IEEE transactions on biomedical circuits and systems最新文献

{"title":"Energy-Efficient Spectral Analysis of ECGs on Resource Constrained IoT Devices.","authors":"Charalampos Eleftheriadis, Georgios Karakonstantis","doi":"10.1109/TBCAS.2024.3406520","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3406520","url":null,"abstract":"<p><p>Power spectral analysis (PSA) is one of the most popular and insightful methods, currently employed in several biomedical applications, aiming to identify and monitor various health related conditions. Among the most common applications of PSA is heart rate variability (HRV) analysis, which allows the extraction of further insights compared with conventional time-domain methods. Unfortunately, existing PSA approaches exhibit high computational complexity, hindering their execution on power-constrained embedded internet of things (IoT) devices. Such IoT devices are increasingly used for monitoring various conditions mainly by processing the input signals in the less complex time-domain. In this paper, a new low-complexity PSA system based on fast Gaussian gridding (FGG) is proposed, which can be used to calculate the Lomb-Scargle periodogram (LSP) of a non-uniformly spaced RR tachogram. The proposed approach is implemented on a popular ARM Cortex-M4 based embedded system, which is widely used in common wearables, and compared with conventional LSP-based approaches. Utilizing this experimental setup, a meticulous analysis is performed in terms of power, performance and quality under different operational settings, such as the total input/output samples, precision of computations, computer arithmetic (floating/fixed-point), and clock frequency. The experimental results show that the proposed FGG-based LSP approach, when specifically optimized for the targeted embedded device, outperforms existing approaches by up-to 92.99% and 91.70% in terms of energy consumption and total execution time respectively, with minimal accuracy loss.</p>","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141177137","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}

{"title":"Ultra-Compact Pulse Charger for Lithium Polymer Battery with Simple Built-in Resistance Compensation in Biomedical Applications.","authors":"Yemin Kim, Junhyuck Lee, Byunghun Lee","doi":"10.1109/TBCAS.2024.3401846","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3401846","url":null,"abstract":"Active implantable medical devices (AIMDs) rely on batteries for uninterrupted operation and patient safety. Therefore, it is critical to ensure battery safety and longevity. To achieve this, constant current/constant voltage (CC/CV) methods have been commonly used and research has been conducted to compensate for the effects of built-in resistance (BIR) of batteries. However, conventional CC/CV methods may pose the risk of lithium plating. Furthermore, conventional compensation methods for BIR require external components, complex algorithms, or large chip sizes, which inhibit the miniaturization and integration of AIMDs. To address this issue, we have developed a pulse charger that utilizes pulse current to ensure battery safety and facilitate easy compensation for BIR. A comparison with previous research on BIR compensation shows that our approach achieves the smallest chip size of 0.0062 mm2 and the lowest system complexity using 1-bit ADC. In addition, we have demonstrated a reduction in charging time by at least 44.4% compared to conventional CC/CV methods, validating the effectiveness of our system's BIR compensation. The compact size and safety features of the proposed charging system make it promising for AIMDs, which have space-constrained environments.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140970496","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}

{"title":"A Voltage-Assist 16-Channel Electrochemical Biosensor with Linearity Compensation.","authors":"Yifei Fan, Dongmin Shi, Yanhang Chen, Qifeng Huang, Siji Huang, Qiwei Zhao, Saqib Mohamad, Jie Yuan","doi":"10.1109/TBCAS.2024.3401784","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3401784","url":null,"abstract":"<p><p>Large capacitive loading of electrodes induces massive error current and imperfect settling in the electrochemical signal acquisition process, leading to inaccurate acquisition results. To efficiently mitigate this inaccuracy, this paper presents a current-and-voltage dual-mode acquisition technique in which a voltage front-end (VFE) is employed to acquire the electrode voltage error and compensate the nonlinearity induced by the electrode capacitive loading. Therefore, the gain and bandwidth requirements of the current front end (CFE) can be relaxed to reduce the complexity and power consumption. With a relieved gain requirement, an inverter-based capacitive trans-impedance amplifier (IB-CTIA) is adopted to boost the input transconductance for low-noise design. By reusing the supply current, the IB-CTIA effectively achieves a low input-referred current noise of 3.9 pA_rms and a dynamic range (DR) of 126 dB with only 18-μW static power. The prototype chip is fabricated in a 180-nm CMOS process. Interleukin-6 immunoassays (IL-6) are implemented to verify the chip's performance. With the proposed nonlinear error compensation, the correlation coefficient of the detection result is improved from 0.951 to 0.980 and the limit of detection (LoD) is reduced from 8.31 pg/mL to 6.90 pg/mL.</p>","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946729","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}

{"title":"A Power-and-Area-Efficient Channel-Interleaved Neural Signal Processor for Wireless Brain-Computer Interfaces with Unsupervised Spike Sorting.","authors":"Zichen Hu, Zhining Zhou, Hongming Lyu","doi":"10.1109/TBCAS.2024.3395353","DOIUrl":"10.1109/TBCAS.2024.3395353","url":null,"abstract":"<p><p>Next generation of wireless brain-computer-interface (BCI) devices require dedicated neural signal processors (NSPs) to extract key neurological information while operating within given power consumption and transmission bandwidth limits. Spike detection and clustering are important signal processing steps in neurological research and clinical applications. Computational-friendly spike detection and feature extraction algorithms are first systematically evaluated in this work. The nonlinear energy operator (NEO) and the first-and-second-derivative (FSDE) together with the 'perturbed' K-mean clustering achieve the highest accuracy performance. An NSP ASIC is implemented in a channel-interleaved architecture and the folding ratio of 16 leads to the minimum power-and-area product. As the result, the NSP consumes 2-μW power consumption and occupies 0.0057 mm2 for each channel in a 65-nm CMOS technology. The proposed system achieves the unsupervised spike classification accuracy of 92% and a data-rate reduction of 98.3%, showing the promise for realizing high-channel-count wireless BCIs.</p>","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140905047","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}

{"title":"BrainFuseNet: Enhancing Wearable Seizure Detection Through EEG-PPG-Accelerometer Sensor Fusion and Efficient Edge Deployment","authors":"Thorir Mar Ingolfsson;Xiaying Wang;Upasana Chakraborty;Simone Benatti;Adriano Bernini;Pauline Ducouret;Philippe Ryvlin;Sándor Beniczky;Luca Benini;Andrea Cossettini","doi":"10.1109/TBCAS.2024.3395534","DOIUrl":"10.1109/TBCAS.2024.3395534","url":null,"abstract":"This paper introduces \u0000<sc>BrainFuseNet</small>\u0000, a novel lightweight seizure detection network based on the sensor fusion of electroencephalography (EEG) with photoplethysmography (PPG) and accelerometer (ACC) signals, tailored for low-channel count wearable systems. \u0000<sc>BrainFuseNet</small>\u0000 utilizes the Sensitivity-Specificity Weighted Cross-Entropy (SSWCE), an innovative loss function incorporating sensitivity and specificity, to address the challenge of heavily unbalanced datasets. The \u0000<sc>BrainFuseNet</small>\u0000-SSWCE approach successfully detects \u0000<inline-formula><tex-math>$93.5%$</tex-math></inline-formula>\u0000 seizure events on the CHB-MIT dataset (\u0000<inline-formula><tex-math>$76.34%$</tex-math></inline-formula>\u0000 sample-based sensitivity), for EEG-based classification with only four channels. On the PEDESITE dataset, we demonstrate a sample-based sensitivity and false positive rate of \u0000<inline-formula><tex-math>$60.66%$</tex-math></inline-formula>\u0000 and \u0000<inline-formula><tex-math>$1.18$</tex-math></inline-formula>\u0000 FP/h, respectively, when considering EEG data alone. Additionally, we demonstrate that integrating PPG signals increases the sensitivity to \u0000<inline-formula><tex-math>$61.22%$</tex-math></inline-formula>\u0000 (successfully detecting \u0000<inline-formula><tex-math>$92%$</tex-math></inline-formula>\u0000 seizure events) while decreasing the number of false positives to \u0000<inline-formula><tex-math>$1.0$</tex-math></inline-formula>\u0000 FP/h. Finally, when ACC data are also considered, the sensitivity increases to \u0000<inline-formula><tex-math>$64.28%$</tex-math></inline-formula>\u0000 (successfully detecting \u0000<inline-formula><tex-math>$95%$</tex-math></inline-formula>\u0000 seizure events) and the number of false positives drops to only \u0000<inline-formula><tex-math>$0.21$</tex-math></inline-formula>\u0000 FP/h for sample-based estimations, with less than one false alarm per day when considering event-based estimations. \u0000<sc>BrainFuseNet</small>\u0000 is resource-friendly and well-suited for implementation on low-power embedded platforms, and we evaluate its performance on GAP9, a state-of-the-art parallel ultra-low power (PULP) microcontroller for tiny Machine Learning applications on wearables. The implementation on GAP9 achieves an energy efficiency of \u0000<inline-formula><tex-math>$21.43$</tex-math></inline-formula>\u0000 GMAC/s/W, with an energy consumption per inference of only \u0000<inline-formula><tex-math>$0.11$</tex-math></inline-formula>\u0000 mJ at high performance (\u0000<inline-formula><tex-math>$412.54$</tex-math></inline-formula>\u0000 MMAC/s). The \u0000<sc>BrainFuseNet</small>\u0000-SSWCE method demonstrates effective and accurate seizure detection on heavily imbalanced datasets while achieving state-of-the-art performance in the false positive rate and being well-suited for deployment on energy-constrained edge devices.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10511055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140827553","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}

{"title":"Together, We are advance technology","authors":"","doi":"10.1109/TBCAS.2024.3381731","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3381731","url":null,"abstract":"","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10487130","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140340074","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}

{"title":"IEEE Transactions on Biomedical Circuits and Systems Publication Information","authors":"","doi":"10.1109/TBCAS.2024.3377328","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3377328","url":null,"abstract":"","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10487129","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140339932","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}

{"title":"Blank Page","authors":"","doi":"10.1109/TBCAS.2024.3377332","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3377332","url":null,"abstract":"","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10487133","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140340153","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}

{"title":"Guest Editorial—Selected Papers From the 2023 IEEE International Symposium on Circuits and Systems","authors":"Hanjun Jiang;Ulkuhan Guler;Marco Carminati","doi":"10.1109/TBCAS.2024.3376888","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3376888","url":null,"abstract":"","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10487138","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140339977","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}

{"title":"TechRxiv: Share Your Preprint Research with the World!","authors":"","doi":"10.1109/TBCAS.2024.3381729","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3381729","url":null,"abstract":"","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10487131","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140340073","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}