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

Iniguez-Lomeli, Francisco Javier; Bornat, Yannick; Renaud, Sylvie; Barrón-Zambrano, José Hugo; Rostro-González, Horacio

doi:10.1007/s00521-021-05853-7

Cited by 4 publications

(6 citation statements)

References 24 publications

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“…For peripheral nervous system recordings, discrimination and identification of APs cannot lack spike sorting either (Metcalfe et al, 2021 ). Spike sorting is required to isolate retinal ganglion cells based on their multiunit activity recordings (Tsai et al, 2017 ; Pérez-Ortega et al, 2021 ) and identify their electrical responses (Li et al, 2021 ), but non-neural tissues can also benefit from it by applying spike sorting algorithms, i.e., on pancreatic biosignals (Iniguez-Lomeli et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

Bod

Rokai

Meszéna

et al. 2022

Front. Neuroinform.

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The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.

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Section: Discussionmentioning

confidence: 99%

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

Bod

Rokai

Meszéna

et al. 2022

Front. Neuroinform.

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“…In this context, the current study extends the prior research undertaken by the same research group. In a previous endeavor detailed in [25], our group developed a real-time action potential detection hardware implementation for the two distinct biosignals used in this work, a macaque monkey signal and a human pancreatic signal. In that study, the research team successfully devised an FPGA implementation of an adaptive threshold method tailored for detecting action potentials (spikes).…”

Section: Methodsmentioning

confidence: 99%

“…Even those employing PCA and K-means do so in a manner distinct from the approach proposed in this study. Specifically, our approach involves an initial step of utilizing spike detections derived from a prior research endeavor conducted by the research group and documented in [25]. This antecedent work employed an adaptive threshold method for real-time action potential detection.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Exploration of Unsupervised Classification in Spike Sorting: A Case Study on Macaque Monkey and Human Pancreatic Signals

Iñiguez-Lomeli,

Franco-Ortiz,

Gonzalez-Acosta

et al. 2024

Algorithms

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Spike sorting, an indispensable process in the analysis of neural biosignals, aims to segregate individual action potentials from mixed recordings. This study delves into a comprehensive investigation of diverse unsupervised classification algorithms, some of which, to the best of our knowledge, have not previously been used for spike sorting. The methods encompass Principal Component Analysis (PCA), K-means, Self-Organizing Maps (SOMs), and hierarchical clustering. The research draws insights from both macaque monkey and human pancreatic signals, providing a holistic evaluation across species. Our research has focused on the utilization of the aforementioned methods for the sorting of 327 detected spikes within an in vivo signal of a macaque monkey, as well as 386 detected spikes within an in vitro signal of a human pancreas. This classification process was carried out by extracting statistical features from these spikes. We initiated our analysis with K-means, employing both unmodified and normalized versions of the features. To enhance the performance of this algorithm, we also employed Principal Component Analysis (PCA) to reduce the dimensionality of the data, thereby leading to more distinct groupings as identified by the K-means algorithm. Furthermore, two additional techniques, namely hierarchical clustering and Self-Organizing Maps, have also undergone exploration and have demonstrated favorable outcomes for both signal types. Across all scenarios, a consistent observation emerged: the identification of six distinctive groups of spikes, each characterized by distinct shapes, within both signal sets. In this regard, we meticulously present and thoroughly analyze the experimental outcomes yielded by each of the employed algorithms. This comprehensive presentation and discussion encapsulate the nuances, patterns, and insights uncovered by these algorithms across our data. By delving into the specifics of these results, we aim to provide a nuanced understanding of the efficacy and performance of each algorithm in the context of spike sorting.

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“…Iniguez-Lomeli et al [ 14 ] implemented a FPGA-based real-time detection and sorting system, specifically designed for bio-signals classification. Their classifier relies on a pulse-shape recognition algorithm based on correlation.…”

Section: Related Workmentioning

confidence: 99%

A Simplified Correlation Index for Fast Real-Time Pulse Shape Recognition

Cicuttin

Morales

Crespo

et al. 2022

Sensors

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A simplified correlation index is proposed to be used in real-time pulse shape recognition systems. This index is similar to the classic Pearson’s correlation coefficient, but it can be efficiently implemented in FPGA devices with far fewer logic resources and excellent performance. Numerical simulations with synthetic data and comparisons with the Pearson’s correlation show the suitability of the proposed index in applications such as the discrimination and counting of pulses with a predefined shape. Superior performance is evident in signal-to-noise ratio scenarios close to unity. FPGA implementation of Person’s method and the proposed correlation index have been successfully tested and the main results are summarized.

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

Cited by 4 publications

References 24 publications

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

A Comprehensive Exploration of Unsupervised Classification in Spike Sorting: A Case Study on Macaque Monkey and Human Pancreatic Signals

A Simplified Correlation Index for Fast Real-Time Pulse Shape Recognition

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