Artificial neural network (ANN) and its variants are favored algorithm in designing cardiac arrhythmia classifier (CAC) for its high accuracy. However, the implementation of ultralow power ANN-CAC is challenging due to the intensive computations. Moreover, the imbalanced MIT-BIH database limits the ANN-CAC performance. Several novel techniques are proposed to address the challenges in the low power implementation. Firstly, continuous-in-time discrete-in-amplitude (CTDA) signal flow is adopted to reduce the multiplication operations. Secondly, conditional grouping scheme (CGS) in combination with biased training (BT) is proposed to handle the imbalanced training samples for better training convergency and evaluation accuracy. Thirdly, arithmetic unit sharing with customized high-performance multiplier improves the power efficiency. Verified in FPGA and synthesized in 0.18 μm CMOS process, the proposed CTDA ANN-CAC can classify an arrhythmia within 252 μs at 25 MHz clock frequency with average power of 13.34 μW for 75bpm heart rate. Evaluated on MIT-BIH database, it shows over 98% classification accuracy, 97% sensitivity, and 94% positive predictivity.

中文翻译：

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用于可穿戴式ECG传感器的事件驱动的特定患者ANN心律失常分类器（13.34μW）。

人工神经网络（ANN）及其变体因其准确性高而在设计心律不齐分类器（CAC）中受到青睐。然而，由于需要大量的计算，因此超低功耗ANN-CAC的实施具有挑战性。此外，不平衡的MIT-BIH数据库限制了ANN-CAC的性能。提出了几种新颖的技术来解决低功耗实施中的挑战。首先，采用连续时间幅度离散（CTDA）信号流来减少乘法运算。其次，提出了有条件分组方案（CGS）结合有偏训练（BT）来处理不均衡的训练样本，以提高训练的收敛性和评估准确性。第三，与定制的高性能乘法器共享算术单元可提高电源效率。拟议的CTDA ANN-CAC经过FPGA验证并以0.18μmCMOS工艺合成，可以在25 MHz时钟频率下以252 bs的心律失常分类，对于75bpm心率，平均功率为13.34μW。在MIT-BIH数据库上进行评估，它显示了超过98％的分类准确性，97％的敏感性和94％的阳性预测性。