IEEE transactions on biomedical circuits and systems最新文献_第10页

Neural Dielet 2.0: A 128-Channel 2mm$times$2mm Battery-Free Neural Dielet Merging Simultaneous Multi-Channel Transmission Through Multi-Carrier Orthogonal Backscatter 神经小模组 2.0：通过多载波正交反向散射同时进行多信道传输的 128 信道 2mm×2mm 无电池神经小模组。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-06-19 DOI: 10.1109/TBCAS.2024.3416728

Changgui Yang;Zhihuan Zhang;Lei Zhang;Yunshan Zhang;Zhuhao Li;Yuxuan Luo;Gang Pan;Bo Zhao

{"title":"Neural Dielet 2.0: A 128-Channel 2mm$times$2mm Battery-Free Neural Dielet Merging Simultaneous Multi-Channel Transmission Through Multi-Carrier Orthogonal Backscatter","authors":"Changgui Yang;Zhihuan Zhang;Lei Zhang;Yunshan Zhang;Zhuhao Li;Yuxuan Luo;Gang Pan;Bo Zhao","doi":"10.1109/TBCAS.2024.3416728","DOIUrl":"10.1109/TBCAS.2024.3416728","url":null,"abstract":"Miniaturization of wireless neural-recording systems enables minimally-invasive surgery and alleviates the rejection reactions for implanted brain-computer interface (BCI) applications. Simultaneous massive-channel recording capability is essential to investigate the behaviors and inter-connections in billions of neurons. In recent years, battery-free techniques based on wireless power transfer (WPT) and backscatter communication have reduced the sizes of neural-recording implants by battery eliminating and antenna sharing. However, the existing battery-free chips realize the multi-channel merging in the signal-acquisition circuits, which leads to large chip area, signal attenuation, insufficient channel number or low bandwidth, etc. In this work, we demonstrate a 2mm<inline-formula><tex-math>$times$</tex-math></inline-formula>2mm battery-free neural dielet, which merges 128 channels in the wireless part. The neural dielet is fabricated with 65nm CMOS process, and measured results show that: 1) The proposed multi-carrier orthogonal backscatter technique achieves a high data rate of 20.16Mb/s and an energy efficiency of 0.8pJ/bit. 2) A self-calibrated direct digital converter (SC-DDC) is proposed to fit the 128 channels in the 2mm<inline-formula><tex-math>$times$</tex-math></inline-formula>2mm die, and then the all-digital implementation achieves 0.02mm<inline-formula><tex-math>${}^{2}$</tex-math></inline-formula> area and 9.87<inline-formula><tex-math>$mu$</tex-math></inline-formula>W power per channel.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 1","pages":"226-237"},"PeriodicalIF":0.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141428583","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

Electrical Capacitance Tomography of Cell Cultures on a CMOS Microelectrode Array CMOS 微电极阵列上的细胞培养电容层析成像。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-06-17 DOI: 10.1109/TBCAS.2024.3415360

Manar Abdelatty;Joseph Incandela;Kangping Hu;Pushkaraj Joshi;Joseph W. Larkin;Sherief Reda;Jacob K. Rosenstein

{"title":"Electrical Capacitance Tomography of Cell Cultures on a CMOS Microelectrode Array","authors":"Manar Abdelatty;Joseph Incandela;Kangping Hu;Pushkaraj Joshi;Joseph W. Larkin;Sherief Reda;Jacob K. Rosenstein","doi":"10.1109/TBCAS.2024.3415360","DOIUrl":"10.1109/TBCAS.2024.3415360","url":null,"abstract":"Electrical capacitance tomography (ECT) can be used to predict information about the interior volume of an object based on measured capacitance at its boundaries. Here, we present a microscale capacitance tomography system with a spatial resolution of 10 microns using an active CMOS microelectrode array. We introduce a deep learning model for reconstructing 3-D volumes of cell cultures using the boundary capacitance measurements acquired from the sensor array, which is trained using a multi-objective loss function that combines a pixel-wise loss function, a distribution-based loss function, and a region-based loss function to improve model's reconstruction accuracy. The multi-objective loss function enhances the model's reconstruction accuracy by 3.2% compared to training only with a pixel-wise loss function. Compared to baseline computational methods, our model achieves an average of 4.6% improvement on the datasets evaluated. We demonstrate our approach on experimental datasets of bacterial biofilms, showcasing the system's ability to resolve microscopic spatial features of cell cultures in three dimensions. Microscale capacitance tomography can be a low-cost, low-power, label-free tool for 3-D imaging of biological samples.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 4","pages":"799-809"},"PeriodicalIF":0.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141422262","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

sEMG-Driven Hand Dynamics Estimation With Incremental Online Learning on a Parallel Ultra-Low-Power Microcontroller 在并行超低功耗微控制器上利用增量在线学习进行 sEMG 驱动的手部动态估计。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-06-17 DOI: 10.1109/TBCAS.2024.3415392

Marcello Zanghieri;Pierangelo Maria Rapa;Mattia Orlandi;Elisa Donati;Luca Benini;Simone Benatti

{"title":"sEMG-Driven Hand Dynamics Estimation With Incremental Online Learning on a Parallel Ultra-Low-Power Microcontroller","authors":"Marcello Zanghieri;Pierangelo Maria Rapa;Mattia Orlandi;Elisa Donati;Luca Benini;Simone Benatti","doi":"10.1109/TBCAS.2024.3415392","DOIUrl":"10.1109/TBCAS.2024.3415392","url":null,"abstract":"Surface electromyography (sEMG) is a State-of-the-Art (SoA) sensing modality for non-invasive human-machine interfaces for consumer, industrial, and rehabilitation use cases. The main limitation of the current sEMG-driven control policies is the sEMG's inherent variability, especially cross-session due to sensor repositioning; this limits the generalization of the Machine/Deep Learning (ML/DL) in charge of the signal-to-command mapping. The other hot front on the ML/DL side of sEMG-driven control is the shift from the classification of fixed hand positions to the regression of hand kinematics and dynamics, promising a more versatile and fluid control. We present an incremental online-training strategy for sEMG-based estimation of simultaneous multi-finger forces, using a small Temporal Convolutional Network suitable for embedded learning-on-device. We validate our method on the HYSER dataset, cross-day. Our incremental online training reaches a cross-day Mean Absolute Error (MAE) of (9.58 ± 3.89)% of the Maximum Voluntary Contraction on HYSER's RANDOM dataset of improvised, non-predefined force sequences, which is the most challenging and closest to real scenarios. This MAE is on par with an accuracy-oriented, non-embeddable offline training exploiting more epochs. Further, we demonstrate that our online training approach can be deployed on the GAP9 ultra-low power microcontroller, obtaining a latency of 1.49 ms and an energy draw of just 40.4 uJ per forward-backward-update step. These results show that our solution fits the requirements for accurate and real-time incremental training-on-device.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 4","pages":"810-820"},"PeriodicalIF":0.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141422263","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

Dual-Mode Imaging System for Early Detection and Monitoring of Ocular Surface Diseases 用于早期检测和监控眼表疾病的双模式成像系统。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-06-14 DOI: 10.1109/TBCAS.2024.3411713

Yuxing Li;Pak Wing Chiu;Vincent Tam;Allie Lee;Edmund Y. Lam

{"title":"Dual-Mode Imaging System for Early Detection and Monitoring of Ocular Surface Diseases","authors":"Yuxing Li;Pak Wing Chiu;Vincent Tam;Allie Lee;Edmund Y. Lam","doi":"10.1109/TBCAS.2024.3411713","DOIUrl":"10.1109/TBCAS.2024.3411713","url":null,"abstract":"The global prevalence of ocular surface diseases (OSDs), such as dry eyes, conjunctivitis, and subconjunctival hemorrhage (SCH), is steadily increasing due to factors such as aging populations, environmental influences, and lifestyle changes. These diseases affect millions of individuals worldwide, emphasizing the importance of early diagnosis and continuous monitoring for effective treatment. Therefore, we present a deep learning-enhanced imaging system for the automated, objective, and reliable assessment of these three representative OSDs. Our comprehensive pipeline incorporates processing techniques derived from dual-mode infrared (IR) and visible (RGB) images. It employs a multi-stage deep learning model to enable accurate and consistent measurement of OSDs. This proposed method has achieved a 98.7% accuracy with an F1 score of 0.980 in class classification and a 96.2% accuracy with an F1 score of 0.956 in SCH region identification. Furthermore, our system aims to facilitate early diagnosis of meibomian gland dysfunction (MGD), a primary factor causing dry eyes, by quantitatively analyzing the meibomian gland (MG) area ratio and detecting gland morphological irregularities with an accuracy of 88.1% and an F1 score of 0.781. To enhance convenience and timely OSD management, we are integrating a portable IR camera for obtaining meibography during home inspections. Our system demonstrates notable improvements in expanding dual-mode image-based diagnosis for broader applicability, effectively enhancing patient care efficiency. With its automation, accuracy, and compact design, this system is well-suited for early detection and ongoing assessment of OSDs, contributing to improved eye healthcare in an accessible and comprehensible manner.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 4","pages":"783-798"},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141322189","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

MorphBungee: A 65-nm 7.2-mm2 27-µJ/Image Digital Edge Neuromorphic Chip With on-Chip 802-Frame/s Multi-Layer Spiking Neural Network Learning MorphBungee：具有片上 802 帧/秒多层尖峰神经网络学习功能的 65 纳米 7.2 mm2 27-μJ/image 数字边缘神经形态芯片。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-06-11 DOI: 10.1109/TBCAS.2024.3412908

Tengxiao Wang;Min Tian;Haibing Wang;Zhengqing Zhong;Junxian He;Fang Tang;Xichuan Zhou;Yingcheng Lin;Shuang-Ming Yu;Liyuan Liu;Cong Shi

{"title":"MorphBungee: A 65-nm 7.2-mm2 27-µJ/Image Digital Edge Neuromorphic Chip With on-Chip 802-Frame/s Multi-Layer Spiking Neural Network Learning","authors":"Tengxiao Wang;Min Tian;Haibing Wang;Zhengqing Zhong;Junxian He;Fang Tang;Xichuan Zhou;Yingcheng Lin;Shuang-Ming Yu;Liyuan Liu;Cong Shi","doi":"10.1109/TBCAS.2024.3412908","DOIUrl":"10.1109/TBCAS.2024.3412908","url":null,"abstract":"This paper presents a digital edge neuromorphic spiking neural network (SNN) processor chip for a variety of edge intelligent cognitive applications. This processor allows high-speed, high-accuracy and fully on-chip spike-timing-based multi-layer SNN learning. It is characteristic of hierarchical multi-core architecture, event-driven processing paradigm, meta-crossbar for efficient spike communication, and hybrid and reconfigurable parallelism. A prototype chip occupying an active silicon area of 7.2 mm2 was fabricated using a 65-nm 1P9M CMOS process. when running a 256-256-256-256-200 4-layer fully-connected SNN on downscaled 16 × 16 MNIST images. it typically achieved a high-speed throughput of 802 and 2270 frames/s for on-chip learning and inference, respectively, with a relatively low power dissipation of around 61 mW at a 100 MHz clock rate under a 1.0V core power supply, Our on-chip learning results in comparably high visual recognition accuracies of 96.06%, 83.38%, 84.53%, 99.22% and 100% on the MNIST, Fashion-MNIST, ETH-80, Yale-10 and ORL-10 datasets, respectively. In addition, we have successfully applied our neuromorphic chip to demonstrate high-resolution satellite cloud image segmentation and non-visual tasks including olfactory classification and textural news categorization. These results indicate that our neuromorphic chip is suitable for various intelligent edge systems under restricted cost, energy and latency budgets while requiring in-situ self-adaptative learning capability.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 1","pages":"209-225"},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141307673","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

Real-Time Motor Unit Tracking From sEMG Signals With Adaptive ICA on a Parallel Ultra-Low Power Processor 利用并行超低功耗处理器上的自适应 ICA，从 sEMG 信号中实时跟踪电机单元。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-06-07 DOI: 10.1109/TBCAS.2024.3410840

Mattia Orlandi;Pierangelo Maria Rapa;Marcello Zanghieri;Sebastian Frey;Victor Kartsch;Luca Benini;Simone Benatti

{"title":"Real-Time Motor Unit Tracking From sEMG Signals With Adaptive ICA on a Parallel Ultra-Low Power Processor","authors":"Mattia Orlandi;Pierangelo Maria Rapa;Marcello Zanghieri;Sebastian Frey;Victor Kartsch;Luca Benini;Simone Benatti","doi":"10.1109/TBCAS.2024.3410840","DOIUrl":"10.1109/TBCAS.2024.3410840","url":null,"abstract":"Spike extraction by blind source separation (BSS) algorithms can successfully extract physiologically meaningful information from the sEMG signal, as they are able to identify motor unit (MU) discharges involved in muscle contractions. However, BSS approaches are currently restricted to isometric contractions, limiting their applicability in real-world scenarios. We present a strategy to track MUs across different dynamic hand gestures using adaptive independent component analysis (ICA): first, a pool of MUs is identified during isometric contractions, and the decomposition parameters are stored; during dynamic gestures, the decomposition parameters are updated online in an unsupervised fashion, yielding the refined MUs; then, a Pan-Tompkins-inspired algorithm detects the spikes in each MUs; finally, the identified spikes are fed to a classifier to recognize the gesture. We validate our approach on a 4-subject, 7-gesture + rest dataset collected with our custom 16-channel dry sEMG armband, achieving an average balanced accuracy of 85.58 \u0000<inline-formula><tex-math>$pm$</tex-math></inline-formula>\u0000 14.91% and macro-F1 score of 85.86 \u0000<inline-formula><tex-math>$pm$</tex-math></inline-formula>\u0000 14.48%. We deploy our solution onto GAP9, a parallel ultra-low-power microcontroller specialized for computation-intensive linear algebra applications at the edge, obtaining an energy consumption of 4.72 mJ @ 240 MHz and a latency of 121.3 ms for each 200 ms-long window of sEMG signal.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 4","pages":"771-782"},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141289049","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

Adaptable Dual-Tuned Optically Controlled on-Coil RF Power Amplifier for MRI 用于核磁共振成像的适应性双调谐光控线圈上射频功率放大器。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-06-05 DOI: 10.1109/TBCAS.2024.3403093

Natalia Gudino

引用次数: 0

Supervised Contrastive Learning Framework and Hardware Implementation of Learned ResNet for Real-Time Respiratory Sound Classification 用于实时呼吸声分类的有监督对比学习框架和学习到的 ResNet 硬件实现。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-06-05 DOI: 10.1109/TBCAS.2024.3409584

Jinhai Hu;Cong Sheng Leow;Shuailin Tao;Wang Ling Goh;Yuan Gao

{"title":"Supervised Contrastive Learning Framework and Hardware Implementation of Learned ResNet for Real-Time Respiratory Sound Classification","authors":"Jinhai Hu;Cong Sheng Leow;Shuailin Tao;Wang Ling Goh;Yuan Gao","doi":"10.1109/TBCAS.2024.3409584","DOIUrl":"10.1109/TBCAS.2024.3409584","url":null,"abstract":"This paper presents a supervised contrastive learning (SCL) framework for respiratory sound classification and the hardware implementation of learned ResNet on field programmable gate array (FPGA) for real-time monitoring. At the algorithmic level, multiple techniques such as features augmentation and MixUp are combined holistically to mitigate the impact of data scarcity and imbalanced classes in the training dataset. Bayesian optimization further enhances the classification accuracy through parameter tuning in pre-processing and SCL. The proposed framework achieves 0.8725 total score (including runtime score) on a ResNet-18 model in both event and record multi-class classification tasks using the SJTU Paediatric Respiratory Sound Database (SPRSound). In addition, algorithm-hardware co-optimizations including Quantization-Aware Training (QAT), merge of network layers, optimization of memory size and number of parallel threads are performed for hardware implementation on FPGA. This approach reduces 40% model size and 70% computation latency. The learned ResNet is implemented on a Xilinx Zynq ZCU102 FPGA with 16ms latency and less than 2% inference score degradation compared to the software model.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 1","pages":"185-195"},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141263563","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 1.11 mm2 IVUS SoC With $pm 50^{circ}$-Range Plane Wave Transmit Beamforming at 40 MHz 1.11 平方毫米 IVUS 系统芯片，采用 40 兆赫 ±50° 范围平面波发射波束成形技术。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-06-04 DOI: 10.1109/TBCAS.2024.3409162

Xitie Zhang;Evren F. Arkan;Coskun Tekes;M. Sait Kilinc;Tzu-Han Wang;F. Levent Degertekin;Shaolan Li

{"title":"A 1.11 mm2 IVUS SoC With $pm 50^{circ}$-Range Plane Wave Transmit Beamforming at 40 MHz","authors":"Xitie Zhang;Evren F. Arkan;Coskun Tekes;M. Sait Kilinc;Tzu-Han Wang;F. Levent Degertekin;Shaolan Li","doi":"10.1109/TBCAS.2024.3409162","DOIUrl":"10.1109/TBCAS.2024.3409162","url":null,"abstract":"Intravascular ultrasound (IVUS) imaging catheters are significant tools for cardiovascular interventions, and their use can be expanded by realizing IVUS imaging guidewires and microcatheters. The miniaturization of these devices creates challenges in SNR due to the need for higher frequencies to provide adequate resolution. An integrated IVUS system with transmit beamforming can mitigate these limitations. This work presents the first practical highly integrated system-on-a-chip (SoC) with plane wave transmit beamforming at 40 MHz for IVUS on guidewire or microcatheters. The front-end circuitry has a 20-channel ultrasound transmitter (Tx) and receiver (Rx) array interfaced with a capacitive micromachined ultrasound transducer (CMUT) array. During each firing, all 20 Tx are excited with the same analog delay with respect to each other, which can be continuously adjusted between <inline-formula><tex-math>$sim$</tex-math></inline-formula>0 and 10 ns in two directions, generating a steerable plane wave in a range of +/-50<inline-formula><tex-math>${}^{circ}$</tex-math></inline-formula> for a phased array at 40 MHz. The unit delays are generated via a voltage-controlled delay line (VCDL), which only needs two external controls, one tuning the unit delay and the other determining the steering direction. The SoC is fabricated using a 180-nm high-voltage (HV) CMOS process and features a slender active area of 0.3 mm <inline-formula><tex-math>$times$</tex-math></inline-formula> 3.7 mm. The proposed SoC consumes 31.3 mW during the receiving mode. The beamformer's functionality and the SoC's overall performance were validated through acoustic characterization and imaging experiments.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 1","pages":"174-184"},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141249075","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

Energy-Efficient Spectral Analysis of ECGs on Resource Constrained IoT Devices. 在资源受限的物联网设备上对心电图进行高能效频谱分析

IEEE transactions on biomedical circuits and systems Pub Date : 2024-05-29 DOI: 10.1109/TBCAS.2024.3406520

Charalampos Eleftheriadis, Georgios Karakonstantis

{"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":"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.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"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}

引用次数: 0