Spiking Reservoir Computing Model for Patient-customized ECG Monitoring
Standard
Spiking Reservoir Computing Model for Patient-customized ECG Monitoring. / Hadäghi, Fatemeh.
Bernstein Conference 2019. 2019.Research output: SCORING: Contribution to book/anthology › Conference contribution - Poster › Research
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - CHAP
T1 - Spiking Reservoir Computing Model for Patient-customized ECG Monitoring
AU - Hadäghi, Fatemeh
PY - 2019
Y1 - 2019
N2 - Echo state network (ESN) with leaky-integrated-and-fire (LIF) neurons is employed to address the classical electrocardiogram (ECG) beat classification problem. Due to its computational efficiency and the fact that training amounts to a simple linear regression, this supervised learning algorithm for recurrent neural network (RNN) has been variously considered as a strategy to implement useful computations not only on digital computers but also on emerging unconventional hardware platforms such as neuromorphic microchips. Here, this biological-inspired learning framework is exploited to devise an accurate patient-adaptive model that has the potential to be integrated in wearable cardiac events monitoring devices. The patient-customized network was trained and tested on ECG recordings from MIT-BIH arrhythmia database. The results of simulations showed a functional ESN with spiking neurons can provide accurate, cheap and fast patient-customized heartbeat classifier.
AB - Echo state network (ESN) with leaky-integrated-and-fire (LIF) neurons is employed to address the classical electrocardiogram (ECG) beat classification problem. Due to its computational efficiency and the fact that training amounts to a simple linear regression, this supervised learning algorithm for recurrent neural network (RNN) has been variously considered as a strategy to implement useful computations not only on digital computers but also on emerging unconventional hardware platforms such as neuromorphic microchips. Here, this biological-inspired learning framework is exploited to devise an accurate patient-adaptive model that has the potential to be integrated in wearable cardiac events monitoring devices. The patient-customized network was trained and tested on ECG recordings from MIT-BIH arrhythmia database. The results of simulations showed a functional ESN with spiking neurons can provide accurate, cheap and fast patient-customized heartbeat classifier.
UR - https://abstracts.g-node.org/conference/BC19/abstracts#/uuid/338ebbc5-2799-44a4-90bf-25415ed1aaec
M3 - Conference contribution - Poster
BT - Bernstein Conference 2019
ER -
