A real-time FPGA-based implementation for detection and sorting of bio-signals

Extracting and analyzing relevant information from bio-signal recordings are complex tasks in which action potential detection and sorting processes take place, moreover if these are performed in real time. In this regard, the present paper introduces real-time FPGA-based architectures for detection and sorting of bio-signals, in particular macaque and human pancreatic signals. Action potential detection is performed by using an adaptive threshold. Also, during this process we have identified six different action potential shapes from the signals, which have been used to classify the action potentials. Our implementation runs at a frequency of 100 MHz with a low resource consumption for both architectures, and action potentials can be also observed in real time during a simulation in an OLED display.

[1]  S. Mukhopadhyay,et al.  A new interpretation of nonlinear energy operator and its efficacy in spike detection , 1998, IEEE Transactions on Biomedical Engineering.

[2]  Yannick Bornat,et al.  A versatile electrode sorting module for MEAs: Implementation in a FPGA-based real-time system , 2017, 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[3]  Matthias Heinig,et al.  New Insights into the Genetic Control of Gene Expression using a Bayesian Multi-tissue Approach , 2010, PLoS Comput. Biol..

[4]  Andrew J. Mason,et al.  Hardware Efficient Automatic Thresholding for NEO-Based Neural Spike Detection , 2017, IEEE Transactions on Biomedical Engineering.

[5]  Taejeong Kim,et al.  A new action potential detector using the MTEO and its effects on spike sorting systems at low signal-to-noise ratios , 2006, IEEE Transactions on Biomedical Engineering.

[6]  Reid R. Harrison,et al.  A low-power integrated circuit for adaptive detection of action potentials in noisy signals , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[7]  Ran Ginosar,et al.  An Integrated System for Multichannel Neuronal Recording With Spike/LFP Separation, Integrated A/D Conversion and Threshold Detection , 2007, IEEE Trans. Biomed. Eng..

[8]  Douglas J. Bakkum,et al.  Revealing neuronal function through microelectrode array recordings , 2015, Front. Neurosci..

[9]  Xiamu Niu,et al.  2-D Cartoon Character Detection based on Scalable-Shape Context and Hough Voting , 2013 .

[10]  Dejan Markovic,et al.  Technology-Aware Algorithm Design for Neural Spike Detection, Feature Extraction, and Dimensionality Reduction , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  Romain Brette,et al.  A Threshold Equation for Action Potential Initiation , 2010, PLoS Comput. Biol..

[12]  Robert Mullins,et al.  On the Reduction of Computational Complexity of Deep Convolutional Neural Networks † , 2018, Entropy.

[13]  Timothy G. Constandinou,et al.  A 1.5 μW NEO-based spike detector with adaptive-threshold for calibration-free multichannel neural interfaces , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[14]  Khan Muhammad,et al.  A Novel Deep Transfer Learning Method for Detection of Myocardial Infarction , 2019, ArXiv.

[15]  Xin He,et al.  Saliency detection based on integrated features , 2014, Neurocomputing.

[16]  Jian Xu,et al.  A 16-Channel Nonparametric Spike Detection ASIC Based on EC-PC Decomposition , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[17]  Benjamin C. Lee,et al.  MAPS: Understanding Metadata Access Patterns in Secure Memory , 2018, 2018 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS).

[18]  Konstantinos Poularakis,et al.  SDN Controller Placement at the Edge: Optimizing Delay and Overheads , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[19]  Saeid Sanei,et al.  Extracellular spike detection from multiple electrode array using novel intelligent filter and ensemble fuzzy decision making , 2015, Journal of Neuroscience Methods.

[20]  Arthur Gretton,et al.  Low-Frequency Local Field Potentials and Spikes in Primary Visual Cortex Convey Independent Visual Information , 2008, The Journal of Neuroscience.

[21]  Noëlle Lewis,et al.  CMOS differential neural amplifier with high input impedance , 2015, 2015 IEEE 13th International New Circuits and Systems Conference (NEWCAS).

[22]  Tomoki Fukai,et al.  Spike detection from noisy neural data in linear‐probe recordings , 2014, The European journal of neuroscience.

[23]  Yannick Bornat,et al.  Slow potentials encode intercellular coupling and insulin demand in pancreatic beta cells , 2015, Diabetologia.