Gagnon-Turcotte G, Sawan M, Gosselin B
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:2167-70. doi: 10.1109/EMBC.2015.7318819.
This paper presents a resources-optimized digital action potential (AP) detector featuring an adaptive threshold based on a new Sigma-delta control loop. The proposed AP detector is optimized for utilizing low hardware resources, which makes it suitable for implementation on most popular low-power microcontrollers units (MCU). The adaptive threshold is calculated using a digital control loop based on a Sigma-delta modulator that precisely estimates the standard deviation of the amplitude of the neuronal signal. The detector was implemented on a popular low-power MCU and fully characterized experimentally using previously recorded neural signals with different signal-to-noise ratios. A comparison of the obtained results with other thresholding approaches shows that the proposed method can compete with high performance and highly resources demanding spike detection approaches while achieving up to 100% of true positive detection rate at high SNR, and up to 63% for an SNR as low as 0 dB, while necessitating an execution time as low as 11 μs with the MCU operating at 8 MHz.
本文提出了一种资源优化的数字动作电位（AP）检测器，其具有基于新型Sigma-delta控制回路的自适应阈值。所提出的AP检测器针对低硬件资源的利用进行了优化，这使其适用于在最流行的低功耗微控制器单元（MCU）上实现。自适应阈值是使用基于Sigma-delta调制器的数字控制回路来计算的，该调制器精确估计神经元信号幅度的标准偏差。该检测器在一种流行的低功耗MCU上实现，并使用先前记录的具有不同信噪比的神经信号进行了全面的实验表征。将所得结果与其他阈值处理方法进行比较表明，所提出的方法可以与高性能且资源需求高的尖峰检测方法相竞争，在高信噪比下实现高达100%的真阳性检测率，并在低至0 dB的信噪比下达到63%，同时在MCU以8 MHz运行时所需的执行时间低至11微秒。
