TY - JOUR

T1 - A Low-Cost Implementation of Sample Entropy in Wearable Embedded Systems

T2 - An Example of Online Analysis for Sleep EEG

AU - Wang, Yung Hung

AU - Chen, I. Yu

AU - Chiueh, Herming

AU - Liang, Sheng Fu

N1 - Publisher Copyright:
© 1963-2012 IEEE.

PY - 2021

Y1 - 2021

N2 - Sample entropy (SpEn) is a measure of the underlying regularity or complexity of a system that is achieved by assessing the entropy of a time series recorded from the system. It is a powerful signal processing tool and has received increasing attention in recent years. SpEn has been successfully applied in biomedical measurements and other applications. In particular, many emerging applications require measuring the SpEn of signals in real-time embedded systems. However, the standard implementation of SpEn requires a computational complexity of O(n2), where n is the data length, making it difficult to meet real-time constraints, especially for large n. Moreover, power consumption and computation latency must be considered as well. The data length used in previous studies was approximately several hundred, and it remains a challenging task to operate on longer data lengths. In this article, we propose the assisted sliding box (SBOX) algorithm to accelerate the computation of SpEn without any approximation while maintaining a low memory overhead so that the algorithm can be executed in embedded systems for edge computing. We also develop an electroencephalogram (EEG)-based wearable device for comfortable overnight recording. The SBOX algorithm is then implemented in the system to measure the online SpEn of an overnight sleep EEG signal. The results show that, compared with the standard algorithm, the SBOX algorithm speeds up the computation time by a factor of 60, thereby reducing power consumption by 98% when measuring a 30-s epoch of sleep EEG with n=7500 and a 250-Hz sampling rate.

AB - Sample entropy (SpEn) is a measure of the underlying regularity or complexity of a system that is achieved by assessing the entropy of a time series recorded from the system. It is a powerful signal processing tool and has received increasing attention in recent years. SpEn has been successfully applied in biomedical measurements and other applications. In particular, many emerging applications require measuring the SpEn of signals in real-time embedded systems. However, the standard implementation of SpEn requires a computational complexity of O(n2), where n is the data length, making it difficult to meet real-time constraints, especially for large n. Moreover, power consumption and computation latency must be considered as well. The data length used in previous studies was approximately several hundred, and it remains a challenging task to operate on longer data lengths. In this article, we propose the assisted sliding box (SBOX) algorithm to accelerate the computation of SpEn without any approximation while maintaining a low memory overhead so that the algorithm can be executed in embedded systems for edge computing. We also develop an electroencephalogram (EEG)-based wearable device for comfortable overnight recording. The SBOX algorithm is then implemented in the system to measure the online SpEn of an overnight sleep EEG signal. The results show that, compared with the standard algorithm, the SBOX algorithm speeds up the computation time by a factor of 60, thereby reducing power consumption by 98% when measuring a 30-s epoch of sleep EEG with n=7500 and a 250-Hz sampling rate.

KW - Computation time

KW - edge computing

KW - microcontroller (MCU)

KW - sample entropy (SpEn)

KW - sleep electroencephalogram (EEG)

KW - wearable device

UR - http://www.scopus.com/inward/record.url?scp=85099412595&partnerID=8YFLogxK

U2 - 10.1109/TIM.2020.3047488

DO - 10.1109/TIM.2020.3047488

M3 - 期刊論文

AN - SCOPUS:85099412595

SN - 0018-9456

VL - 70

JO - IEEE Transactions on Instrumentation and Measurement

JF - IEEE Transactions on Instrumentation and Measurement

M1 - 9312616

ER -