Abstract
In this study, a very-large-scale integration implementation of a convolutional neural network (CNN) inference for abnormal heartbeat detection was proposed. Four-lead electrocardiogram signals were used to detect abnormal heartbeat conditions, such as premature ventricular complex. 1D CNNs and fully connected layers were utilised in the proposed chip to achieve high-speed, small-area, and high-accuracy arrhythmia detection. The proposed chip was implemented using a 90-nm complementary metal-oxide-semiconductor process and operated at 125 MHz with a 0.67mm2 core area. The power consumption was 4.18mW at high-speed operation frequency (125 MHz) and 3.79μW at 10 kHz for low-power applications. The detection accuracy was 95.14% based on the MIT-BIH arrhythmia database. Consequently, the properties of high speed, low power, small area, and high accuracy were established in the proposed accelerator chip.
Original language | English |
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Pages (from-to) | 330-331 |
Number of pages | 2 |
Journal | Electronics Letters |
Volume | 56 |
Issue number | 7 |
DOIs | |
State | Published - 30 03 2020 |
Bibliographical note
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