An Automatic Action Potential Detector for Neural Recording Implants
- 71 Downloads
In this paper, a low-power CMOS analog automatic action potential (AP) detector is proposed for wireless neural recording implants. The proposed AP detector is based on comparing the neural input signal with an analog threshold level. The threshold level is obtained by calculating the root mean square value of the neural input signal. In order to generate the threshold voltage level, the AP detector incorporates a continuous-time (CT) sigma-delta (Σ∆) modulator in its analog signal processing section. This structure benefits from the combination of a CT Σ∆ modulator and a single-bit DAC as the multiplier to reduce the power consumption. Although in contrast to the traditional methods, the required circuits are not biased in the subthreshold region, the total power consumption is reduced. The proposed AP detector is designed in TSMC 90 nm CMOS technology and consumes 11.8 µW from a single 1-V power supply. It is worth mentioning that the utilized CT Σ∆ modulator can also be used in the analog-to-digital converter to significantly reduce both the power consumption and silicon area of the complete neural recording system.
KeywordsAction potential detection Neural recording systems CT Σ∆ modulators Root mean square (RMS) Analog-to-digital converters Threshold level CMOS technology
This work has been financially supported by Iran National Science Foundation (INSF).
- 8.G. Ferri, S. Pennisi, S. Sperandii, A low-voltage CMOS 1-Hz low-pass filter, in IEEE International Conference on Electronics, Circuits and Systems (ICECS) (1999), pp 1341–1343Google Scholar
- 12.M. Jalalifar, G.-S. Byun, An ultra-low power spike detector for implantable biomedical systems, in IEEE Wireless and Microwave Technology Conference (WAMICON)(2013), pp 1–4Google Scholar
- 13.E. Koutsos, S.E. Paraskevopoulou, T.G. Constandinou, A 1.5 μW NEO-based spike detector with adaptive-threshold for calibration-free multichannel neural interfaces, in IEEE International Symposium on Circuits and Systems (ISCAS) (2013), pp 1922–1925Google Scholar
- 15.R.M. Rangayyan, Biomedical Signal Analysis: A Case-Study Approach (Wiley, Hoboken, 2002)Google Scholar
- 18.C. Sawigun, S. Thanapitak, A 0.9-nW, 101-Hz, and 46.3-μVrms IRN low-pass filter for ECG acquisition using FVF biquads. IEEE Trans. Very Large Scale Integr (VLSI) Syst 1–9 (2018)Google Scholar
- 19.R. Schreier, G.C. Temes, Understanding Delta-Sigma Data Converters (Wiley/IEEE Press, Hoboken, 2005)Google Scholar
- 23.T. Wu, Z. Yang, A multichannel integrated circuit for neural spike detection based on EC-PC threshold estimation, in Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2013), pp 779–782Google Scholar