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 -