Extraction of ECG features with spiking neurons for decreased power consumption in embedded devices

Zonglong Li; Laurie E. Calvet

doi:10.1109/SMACD58065.2023.10192147

Extraction of ECG features with spiking neurons for decreased power consumption in embedded devices

Zonglong Li
, Laurie E. Calvet

Centre de Nanosciences et de Nanotechnologies

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

In recent years, the computational efficiency of spike-based biomimetic information processing has received increasing interest. Here we show by simulation how two spiking neurons with different thresholds can be used to extract ECG features. One high-threshold neuron detects the location of the heartbeat, and the other low-threshold neuron detects other small-magnitude features. These detected features alone can then be transmitted to a nearby computer to classify the heartbeat instead of the entire ECG signal. Reducing transferred data by about 50 times, minimizing energy consumption and thus potentially extending the continuous use time for health monitoring applications. We show that a KNN algorithm classifies the heartbeat based on the obtained features with an overall accuracy of 96%, proving our method's feasibility.

langue originale	Anglais
titre	Proceedings - 2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023
Editeur	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronique)	9798350332650
Les DOIs	https://doi.org/10.1109/SMACD58065.2023.10192147
état	Publié - 1 janv. 2023
Evénement	19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023 - Funchal, Portugal Durée: 3 juil. 2023 → 5 juil. 2023

Série de publications

Nom	Proceedings - 2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023

Une conférence

Une conférence	19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023
Pays/Territoire	Portugal
La ville	Funchal
période	3/07/23 → 5/07/23

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10.1109/SMACD58065.2023.10192147

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Li, Z., & Calvet, L. E. (2023). Extraction of ECG features with spiking neurons for decreased power consumption in embedded devices. Dans Proceedings - 2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023 (Proceedings - 2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMACD58065.2023.10192147

Li, Zonglong ; Calvet, Laurie E. / Extraction of ECG features with spiking neurons for decreased power consumption in embedded devices. Proceedings - 2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings - 2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023).

@inproceedings{d463831ae83246c1afd5ed8c975e3b45,

title = "Extraction of ECG features with spiking neurons for decreased power consumption in embedded devices",

abstract = "In recent years, the computational efficiency of spike-based biomimetic information processing has received increasing interest. Here we show by simulation how two spiking neurons with different thresholds can be used to extract ECG features. One high-threshold neuron detects the location of the heartbeat, and the other low-threshold neuron detects other small-magnitude features. These detected features alone can then be transmitted to a nearby computer to classify the heartbeat instead of the entire ECG signal. Reducing transferred data by about 50 times, minimizing energy consumption and thus potentially extending the continuous use time for health monitoring applications. We show that a KNN algorithm classifies the heartbeat based on the obtained features with an overall accuracy of 96\%, proving our method's feasibility.",

keywords = "ECG, data transmission reduction, health monitoring equipment, spike-based biomimetic information processing, spiking neuron circuit",

author = "Zonglong Li and Calvet, \{Laurie E.\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023 ; Conference date: 03-07-2023 Through 05-07-2023",

year = "2023",

month = jan,

day = "1",

doi = "10.1109/SMACD58065.2023.10192147",

language = "English",

series = "Proceedings - 2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Li, Z & Calvet, LE 2023, Extraction of ECG features with spiking neurons for decreased power consumption in embedded devices. Dans Proceedings - 2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023. Proceedings - 2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023, Institute of Electrical and Electronics Engineers Inc., 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023, Funchal, Portugal, 3/07/23. https://doi.org/10.1109/SMACD58065.2023.10192147

Extraction of ECG features with spiking neurons for decreased power consumption in embedded devices. / Li, Zonglong; Calvet, Laurie E.
Proceedings - 2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings - 2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Extraction of ECG features with spiking neurons for decreased power consumption in embedded devices

AU - Li, Zonglong

AU - Calvet, Laurie E.

PY - 2023/1/1

Y1 - 2023/1/1

N2 - In recent years, the computational efficiency of spike-based biomimetic information processing has received increasing interest. Here we show by simulation how two spiking neurons with different thresholds can be used to extract ECG features. One high-threshold neuron detects the location of the heartbeat, and the other low-threshold neuron detects other small-magnitude features. These detected features alone can then be transmitted to a nearby computer to classify the heartbeat instead of the entire ECG signal. Reducing transferred data by about 50 times, minimizing energy consumption and thus potentially extending the continuous use time for health monitoring applications. We show that a KNN algorithm classifies the heartbeat based on the obtained features with an overall accuracy of 96%, proving our method's feasibility.

AB - In recent years, the computational efficiency of spike-based biomimetic information processing has received increasing interest. Here we show by simulation how two spiking neurons with different thresholds can be used to extract ECG features. One high-threshold neuron detects the location of the heartbeat, and the other low-threshold neuron detects other small-magnitude features. These detected features alone can then be transmitted to a nearby computer to classify the heartbeat instead of the entire ECG signal. Reducing transferred data by about 50 times, minimizing energy consumption and thus potentially extending the continuous use time for health monitoring applications. We show that a KNN algorithm classifies the heartbeat based on the obtained features with an overall accuracy of 96%, proving our method's feasibility.

KW - ECG

KW - data transmission reduction

KW - health monitoring equipment

KW - spike-based biomimetic information processing

KW - spiking neuron circuit

UR - https://www.scopus.com/pages/publications/85168685632

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DO - 10.1109/SMACD58065.2023.10192147

M3 - Conference contribution

AN - SCOPUS:85168685632

T3 - Proceedings - 2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023

BT - Proceedings - 2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023

Y2 - 3 July 2023 through 5 July 2023

ER -

Li Z, Calvet LE. Extraction of ECG features with spiking neurons for decreased power consumption in embedded devices. Dans Proceedings - 2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings - 2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023). doi: 10.1109/SMACD58065.2023.10192147

Extraction of ECG features with spiking neurons for decreased power consumption in embedded devices

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