It is difficult for the existing Automated External Defibrillator (AED) on-board microprocessors to accurately classify electrocardiographic signals (ECGs) mixed with Cardiopulmonary Resuscitation artifacts in real-time. In order to improve recognition speed and accuracy of electrocardiographic signals containing Cardiopulmonary Resuscitation artifacts, a new special coprocessor system-on-chip (SoC) for defibrillators was designed. In this study, a microprocessor was designed based on the RISC-V architecture to achieve hardware acceleration for ECGs classification; Besides, an Approximate Entropy (ApEn) and Convolutional neural networks (CNNs) integrated algorithm capable of running on it was designed. The algorithm differs from traditional electrocardiographic (ECG) classification algorithms. It can be used to perform ECG classification while chest compressions are applied. The proposed co-processor can be used to accelerate computation rate of ApEn by 34 times compared with pure software computation. It can also be used to accelerate the speed of CNNs ECG recognition by 33 times. The combined algorithm was used to classify ECGs with CPR artifacts. It achieved a precision of 96%, which was significantly superior to that of simple CNNs. The coprocessor can be used to significantly improve the recognition efficiency and accuracy of ECGs containing CPR artifacts. It is suitable for automatic external defibrillator and other medical devices in which one-dimensional physiological signals.
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