TY - JOUR
T1 - Development of an Arrhythmia Monitoring System and Human Study
AU - Lee, Shuenn Yuh
AU - Huang, Peng Wei
AU - Liang, Ming Chun
AU - Hong, Jia Hua
AU - Chen, Ju Yi
N1 - Funding Information:
Manuscript received June 22, 2018; revised August 25, 2018 and October 4, 2018; accepted October 8, 2018. Date of publication October 12, 2018; date of current version December 4, 2018. This work was supported in part by the Chip Implementation Center and in part by the Ministry of Science and Technology (MOST), Taiwan, under Grant MOST 106-2314-B-006-001, 107-2218-E-006-034 and Grant MOST 107-2622-8-006-009-TE2. (Corresponding author: Shuenn-Yuh Lee.) S.-Y. Lee, P.-W. Huang, M.-C. Liang, and J.-H. Hong are with the Electrical Engineering Department, National Cheng Kung University, Tainan 70101, Taiwan (e-mail: ieesyl@mail.ncku.edu.tw; lookonthebrightside_j@hotmail.com; mcliang@cbic.ee.ncku.edu.tw; deo@nwd.com.tw).
Publisher Copyright:
© 1975-2011 IEEE.
PY - 2018/11
Y1 - 2018/11
N2 - Electrocardiography (ECG) is a fundamental method not only commonly used in the hospital for clinical requirement but also widely adopted in home and personal healthcare systems to obtain the electrical activity of the heart. An arrhythmia monitoring system is proposed and used in a clinical trial. The proposed system has three parts. The first is a high-resolution, low-power analog front-end circuit for implementing bio-signal sensing circuits. This part is developed with a chopper-based pre-amplifier and a high-pass sigma-delta modulator. The features of the circuits are low complexity, high resolution, and low power consumption. The second part is a digital signal processor with a decimation filter and a universal asynchronous receiver/transmitter package generator. The last part is used to realize a software interface on smartphone for ECG signal recording, display, and classification. A wavelet-based classification method is also proposed to classify the rhythm. The chip used in the system is fabricated through the 0.18 $\boldsymbol {\mu }\text{m}$ standard complementary metal-oxide-semiconductor process, and the operation voltage is 1.2 V. The classification algorithm is verified with data from the MIT/BIH arrhythmia database. The accuracy of beat detection and arrhythmia classification is 99.4% and 95.83%, respectively. Eight patients are enrolled in a human study to verify the performance of the proposed arrhythmia monitoring system. Results show that the system can acquire and classify ECG signals.
AB - Electrocardiography (ECG) is a fundamental method not only commonly used in the hospital for clinical requirement but also widely adopted in home and personal healthcare systems to obtain the electrical activity of the heart. An arrhythmia monitoring system is proposed and used in a clinical trial. The proposed system has three parts. The first is a high-resolution, low-power analog front-end circuit for implementing bio-signal sensing circuits. This part is developed with a chopper-based pre-amplifier and a high-pass sigma-delta modulator. The features of the circuits are low complexity, high resolution, and low power consumption. The second part is a digital signal processor with a decimation filter and a universal asynchronous receiver/transmitter package generator. The last part is used to realize a software interface on smartphone for ECG signal recording, display, and classification. A wavelet-based classification method is also proposed to classify the rhythm. The chip used in the system is fabricated through the 0.18 $\boldsymbol {\mu }\text{m}$ standard complementary metal-oxide-semiconductor process, and the operation voltage is 1.2 V. The classification algorithm is verified with data from the MIT/BIH arrhythmia database. The accuracy of beat detection and arrhythmia classification is 99.4% and 95.83%, respectively. Eight patients are enrolled in a human study to verify the performance of the proposed arrhythmia monitoring system. Results show that the system can acquire and classify ECG signals.
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U2 - 10.1109/TCE.2018.2875799
DO - 10.1109/TCE.2018.2875799
M3 - Article
AN - SCOPUS:85055035663
SN - 0098-3063
VL - 64
SP - 442
EP - 451
JO - IEEE Transactions on Consumer Electronics
JF - IEEE Transactions on Consumer Electronics
IS - 4
M1 - 8490696
ER -