Filters








8 Hits in 10.6 sec

Ultra Low-Power and Real-time ECG Classification Based on STDP and R-STDP Neural Networks for Wearable Devices [article]

Alireza Amirshahi, Matin Hashemi
2019 arXiv   pre-print
This paper presents a novel ECG classification algorithm for real-time cardiac monitoring on ultra low-power wearable devices.  ...  The proposed solution is based on spiking neural networks which are the third generation of neural networks.  ...  This paper proposes a novel SNN-based ECG classification algorithm for real-time operation on ultra low-power wearable devices. The overall view of the proposed solution is shown in Fig. 1 .  ... 
arXiv:1905.02954v3 fatcat:wp6obeqvuzfz7aqdi3cyyzkcce

Table of Contents

2019 IEEE Transactions on Biomedical Circuits and Systems  
Chen 1471 ECG Classification Algorithm Based on STDP and R-STDP Neural Networks for Real-Time Monitoring on Ultra Low-Power Personal Wearable Devices . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Bai 1563 Real-Time Ultra-Low Power ECG Anomaly Detection Using an Event-Driven Neuromorphic Processor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tbcas.2019.2961550 fatcat:hqa2tvaic5dfzeybrqz677nkiu

2020 Index IEEE Transactions on Biomedical Circuits and Systems Vol. 14

2020 IEEE Transactions on Biomedical Circuits and Systems  
., 1333-1345 J Jafari, R., see  ...  Fiorelli, R., +, TBCAS June 2020 606-619 ECG Authentication Hardware Design With Low-Power Signal Processing and Neural Network Optimization With Low Precision and Structured Compression.  ...  ., +, TBCAS Aug. 2020 681-691 ECG Authentication Hardware Design With Low-Power Signal Processing and Neural Network Optimization With Low Precision and Structured Compression.  ... 
doi:10.1109/tbcas.2021.3059009 fatcat:rthgi6nrxzeaxkkwqwx753nrze

MSPAN: A Memristive Spike-Based Computing Engine With Adaptive Neuron for Edge Arrhythmia Detection

Jingwen Jiang, Fengshi Tian, Jinhao Liang, Ziyang Shen, Yirui Liu, Jiapei Zheng, Hui Wu, Zhiyuan Zhang, Chaoming Fang, Yifan Zhao, Jiahe Shi, Xiaoyong Xue (+1 others)
2021 Frontiers in Neuroscience  
A multi-layer deep integrative spiking neural network (DiSNN) is first designed with an accuracy of 93.6% in 4-class ECG classification tasks.  ...  By evaluation, the overall system achieves an accuracy of over 92.25% on the MIT-BIH dataset while the area is 3.438 mm2 and the power consumption is 0.178 μJ per heartbeat at a clock frequency of 500  ...  ., and Hashemi, M. (2019). ECG classification algorithm based on STDP and R-STDP neural networks for real-time monitoring on ultra low-power personal wearable devices. IEEE Trans. Biomed.  ... 
doi:10.3389/fnins.2021.761127 pmid:34975373 pmcid:PMC8715923 fatcat:tkgcnfj355ffbdihyvqmwtahzm

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

Mostafa Rahimiazghadi, Corey Lammie, Jason Kamranr Eshraghian, Melika Payvand, Elisa Donati, Bernabe Linares-Barranco, Giacomo Indiveri
2020 IEEE Transactions on Biomedical Circuits and Systems  
With the advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors, new opportunities are emerging for applying deep and Spiking Neural Network (SNN) algorithms to healthcare and  ...  After providing the required background, we unify the sparsely distributed research on neural network and neuromorphic hardware implementations as applied to the healthcare domain.  ...  Although low-power consumption and affordable cost are two key factors for almost any edge-computing or near-sensor device, these are even more important for biomedical devices such as wearables, health-monitoring  ... 
doi:10.1109/tbcas.2020.3036081 pmid:33156792 fatcat:rjwfjd7vmvglpk762mqeyiteqq

2022 Roadmap on Neuromorphic Computing and Engineering [article]

Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano (+47 others)
2022 arXiv   pre-print
Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically  ...  technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics.  ...  This new class of extremely low-power and lowlatency artificial intelligence systems could, In a world where power-hungry deep learning techniques are becoming a commodity, and at the same time, environmental  ... 
arXiv:2105.05956v3 fatcat:pqir5infojfpvdzdwgmwdhsdi4

Novel Concepts for Organic Transistors: Physics, Device Design, and Applications [article]

Hans Kleemann
2021 arXiv   pre-print
In particular, the combination of OECTs acting as sensor units and self-learning neural networks at once enables the development of intelligent tags for medical applications.  ...  In this context, mixed ionic-electronic conductors and organic electro-chemical transistors (OECTs) are identified as highly promising approaches for electronic bio-interfaces enabling ultra-sensitive  ...  Thanks also to Shen and Jonas for their work on the organic photodetectors. Furthermore, I am grateful to Micha and Felix for proof-reading.  ... 
arXiv:2111.09430v1 fatcat:hemjnbscwvfo7l3i33f7se3gru

2022 roadmap on neuromorphic computing and engineering

Dennis Valbjørn Christensen, Regina Dittmann, Bernabé Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano (+12 others)
2022
Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically  ...  technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics.  ...  Acknowledgements The author thanks the CNRS for support. Acknowledgements The author would like to thank E Donati for fun and insightful discussions and brainstorming on the topic.  ... 
doi:10.3929/ethz-b-000529282 fatcat:x5s5lalqqbbn7fb6gmpqmbqkxu