Development of an Arrhythmia Monitoring System and Human Study

• Published in: IEEE transactions on consumer electronics Vol. 64; no. 4; pp. 442 - 451
• Main Authors: Lee, Shuenn-Yuh, Huang, Peng-Wei, Liang, Ming-Chun, Hong, Jia-Hua, Chen, Ju-Yi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analog circuits; Arrhythmia; arrhythmia classification; Bio-signal acquisition; Biomedical monitoring; Biomedical signal processing; Bluetooth; Cardiac arrhythmia; Classification; Digital signal processors; electrocardiogram; Electrocardiography; Generators; High resolution; human study; IIR filters; Medical services; Microprocessors; Monitoring; Monitoring systems; Power consumption; Recording; Signal classification; Signal processing; Smartphones; Universal asynchronous receiver/transmitters; Wavelet analysis; wavelet transform
• ISSN: 0098-3063; 1558-4127
• DOI: 10.1109/TCE.2018.2875799

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 <inline-formula> <tex-math notation="LaTeX">\boldsymbol {\mu }\text{m} </tex-math></inline-formula> 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.