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检索条件"任意字段=IEEE Biomedical Circuits and Systems Conference"

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Photoluminescent Polymer Films for High-Sensitivity Oxygen Sensing in biomedical Implants

Photoluminescent Polymer Films for High-Sensitivity Oxygen S...

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ieee International conference on Micro Electro Mechanical systems

作者： Julian A. Singer Anton Geläschus Patrick Kleinschnittger Ute Schmidt Matthias Kuhl Andreas Bahr Institute for Integrated Circuits Hamburg University of Technology Germany Laboratory for Microelectronics IMTEK University of Freiburg Germany Biomedical Electronics Technische Universität Dresden Germany

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

This paper presents a comprehensive experimental study on photoluminescent oxygen indicators and a rapid prototyping approach to manufacture biocompatible polymer films for future implantable sensors. Combining single photon avalanche diodes (SPADs) in a complementary metal-oxide-semiconductor (CMOS) technology, post-CMOS micro light-emitting diodes (µLEDs), and a photoluminescent layer allows for precise integrated oxygen detectors. To optimize the phosphorescent component, this paper provides extensive data by comparing different indicators, light sources, concentrations, and manufacturing technologies, tailored to the application in patient-specific implants.

关键词： Micromechanical devices Mechanical sensors Oxygen Implants Photoluminescence Polymer films Rapid prototyping Sensor systems Biosensors Single-photon avalanche diodes

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Improved Support Vector Machine for ECG Signal Classification in Implantable biomedical Devices

Improved Support Vector Machine for ECG Signal Classificatio...

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Integrated circuits and Communication systems (ICICACS), ieee International conference on

作者： Pavan Srikanth SubbaRaju Patchamatla Zayd Ajsan Balsem KG Parthiban Ramachandra A C Latha Bhuvaneswari S RKInfo-Tech LLC Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Al Diwaniyah Iraq Department of Biomedical Engineering Dhaanish Ahmed Institute of Technology Coimbatore India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Dhanalakshmi Srinivasan College Of Engineering Technology Mamallapuram India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Electrocardiogram (ECG) signal classification in implantable biomedical devices plays a significant role for continuous heart monitoring and detecting cardiac abnormalities such as arrhythmias. These devices analyse ECG patterns to enable accurate diagnostics and provide necessary interventions. However, traditional ECG monitoring tools faces certain challenges like low power transfer efficiency, failure in capturing most paroxysmal Atrial Fibrillation (AFib), and difficulty in automatic determination of AFib. To solve this issue, the implantable ECG sensor with improved Support Vector Machine (SVM) is proposed to monitor the patient continuously and detect AFib effectively. The QRS complex is observed from ECG signals of 29 AFib patients is extracted by utilizing signal processing techniques such as wavelet transform and statistical analysis. The Minimum Description Length (MDL) method is applied to extract and select 12 features of variability-based heart rate parameters. Then, the improved SVM classifier with a Radial Basis Function (RBF) is used to classify the AFib in ECG data accurately. Experimental results demonstrate that the proposed implantable ECG sensor with optimized SVM achieves a better accuracy of 99.2%, sensitivity of 98.8%, specificity of 99.5%, and an F1-score of 98.9% which outperforms the existing methods like traditional SVM and CNN-based models.

关键词： Support vector machines Accuracy Pattern classification Implants Electrocardiography Feature extraction Integrated circuit modeling biomedical monitoring Monitoring Context modeling

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A Reconfigurable 0.69-1.02nJ/Classification biomedical AI Processor for Intelligent Health Monitoring Devices

A Reconfigurable 0.69-1.02nJ/Classification Biomedical AI Pr...

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Custom Integrated circuits conference (CICC)

作者： Yuanzhe Zhao Yuheng Wang Zijian Wang Yan Zhu R. P. Martins Chi-Hang Chan Minglei Zhang University of Macau Macau China Instituto Superior Tecnico/University of Lisboa Lisbon Portugal

ISBN: (数字)9798331517458

ISBN: (纸本)9798331517465

Ultra-low-power biomedical AI processors are crucial for effective intelligent health monitoring systems (HMSs) in wearable devices. Traditional HMSs rely on feature-based methods [1], [2] that necessitate human intervention for pre-extract features before classification. Unfortunately, this approach often leads to poor accuracy <90%) and can be time-consuming. Recently, end-to-end neural networks (NNs) [3]–[8] have demonstrated promising results by directly processing original ExG (EEG, ECG, EMG, etc.) signals for classification. This method circumvents the limitations imposed by human expertise and achieves higher accuracy. Yet, the implementation of more complex NNs brings forth new challenges, as illustrated in Fig. 1: 1) prior reconfigurable HMSs underutilize memory resources (~8.9% utilization in the EEG mode of [7]) due to the need for multiple NNs of varying sizes for different detection modes; 2) frequent memory access significantly dominates the power consumption of AI systems; 3) the presence of redundant bio-information in ExG signals results in unnecessary computational energy dissipation.

关键词： Program processors Accuracy Power demand Memory management Energy dissipation Artificial neural networks Electroencephalography Artificial intelligence Wearable devices Monitoring

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A Low-Power Voltage-Controlled Current Source for Wearable Bioimpedance Applications

A Low-Power Voltage-Controlled Current Source for Wearable B...

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ieee SSD International Multi-conference on systems, Signals and Devices

作者： Cherif Ouni Bilel Ghoul Hamdi Al-Shaikhli Jumana Ma'touq Ahmed Fakhfakh Olfa Kanoun Measurement and Sensor Technology Faculty of Electrical Engineering and Information Technology Chemnitz University of Technology Chemnitz Germany Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Centre of Sfax National School of Electronics and Telecommunications of Sfax Sfax Tunisia Department of Biomedical Engineering School of Applied Medical Sciences (SAMS) German Jordanian University Amman Jordan

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Bioimpedance spectroscopy (BIS) has gained prominence as a non-invasive diagnostic tool for characterizing biological tissues, offering critical insights into physiological and pathological conditions. At the core of BIS systems lies the Voltage-Controlled Current Source (VCCS), which is essential for ensuring precise and stable current delivery across varying load conditions. This paper investigates a modified dual Howland configuration with load-in-loop compensation designed for low-power applications. By operating at reduced supply voltages (5V) instead of the conventional ±12V, the proposed design addresses the limitations of existing VCCS circuits in portable and wearable medical devices. The study evaluates the performance of the proposed VCCS circuit in a wide range of frequencies (50 kHz to 1 MHz), load conditions (1000 to 18kO), and input signal types (sine, multi-sine, and sine-sweep). Key findings demonstrate stable current output, efficient power consumption, and robust performance at reduced power levels, making it suitable for bioimpedance applications requiring portable operation.

关键词： Performance evaluation Spectroscopy Pathology Power demand Medical devices circuits Bioimpedance Physiology Wearable devices Voltage control

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20.10 A 200GHz 200-Pixel 2D Near-Field Imager for biomedical Applications

20.10 A 200GHz 200-Pixel 2D Near-Field Imager for Biomedical...

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ieee International conference on Solid-State circuits (ISSCC)

作者： Ali Ameri Jun-Chau Chien Ali Niknejad University of California Berkeley CA

ISBN: (数字)9798331541019

ISBN: (纸本)9798331541026

Near fields, unencumbered by the restrictions imposed by diffraction limits, can be leveraged in high-resolution sub-wavelength imaging systems [1]. Utilizing the near-fields generated by arrays of mmWave and THz resonators can be a compelling candidate for realizing such systems. On the one hand, by increasing the frequency of operation, the resonators' physical footprint shrinks, enhancing the imaging resolution. On the other hand, their highly localized and easily perturbable EM-fields and narrow-band nature can potentially bring about high SNR detection and characterization of samples as small as single cells [2]. These potentials, however, have never been simultaneously realized due to reasons such as: 1) The challenges in the generation and the detection of mmWave and THz signals have limited previous efforts to a single-source and/or single-detector per pixel architectures [1]–[4], wherein the imaging resolution does not scale consistently with the operation frequency. 2) The absence of a proper method for the selection and the readout of a single resonator in isolation from the rest of the array further exacerbates the resolution problem. 3) The resonator Q degrades at high frequencies and in the presence of lossy biological samples, ultimately reducing the sensitivity limits. This work advances the state of the art by overcoming the aforementioned challenges through several architectural innovations: 1) Q-boosted Active Split-Ring Resonators (ASRR) are introduced, which allow dense integration and individual pixel selection with minimum capacitive loading to preserve the sensitivity. 2) ASRRs can be selectively activated and deactivated, which enables using a single source and a detector for multiple pixels. 3) Phase detection replaces the standard power or frequency detection methods, which are prone to the remarkably high phase noise of free-running high-frequency VCOs. 4) By periodically switching the selected pixel between reference and sensing states,

关键词： Hands Technological innovation Sensitivity Image resolution Resonant frequency Switches Solid state circuits Resonators Millimeter wave communication Standards

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Miniature and Wireless Ultrasonic Imaging System for Wearable Human Carotid Artery Health Monitoring

Miniature and Wireless Ultrasonic Imaging System for Wearabl...

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ieee International conference on Micro Electro Mechanical systems

作者： Lei Zhao Aocheng Bao Chong Yang Junhao Wang Xixin Cao Yipeng Lu School of Software and Microelectronics Peking University China School of Integrated Circuits Peking University China

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

This paper presents a miniature and wireless ultrasonic imaging system based on a high-frequency piezoelectric micromachined ultrasonic transducer (PMUT) array. The high fill-factor rectangular PMUT array with multimodal fusion operates at a center frequency of 11 MHz with bandwidth 7.5 MHz and transmitting sensitivity 8.25 kPa/V at 5 mm from the surface, achieving a great figure of merit (FOM) of 61.9 MHz×kPa/V. The 64-channel array, fabricated using a cavity silicon on insulator (CSOI) substrate, shows less than 4% frequency variation, ensuring excellent consistency. The superior electrical-mechanical-acoustic coupling efficiency and broad bandwidth provide a great margin for signal-to-noise ratio loss, a challenge of miniature systems due to compact circuit size and simplified signal processing. The miniature and wireless ultrasonic imaging system was developed and tested with the human carotid artery, achieving detailed imaging of arterial walls and lumen. The small size and high performance make it promising for wearable monitoring with continuous and non-invasive assessment, which is essential for early detection of atherosclerotic changes and cardiovascular health management.

关键词： Wireless communication Wireless sensor networks Ultrasonic imaging Sensitivity Ultrasonic transducer arrays Imaging Bandwidth biomedical monitoring Monitoring Carotid arteries

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A Wearable Backscatter System Featuring Concurrent RF Harvesting and Bidirectional Communication with Commodity BLE Transceivers

A Wearable Backscatter System Featuring Concurrent RF Harves...

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Custom Integrated circuits conference (CICC)

作者： Ji Xiong Yongling Zhang Junzai Chen Xiaoyu Li Jinrui ZuO Yan Wang Xiaoyi Wang Miao Meng Tongji University Shanghai China Fudan University Shanghai China

ISBN: (数字)9798331517458

ISBN: (纸本)9798331517465

Emerging wearable medical sensors enabling real-time health monitoring have become increasingly important for personalized healthcare. Nearly all devices require wireless connectivity at low power to reduce size, weight, and maintenance costs (recharging or replacing the battery). Recent works in body channel communication (BCC), such as galvanic [1], magnetic resonance [2], and capacitive coupling [3], have demonstrated low power communication around human bodies, thanks to the reduced path loss. However, current BCC solutions rely on custom-designed transceivers (TRX) that are not built into commodity hardware (i.e., smartphones/smartwatches). To enable rapid deployment at scale with low cost, communication should ideally be compatible with existing infrastructure, such as WiFi and BLE. However, conventional WiFi or BLE TRXs require 10s to 100s of mW of active power; therefore, large/frequent recharged battery is still necessary.

关键词： Wireless communication Radio frequency Wireless sensor networks Costs Transceivers Real-time systems Batteries biomedical monitoring Wireless fidelity Wearable sensors

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E-NPU: A 34~126nJ/Class Event-Driven Adaptive Neural SoC with Signal-Dynamics-Aware Feature Clustering and Multi-Model In-Memory Inference/Training for Personalized Medical Wearables

E-NPU: A 34~126nJ/Class Event-Driven Adaptive Neural SoC wit...

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Custom Integrated circuits conference (CICC)

作者： Fengshi Tian Jinbo Chen Kunming Shao Zilu Liu Jiakun Zheng Hui Wu Chaoming Fang Xiaomeng Wang Ziyang Shen Pingcheng Dong Yuan Yao Xuliang Wang Jie Yang Mohamad Sawan Chi-Ying Tsui Kwang-Ting Cheng HKUST Hong Kong SAR SAR AI Chip Center for Emerging Smart Systems Hong Kong SAR Westlake University China Tsinghua University China

ISBN: (数字)9798331517458

ISBN: (纸本)9798331517465

biomedical signal processing systems-on-chip (SoC) have shown significant promise in human-machine interfaces and closed-loop neuromodulation applications [1]–[5]. Future personalized medical wearables are anticipated to provide long-term monitoring and accurate interpretation while maintaining minimal energy consumption [6]–[8]. Lightweight neural networks, such as binary, spiking, and convolutional neural networks (BNN/SNN/CNN), are promising for neural signal analysis, with neural architecture search (NAS) techniques enabling the deployment of task-specific models optimized for accuracy and efficiency [9]–[12]. However, developing an accurate, ultra-energy-efficient medical SoC that seamlessly integrates signal feature extraction and processing with multi-model reconfigurability remains challenging. Furthermore, inter-patient variance adversely affects the accuracy of biosignal classifications in real-world scenarios, thereby limiting the reliability of these devices [13], [14].

关键词： Accuracy Limiting Human-machine systems Neural networks Neural architecture search Wearable devices Signal analysis Neuromodulation Integrated circuit reliability Monitoring

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CNN-based Neuroimaging Analysis for Mapping Neurotransmitter Dysregulation in Anxiety Disorders

CNN-based Neuroimaging Analysis for Mapping Neurotransmitter...

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Integrated circuits and Communication systems (ICICACS), ieee International conference on

作者： T Kumanan Sandeep Reddy Narani Bharat Tidke Thirumurugan R S. Karthikeyan S. Sujatha Department of Computer Science and Engineering Dr MGR Educational and Research Institute Chennai India Texas USA Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed) University Pune India Department of Electronics and Communication Engineering Adhi College of Engineering and Technology Kanchipuram India Department of Electronics and Communication Engineering K.S.R. College of Engineering Tiruchengode India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

This research examines the use of Convolutional Neural Networks (CNNs) in analyzing neuroimaging data to identify neurotransmitter dysregulation in anxiety disorders. Neurotransmitter abnormalities significantly contribute to anxiety disorders, but identifying these imbalances by imaging techniques is challenging. A CNN-based model was created using functional neuroimaging data (e.g., fMRI, PET scans) to detect patterns of neurotransmitter dysregulation. The model was trained on a dataset of neuroimaging scans from individuals with anxiety disorders, using preprocessing processes such as spatial smoothing and normalization. Various CNN models were evaluated, with a conventional CNN chosen for its optimal mix of accuracy and computing economy. The CNN model accuracy of 94.7% detects neurotransmitter dysregulation from normal brain activity. CNN showed the capacity to generalize to novel patient populations with varied demographic and genetic profiles. The proposed model presents a significant tool for enhancing diagnostic accuracy and directing personalized treatment approaches in anxiety disorders.

关键词： Neuroimaging Training Neurotransmitters Accuracy Computational modeling Anxiety disorders Brain modeling Data models Convolutional neural networks Integrated circuit modeling

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Convolutional Neural Network – Regularized Extreme Learning Machine with Hyperbolic Secant for Breast Cancer Segmentation and Classification

Convolutional Neural Network – Regularized Extreme Learning...

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Integrated circuits and Communication systems (ICICACS), ieee International conference on

作者： Bassamma Patil P Vishwanath K Priyanka Muhamed Husseyn KG Parthiban Department of Electronics and Communication Engineering HKE Society's Sir M Visvesvaraya college of Engineering Raichur India Affiliated to VTU Belagavi India Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Al Diwaniyah Iraq Department of Biomedical Engineering Dhaanish Ahmed Institute of Technology Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Early detection of breast cancer by mammogram images is essential to reduces the mortality rate of women and allow for appropriate treatment. Deep Learning (DL) algorithms are generally utilized for feature extraction and have shown effective performance. Though, these features are performed well in certain cases because of redundant and irrelevant data. To overcome this, in this manuscript developed the Convolutional Neural Network - Regularized Extreme Learning Machine (CNN-RELM) with Hyperbolic Secant (HS) activation function for precise breast cancer segmentation and classification. Initially, the images are segmented by using the Optimized Region Growing (ORG) algorithm which effectively segments the images to differentiate the different classes of tumor regions. The CNN layers extracted both low-level and high-level features and reduced dimensions while retaining significant features. The RELM includes the regularization term for enhancing the generalization ability and handles the noise data. The HS activation function process based on hyperbolic secant function and offers smooth activation, good gradient flow and the network generalization ability. The developed RELM-CNN with HS method obtained 99.87% accuracy on MIAS dataset while comparing with conventional algorithms.

关键词： Image segmentation Accuracy Extreme learning machines Noise Feature extraction Breast cancer Classification algorithms Convolutional neural networks Optimization Tumors

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