Key result
The proposed arrhythmia monitoring system successfully acquired and classified ECG signals in a human study of 8 patients, with algorithm accuracy of 99.4% for beat detection on database testing.
Why the study?
Does the proposed arrhythmia monitoring system accurately acquire and classify ECG signals?
Does the proposed arrhythmia monitoring system accurately acquire and classify ECG signals?
A novel, low-power arrhythmia monitoring system with a smartphone interface demonstrated high accuracy for beat detection and classification in database testing and feasibility in a small human pilot study.
Small human study supports technical feasibility; leaves open larger validation before clinical adoption.
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 μm 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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Lee et al. (2018) studied Arrhythmia (n=8). Arrhythmia monitoring system was evaluated on Beat detection and arrhythmia classification accuracy. The proposed arrhythmia monitoring system successfully acquired and classified ECG signals in a human study of 8 patients, with algorithm accuracy of 99.4% for beat detection on database testing.
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