Proceedings ArticleDOI
ECG-based Heartbeat Classification in Neuromorphic Hardware
Federico Corradi,Sandeep Pande,Jan Stuijt,Ning Qiao,Siebren Schaafsma,Giacomo Indiveri,Francky Catthoor +6 more
- pp 1-8
TLDR
A hardware setup is proposed that enables the always-on monitoring of ECG signals into wearables and shows an overall classification accuracy of 95% on the PhysioNet Arrhythmia Database provided by the Massachusetts Institute of Technology and Beth Israel Hospital.Abstract:
Heart activity can be monitored by means of ElectroCardioGram (ECG) measure which is widely used to detect heart diseases due to its non-invasive nature. Trained cardiologists can detect anomalies by visual inspecting recordings of the ECG signals. However, arrhythmias occur intermittently especially in early stages and therefore they can be missed in routine check recordings. We propose a hardware setup that enables the always-on monitoring of ECG signals into wearables. The system exploits a fully event-driven approach for carrying arrhythmia detection and classification employing a bio-inspired spiking neural network. The two staged Spiking Neural Network (SNN) topology comprises a recurrent network of spiking neurons whose output is classified by a cluster of Leaky integrate-and-fire (LIF) neurons that have been supervisely trained to distinguish 17 types of cardiac patterns. We introduce a method for compressing ECG signals into a stream of asynchronous digital events that are used to stimulate the recurrent SNN. Using ablative analysis, we demonstrate the impact of the recurrent SNN and we show an overall classification accuracy of 95% on the PhysioNet Arrhythmia Database provided by the Massachusetts Institute of Technology and Beth Israel Hospital (MIT/BIH). The proposed system has been implemented on an event-driven mixed-signal analog/digital neuromorphic processor. This work contributes to the realization of an energy-efficient, wearable, and accurate multi-class ECG classification system.read more
Citations
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Journal ArticleDOI
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
Mostafa Rahimi Azghadi,Corey Lammie,Jason K. Eshraghian,Melika Payvand,Elisa Donati,Bernabe Linares-Barranco,Giacomo Indiveri +6 more
TL;DR: In this article, the authors provide a tutorial describing how various technologies including emerging memristive devices, Field Programmable Gate Arrays (FPGAs), and Complementary Metal Oxide Semiconductor (CMOS) can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare.
Journal ArticleDOI
Real-time ultra-low power ECG anomaly detection using an event-driven neuromorphic processor
TL;DR: This work proposes a compact and sub-mW low power neural processing system that can be used to perform on-line and real-time preliminary diagnosis of pathological conditions, to raise warnings for the existence of possible pathological conditions or to trigger an off-line data recording system for further analysis by a medical professional.
Journal ArticleDOI
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
Mostafa Rahimi Azghadi,Corey Lammie,Jason K. Eshraghian,Melika Payvand,Elisa Donati,Bernabe Linares-Barranco,Giacomo Indiveri +6 more
TL;DR: A tutorial describing how various technologies including emerging memristive devices, Field Programmable Gate Arrays (FPGAs), and Complementary Metal Oxide Semiconductor (CMOS) can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare.
Journal ArticleDOI
An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG.
TL;DR: In this article, the authors presented a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing intracranial EEG (iEEG) from epilepsy patients for the detection of high frequency oscillations (HFO), which are a biomarker for epileptogenic brain tissue.
Journal ArticleDOI
Adaptive Extreme Edge Computing for Wearable Devices.
TL;DR: In this article, the authors provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era, and evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size.
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Journal ArticleDOI
Loihi: A Neuromorphic Manycore Processor with On-Chip Learning
Michael Davies,Narayan Srinivasa,Tsung-Han Lin,Gautham N. Chinya,Cao Yongqiang,Sri Harsha Choday,Georgios D. Dimou,Prasad Joshi,Nabil Imam,Shweta Jain,Yuyun Liao,Chit-Kwan Lin,Andrew Lines,Ruokun Liu,Deepak A. Mathaikutty,Steven McCoy,Arnab Paul,Jonathan Tse,Guruguhanathan Venkataramanan,Yi-Hsin Weng,Andreas Wild,Yoon Seok Yang,Hong Wang +22 more
TL;DR: Loihi is a 60-mm2 chip fabricated in Intels 14-nm process that advances the state-of-the-art modeling of spiking neural networks in silicon, and can solve LASSO optimization problems with over three orders of magnitude superior energy-delay-product compared to conventional solvers running on a CPU iso-process/voltage/area.
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