engine for ECG anomaly detection. The DQ and the digital CNN are fabricated in 65-nm and 180-nm CMOS technology, respectively, and the two chips are integrated on board through wire bonding. The measured detection accuracy is 90.6% ∼ 91.3% when tested on the MIT-BIH arrhythmia database, identifying three different ECG anomalies. Operating at 1 V and 1.4 V power supplies for the DQ and the digital CNN, respectively, the measured long-term average power consumption of the core circuits is 36 nW, which makes the detector among those state-of-the-art always-on cardiac anomaly detection devices with the lowest power consumption.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139652473","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 2.3–5.7 μW Tri-Modal Self-Adaptive Photoplethysmography Sensor Interface IC for Heart Rate, SpO2, and Pulse Transit Time Co-Monitoring 用于心率、血氧饱和度和脉搏传输时间协同监测的 2.3-5.7μW 三模式自适应光敏传感器接口集成电路。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-01-30 DOI: 10.1109/TBCAS.2024.3360140

Peng Wang;Rishika Agarwala;Natalie B. Ownby;Xinjian Liu;Benton H. Calhoun

{"title":"A 2.3–5.7 μW Tri-Modal Self-Adaptive Photoplethysmography Sensor Interface IC for Heart Rate, SpO2, and Pulse Transit Time Co-Monitoring","authors":"Peng Wang;Rishika Agarwala;Natalie B. Ownby;Xinjian Liu;Benton H. Calhoun","doi":"10.1109/TBCAS.2024.3360140","DOIUrl":"10.1109/TBCAS.2024.3360140","url":null,"abstract":"This paper presents a tri-modal self-adaptive photoplethysmography (PPG) sensor interface IC for concurrently monitoring heart rate, SpO\u00002\u0000, and pulse transit time, which is a critical intermediate parameter to derive blood pressure. By implementing a highly-reconfigurable analog front-end (AFE) architecture, flexible signal chain timing control, and flexible dual-LED drivers, this sensor interface provides wide operating space to support various PPG-sensing use cases. A heart-beat-locked-loop (HBLL) scheme is further extended to achieve time-multiplexed dual-input pulse transit time extraction based on two PPG sensors placed at fingertip and chest. A self-adaptive calibration scheme is proposed to automatically match the chip's operating point with the current use case, guaranteeing a sufficient signal-to-noise ratio for the user while consuming minimum system power. This paper proposes a DC offset cancellation (DCOC) approach comprised by a logarithmic transimpedance amplifier and an 8-bit SAR ADC, achieving a measured 38 nA residue error and 8.84 \u0000<italic>μ\u0000A maximum input current. Fabricated in a 65nm CMOS process, the proposed tri-modal PPG sensor interface consumes 2.3–5.7 \u0000<italic>μ\u0000W AFE power and 1.52 mm\u00002\u0000 die area with 102dB (SpO\u00002\u0000 mode), 110–116 dB (HR & PTT mode) dynamic range. A SpO\u00002\u0000 test case and a HR & PTT test case are both demonstrated in the paper, achieving 18.9 \u0000<italic>μ\u0000W and 43.7 \u0000<italic>μ\u0000W system power, respectively.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139643549","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

Wirelessly Powered and Bi-Directional Data Communication System With Adaptive Conversion Chain for Multisite Biomedical Implants Over Single Inductive Link 带有自适应转换链的无线供电和双向数据通信系统，用于通过单一感应链路进行多点生物医学植入。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-01-29 DOI: 10.1109/TBCAS.2024.3359772

Mohammad Javad Karimi;Menghe Jin;Yuxuan Zhou;Catherine Dehollain;Alexandre Schmid

{"title":"Wirelessly Powered and Bi-Directional Data Communication System With Adaptive Conversion Chain for Multisite Biomedical Implants Over Single Inductive Link","authors":"Mohammad Javad Karimi;Menghe Jin;Yuxuan Zhou;Catherine Dehollain;Alexandre Schmid","doi":"10.1109/TBCAS.2024.3359772","DOIUrl":"10.1109/TBCAS.2024.3359772","url":null,"abstract":"A wirelessly powered and data communication system is presented which is implemented as a full system, designed for multisite implanted biomedical applications. The system is capable of receiving wireless power and data communication for each implant separately, using inductive links with different resonance frequencies. To achieve this, dual-band coils are presented in the system. In addition, the system supports bi-directional half-duplex data communication, utilizing amplitude and load shift keying (ASK and LSK) modulation schemes over a single inductive link. The system employs a digitally assisted active rectifier and an automatic resonance tuning system, to improve the power transfer efficiency (PTE) through various coupling coefficients, while minimizing the reverse current and power dissipation. The power control unit enables closed-loop monitoring to prevent high or low power delivery, and it can detect inefficient or excessive wireless power transmission or prevent temperature elevation by limiting the voltage to a safe level. A new structure of self-sampling separated-\u0000<inline-formula><tex-math>$V_{b}$</tex-math></inline-formula>\u0000 ASK demodulator is proposed in the paper which is utilized within the data conversion chain, serving both the external and implanted units. The whole system is fabricated using a standard 180-nm 1.8/3.3 V CMOS process with a core area of 0.82 mm\u0000<inline-formula><tex-math>$^text{2}$</tex-math></inline-formula>\u0000. The system is tested with coupled multisite inductive links and offers the maximum overall PTE of 31.2%, from the Tx coil to the implant load.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139577324","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

Efficient in Vivo Neural Signal Compression Using an Autoencoder-Based Neural Network 使用基于自动编码器的神经网络高效压缩体内神经信号

IEEE transactions on biomedical circuits and systems Pub Date : 2024-01-29 DOI: 10.1109/TBCAS.2024.3359994

Daniel Valencia;Patrick P. Mercier;Amir Alimohammad

{"title":"Efficient in Vivo Neural Signal Compression Using an Autoencoder-Based Neural Network","authors":"Daniel Valencia;Patrick P. Mercier;Amir Alimohammad","doi":"10.1109/TBCAS.2024.3359994","DOIUrl":"10.1109/TBCAS.2024.3359994","url":null,"abstract":"Conventional in vivo neural signal processing involves extracting spiking activity within the recorded signals from an ensemble of neurons and transmitting only spike counts over an adequate interval. However, for brain-computer interface (BCI) applications utilizing continuous local field potentials (LFPs) for cognitive decoding, the volume of neural data to be transmitted to a computer imposes relatively high data rate requirements. This is particularly true for BCIs employing high-density intracortical recordings with hundreds or thousands of electrodes. This article introduces the first autoencoder-based compression digital circuit for the efficient transmission of LFP neural signals. Various algorithmic and architectural-level optimizations are implemented to significantly reduce the computational complexity and memory requirements of the designed in vivo compression circuit. This circuit employs an autoencoder-based neural network, providing a robust signal reconstruction. The application-specific integrated circuit (ASIC) of the in vivo compression logic occupies the smallest silicon area and consumes the lowest power among the reported state-of-the-art compression ASICs. Additionally, it offers a higher compression rate and a superior signal-to-noise and distortion ratio.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139577322","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 Frequency-Switching Inductive Power Transfer System for Wireless, Miniaturised and Large-Scale Neural Interfaces 用于无线、微型和大规模神经接口的频率切换感应式功率传输系统。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-01-29 DOI: 10.1109/TBCAS.2024.3359481

Gian Luca Barbruni;Claudia Cordara;Marco Carminati;Sandro Carrara;Diego Ghezzi

{"title":"A Frequency-Switching Inductive Power Transfer System for Wireless, Miniaturised and Large-Scale Neural Interfaces","authors":"Gian Luca Barbruni;Claudia Cordara;Marco Carminati;Sandro Carrara;Diego Ghezzi","doi":"10.1109/TBCAS.2024.3359481","DOIUrl":"10.1109/TBCAS.2024.3359481","url":null,"abstract":"Three-coil inductive power transfer is the state-of-the-art solution to power multiple miniaturised neural implants. However, the maximum delivered power is limited by the efficiency of the powering link and safety constrains. Here we propose a frequency-switching inductive link, where the passive resonator normally used in a three-coil link is replaced by an active resonator. It receives power from the external transmitter via a two-coil inductive link at the low frequency of 13.56 MHz. Then, it switches the operating frequency to the higher frequency of 433.92 MHz through a dedicated circuitry. Last, it transmits power to 1024 miniaturised implants via a three-coil inductive link using an array of 37 focusing resonators for a brain coverage of 163.84 mm\u0000<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>\u0000. Our simulations reported a power transfer efficiency of 0.013\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000 and a maximum power delivered to the load of 1970 \u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000W under safety-constrains, which are respectively two orders of magnitude and more than six decades higher compared to an equivalent passive three-coil link. The frequency-switching inductive system is a scalable and highly versatile solution for wireless, miniaturised and large-scale neural interfaces.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139577319","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 Circuits and Systems Society Information 电气和电子工程师学会电路与系统协会信息

IEEE transactions on biomedical circuits and systems Pub Date : 2024-01-26 DOI: 10.1109/TBCAS.2024.3357911

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Incoming Editorial 来稿编辑

IEEE transactions on biomedical circuits and systems Pub Date : 2024-01-26 DOI: 10.1109/TBCAS.2024.3356620

Pedram Mohseni

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IEEE Transactions on Biomedical Circuits and Systems Publication Information IEEE 生物医学电路与系统论文集》出版信息

IEEE transactions on biomedical circuits and systems Pub Date : 2024-01-26 DOI: 10.1109/TBCAS.2024.3349794

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IEEE transactions on biomedical circuits and systems Pub Date : 2024-01-26 DOI: 10.1109/TBCAS.2024.3357913

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