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WO2025175394A1 - Étalonnage de seuil adaptatif pour détection de pic - Google Patents

Étalonnage de seuil adaptatif pour détection de pic

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Publication number
WO2025175394A1
WO2025175394A1 PCT/CA2025/050221 CA2025050221W WO2025175394A1 WO 2025175394 A1 WO2025175394 A1 WO 2025175394A1 CA 2025050221 W CA2025050221 W CA 2025050221W WO 2025175394 A1 WO2025175394 A1 WO 2025175394A1
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WIPO (PCT)
Prior art keywords
signal
spikes
threshold
standard deviation
electrode
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PCT/CA2025/050221
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English (en)
Inventor
Tayebeh YOUSEFI
Hossein KASSIRI
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Individual
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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/388—Nerve conduction study, e.g. detecting action potential of peripheral nerves
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7225—Details of analogue processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation

Definitions

  • the embodiments disclosed herein relate to spike detection of electric signals and in particular spikes of electrophysiological signals corresponding to activity.
  • Neural spikes as captured in electrophysiological recordings, particularly through extracellular methods, are crucial for understanding the complex dynamics of the brain. Unlike action potentials that occur within individual neurons, spikes in these recordings often represent the combined activity of multiple neurons firing near-simultaneously. This collective neural activity is vital for interpreting the functional aspects of brain networks.
  • spike detection relies on setting a threshold level, with neural activity above this threshold identified as a spike.
  • this threshold in existing systems, particularly those with many electrodes and corresponding channels, also known as high-channel recording systems, setting this threshold in the process of spike detection presents unique challenges.
  • this threshold level is set manually.
  • the threshold for spike detection needs to be individually defined for each electrode, accommodating variations that can occur between different individuals, over time, and across the areas of the high channel count pelectrode array. It is also necessary to record the data with high sampling rate (i.e. 2* spike activity highest frequency content, ⁇ 15kHz), digitize the recordings, transmit the data to external unit, find the threshold value and transmit the threshold information back to the integrated circuit (IC) to set the threshold value. All of these steps need to be repeated (i.e. periodically) to account for the changes in the threshold value over time.
  • implanted ICs require the capability to be repeatedly calibrated.
  • this recalibration necessity restricts the design process particularly for implantable applications where access and power consumption after implantation are restricted.
  • SNR enhancement techniques such as the Nonlinear Energy Operator (NEO) or Energy of Derivative (ED) are applied to attenuate the low-frequency and low amplitude content of the signal (often where most of the noise resides) and accentuate the high-frequency and high amplitude content, which is indicative of spiking activity.
  • NEO Nonlinear Energy Operator
  • ED Energy of Derivative
  • the threshold must be determined.
  • the threshold is based on an average of the signal (whether raw or enhanced). Averaging the signal, however, often produces an overly aggressive threshold that may exclude activity spikes or an under aggressive threshold that may include noise.
  • a method for detecting spikes in an electrophysiological signal corresponding to neurological activity includes receiving and enhancing the electrophysiological signal using an SNR enhancement technique to obtain an enhanced signal.
  • the method further includes calculating the standard deviation of the enhanced signal based on a distribution constant corresponding to the signal to noise ratio (SNR) enhancement technique.
  • the method further includes calculating a threshold based on the calculated standard deviation.
  • the method further includes detecting spikes based on the calculated threshold.
  • the background noise may follow a Gaussian distribution with a mean of zero.
  • the distribution constant may be 0.234.
  • Calculating the threshold may include setting the threshold to a predetermined multiple of the standard deviation by a predetermined scaling factor.
  • the predetermined scaling factor may be 5.
  • the predetermined scaling factor may be 2 or 3.
  • Detecting the one or more spikes may include comparing the enhanced signal to the threshold.
  • the one or more spikes may be detected where the enhanced signal exceeds the threshold.
  • the electrophysiological signal may be received by an electrode.
  • the electrode may be a passive electrode. [0033] The electrode may be an active electrode.
  • the electrode may include a high pass filter.
  • the amplifier may have a bandwidth between 300 Hz to 8 kHz to remove the LFPs while passing the one or more spikes.
  • the amplifier may be a two-stage capacitively-coupled operational transconductance amplifier (OTA).
  • OTA operational transconductance amplifier
  • the differentiator may be implemented using passive components.
  • the electrophysiological signal may be amplified and passed through a band pass filter.
  • the LFPs may be removed from the electrophysiological signal by a high pass pole.
  • the standard deviation may be calculated in the digital domain.
  • the threshold may be a predetermined multiple of the standard deviation by a predetermined scaling factor.
  • the predetermined scaling factor may be 2 or 3.
  • the spike detection module may detect the one or more spikes by comparing the enhanced signal to the threshold.
  • the electrode may be a passive electrode.
  • the electrode may be an active electrode.
  • the electrode may include a high pass filter.
  • the electrode may include an amplifier.
  • the amplifier may have a bandwidth between 300 Hz to 8 kHz to remove the LFPs while passing the one or more spikes.
  • the amplifier may be a two-stage capacitively-coupled operational transconductance amplifier (OTA).
  • OTA operational transconductance amplifier
  • Figure 7A is a block diagram of a system for detecting spikes of Figure 1 , according to an embodiment
  • Figure 14B is simulation results for varying input DC levels and input pulse signal with varying duty cycles input to the switched-capacitor based rolling average of Figure 14A, according to an embodiment
  • Figure 19B is experimental measurement results of performance evaluation of spike detection of Figures 7A and 7B in a high-noise scenario using prerecorded neural data, according to an embodiment.
  • Spikes provide a window into the cooperative and synchronized activities of neuronal populations, revealing how groups of neurons communicate and process information in concert. The ability to accurately detect and analyze these spikes is therefore essential for decoding the intricate patterns of neural communication and understanding the emergent properties of neural networks. This focus on spikes reflects a broader perspective in neuroscience, emphasizing the importance of collective neural behavior over individual neuronal events in understanding brain function and disorders.
  • the application 100 includes a signal acquisition module 102 configure to acquire a bioelectrical signal.
  • the signal acquisition module 102 may be an implantable electrode or the like configured to monitor electrical signals corresponding to a specific activity such as neurological activity.
  • the signal can be from any neural cell such as a retinal ganglion cell (RGC).
  • RRC retinal ganglion cell
  • the raw bioelectrical signal is provided to a spike detection module 104 for detecting spikes of the bioelectrical signal.
  • a detection threshold of the spike detection module 104 is finely tuned to effectively capture spiking activities while excluding incidental peaks attributable to background noise.
  • the method 200 includes enhancing the raw signal 206 to obtain the enhanced signal 210.
  • the signal is enhanced by SNR enhancement techniques.
  • the SNR enhancement techniques are applied to attenuate the low-frequency and low amplitude content of the signal (often where most of the noise resides) and accentuate the high-frequency and high amplitude content, which is indicative of spiking activity. By doing so, they effectively enhance the energy content of spikes relative to the background noise. This results in a clearer distinction between neural spikes and noise, facilitating the task of defining the spike detection threshold.
  • each channel typically operates with a limited power budget. This restriction often leads to a higher level of integrated input-referred noise in the recording front-end, resulting in a lower SNR, which can significantly hinder the effectiveness of spike detection when relying solely on raw data.
  • the amplified noise level can obscure the spikes, leading to increased false negatives (missed spikes) or false positives (noise mistaken for spikes).
  • SNR enhancement techniques can mitigate these issues by boosting the signal component relative to the noise, making the spikes more prominent and distinguishable even in a low SNR setting.
  • Such techniques may include NEO or ED.
  • NEO enhances the detection of energy transients in signals, such as neural spikes.
  • NEO works by nonlinearly combining a signal with its first and second derivatives to accentuate high-energy features.
  • x(t) For a continuous signal x(t), the NEO is defined as i
  • /NEo(x(t)) x’(t) 2 - x(t)x”(t).
  • This equation amplifies points in the signal where there is a significant energy difference, thereby enhancing sharp transients. Therefore, NEO is especially effective in suppressing low-frequency components prevalent in background noise, while enhancing high-frequency components associated with spikes.
  • the Energy of Derivative (ED) technique emphasizes the energy content of a signal’s temporal derivatives to enhance spike detectability.
  • ED is based on the premise that neural spikes, due to their rapid onset and offset, exhibit considerable energy in their first derivatives.
  • ED amplifies parts of the signal with rapid changes, such as the edges of a spike, aiding in distinguishing spikes from slower signal components.
  • the ED technique focuses on the square of the signal’s derivative eliminating the need for dual-threshold levels by ensuring that all data points are positive. This beneficially simplifies the thresholding process over, for example, other SNR enhancement techniques or using raw data and enables a single threshold level that can be determined in the analog domain.
  • FIGS. 3 and shown therein are examples 300 of performance of spike detection for high and low SNR recordings using raw and SNR enhanced signals which shows sensitivity improvements especially in low SNR conditions, according to an embodiment. These figures show how the ED operator is more necessary in case of high background noise.
  • FIG. 4 shown therein are simulation results 400 for sensitivity versus false positive rate (FPR) receiver operating characteristic (ROC) generated spike detection with and without ED operator, according to an embodiment.
  • the ED operator may be the ED operator 506 of Figure 5.
  • the results show how the ED operator is more necessary in case of high background noise by raw signal standard deviation (RSTD) or ED STD methods with analog and digital implementation.
  • RSTD raw signal standard deviation
  • the comparison in Figure 4 shows how the spike detection of the raw signal can be effective for low noise recording, but it will fail for recording with higher noise.
  • the SNR enhancement performed by the ED operator makes it possible to detect the spike even in a noisy background.
  • the method 200 further includes, at 212, calculating the standard deviation of the enhanced signal 210 as Sigma en hanced 214. Because spiking events are sparse relative to the background noise within the enhanced signal 210, the Sigmaenhanced 214 is selected as a representative of the background noise variation. Consequently, Sigmaenhanced 214 predominantly reflects the fluctuations of the background activity, thereby providing a robust baseline against which spikes, characterized by their relatively higher amplitude, can be detected.
  • the goal is to perform as much computing as possible in the analog domain and ideally extract the spiking activity which can trigger real-time application or trigger specific algorithms that have been implemented using spiking neural networks which can best imitate the biological activities. Therefore, while both analog and digital domains have their merits in adaptive spike detection, analog implementation emerges as a more suitable option for high-channel count applications considering the advantages listed below:
  • Analog circuits can handle parallel processing, which is advantageous when dealing with multiple channels of neural data simultaneously.
  • the target is to find the standard deviation of the background noise. It has been shown that measured background noise from actual recording have almost a Gaussian distribution. Consequently, when a data stream characterized by a Gaussian noise distribution is compared against its own standard deviation, the duty cycle (defined as the proportion of time the output signal is ’high’ or at a logic level of 1 , over the total observation time) would stabilize at a distribution constant value of 0.159xVDDComp. This value represents the percentage of time that the signal exceeds its own standard deviation within the Gaussian distribution.
  • the system 700 includes a signal enhancement module 708.
  • the signal enhancement module receives a raw signal 206 output from the electrode 704 and applies SNR enhancement techniques to obtain an enhanced signal 210.
  • the signal enhancement techniques may be NEO or ED as described at 208 of Figure 2.
  • the system 700 further includes a standard deviation calculator 712.
  • the standard deviation calculator 712 calculates a Sigmaenhanced 214 based on the enhanced signal 210 and a distribution constant 724.
  • the distribution constant 724 may be calculated via a feedforward structure such as the feed forward structure 502 of Figure 5B.
  • the system 700 further includes a threshold calculator 716.
  • the threshold calculator 716 receives the Sigma en hanced 214 from the standard deviation calculator 712 and calculates a threshold 218 based on the Sigmaenhanced 214.
  • the threshold 218 is a multiple of the Sigmaenhanced 214.
  • the multiplier (K) by which the threshold 218 is a multiple of the Sigmaenhanced 214 may be predetermined based on the application of the system. For example, in biomedical applications the multiplier may be 2-4.
  • the system 700 further includes a spike detection module 720.
  • the spike detection 720 compares the threshold 218 to the enhanced signal 210 to detect spikes 222.
  • the system 700 further includes a weighted rolling average 728.
  • the weighted rolling average 728 calculates an average of the output from the comparator 726.
  • the system 700 further includes a subtraction module 730.
  • the subtraction module 730 calculates a subtraction of the output of the weighted rolling average 728 and the distribution constant 724.
  • the system 700 further includes a gain 732.
  • the gain 732 multiplies the signal output by the subtraction module 730 by a factor K.
  • FIG 8 shown therein is a block diagram of a Top-level implementation of an ED-based adaptive threshold generation IC 800, according to an embodiment.
  • the IC 800 is an embodiment of the system 700 of Figure 7.
  • the constant gain is secured through a capacitive gain structure
  • the high pass pole is determined by the feedback RC network including resistor 926 and capacitor 928
  • G m may be the G m of the first stage 902
  • CL may be the load capacitance
  • Cm may be the capacitor 930
  • Cf may be the capacitor 928.
  • LN low-noise
  • the second stage 906 employs a low-power OTA, as the signal is now more robust against noise factors.
  • the low power OTA of the second stage 906 is designed with a similar architecture and transistor sizing of the first stage 902.
  • the size of the M0 transistor 908 has a width/length (W/L) of 1 m/5pm, making the direct current (DC) bias current of the low-power OTA 906 (0.25pA) half of the low-noise OTA 902 (0.5pA), also referred to as the current mirror OTA.
  • the current mirror OTA 902 is selected for its robustness and potential to achieve a low Noise Efficiency Factor (NEF).
  • NEF Noise Efficiency Factor
  • FIG. 10A and 10B shown therein is a schematic and phase plot, respectively, of a circuit-level implementation of a switched-capacitor-based differentiator 1000, according to an embodiment.
  • the differentiator 1000 is an embodiment of the discrete-time, switched-capacitor-based differentiator 810 of Figure 8.
  • the derivative of the differentiator 1000 is implemented using passive components.
  • the differentiator 1000 is, therefore, more power efficient in comparison with the OTA-based implementation 900a.
  • the differentiator 1000 is also less sensitive to high frequency noise as this circuit inherently avoids amplification of high- frequency signal.
  • the differentiator 1000 is also implemented in discrete fashion and therefore, is inherently stable. Also, the clock frequency is selected based on the signal Nyquist rate and therefore, it is not prone to high frequency noise saturation.
  • FIG. 11 shown therein is a simulation result 1100 showing the functionality of the pair of opposite phase differentiators 1000 of Figure 10A.
  • Minimizing the size of the Co 1014 to avoid attenuating the output voltage makes the storage capacitor (i.e., the C o capacitor 1014) prone to fast discharging and clock feedthrough. Therefore, a pair of duplicate switched-capacitor-based differentiators are used where the ⁇ pi 1004 and ⁇ p 2 1008 phases are opposite but the ⁇ p 0 1012 is the same.
  • the output of the two Differentiators 1000 have the same non-idealities which are canceled out on the difference of the two waveforms to obtain a clean discrete-form differentiation of the input voltage.
  • the duty cycle of the standard deviation calculator 812 of Figure 8 includes a switched-capacitor-based weighted rolling average 1400.
  • the switched-capacitor-based weighted rolling average 1400 quantifies the proportion of time during which the output of the comparator 816 remains high, starting from the circuit’s initial operation. This implies that rather than evaluating a fixed time window, the circuit retains all prior data, assigning greater significance to more recent inputs. This computation is fundamentally different from that of an integrator.
  • a switched-capacitor-based weighted rolling average structure is implemented to overcome this limitation.
  • the input 1408 is sampled onto a 30fF capacitor 1402.
  • Ci 1402 and C 2 1404 are connected to equalize their voltages.
  • This expression represents the weighted rolling average of previous samples, enabling the circuit output to fluctuate around the target average.
  • An additional RC output stage including resistor 1412 and capacitor 1414 calculates the average of the signal 1416 that is already stabilized to its mean value. Therefore, the RC stage in this embodiment aims to reduce or smooth the ripple in the output of the Ci 1402 and C 2 1404 capacitors.
  • FIG 14B shown therein are simulation results for varying input DC levels and input pulse signals with varying duty cycles input to the switched-capacitor based rolling average 1400, according to an embodiment. Shown are simulation results under two conditions: (top 1401) when the input is a constant DC level (0.2V- 0.4V- 0.6V- 0.8V) and (bottom 1403) when the input is a pulse waveform with varying duty cycles (20%-40%-60%-80%). The simulations show that the circuit accurately computes the average, with the switched-capacitor arrangement results in minor deviations around the target value and the subsequent stage applying a low-pass filter to produce a near-DC output voltage.
  • FIG. 14C shown therein is a flow schematic indicating the operation of a resistance capacitor structure (RC) 1418 without a switched capacitor, according to an embodiment.
  • RC resistance capacitor structure
  • the output 1420 from this RC structure 1418 which feeds into a subtraction circuit, has a relatively stable value.
  • the comparator’s output 1422 alternates between 0 and VDD from rail to rial, to minimize fluctuations at the RC output 1420, a small pole, necessitating a large resistor 1424 and capacitor 1426 (time constant > 100ms), is needed.
  • the IC 800 further includes a threshold calculator 828 and a spike detection module 830.
  • the calculator 828 and the module 830 operate substantially similarly to the threshold calculator 716 and the spike detection module 720 of Figure 7B, respectively.
  • the calculator 828 represents the gain of the threshold signal with respect to the calculated standard deviation.
  • the module 830 is the final comparator that compares the output of ED operator with the automatically generated threshold signal and outputs the detected spikes.
  • FIGS 15A and 15B shown therein are experimental measurement results of input referred noise 1500 and gain and bandwidth 1502, respectively of the two-stage amplifier 802 of Figure 8, according to an embodiment.
  • the results show the measured integrated input referred noise and voltage gain of the input two-stage amplifier 802, confirming an Infinitely iterated rippled noise (URN) of 8.82pV in the band of interest, mid-band gain of 51.45 dB and a bandwidth of 237.5Hz- 8.27kHz.
  • UPN Infinitely iterated rippled noise
  • FIG. 16 shown therein is a dynamic response 1600 of the spike detection system 720 of Figure 7A to Gaussian noise input, showing the automatically calculated standard deviation and the adaptive threshold over a 20-second interval, according to an embodiment.
  • the results show a response of the spike detection system 720 to a Gaussian noise with temporally varying standard deviation fed to the recording front-end.
  • the automatically calculated sigma E D and threshold voltages for a Gaussian noise are shown with the temporally varying standard deviation input signal.
  • the results indicate that the threshold is set at a voltage that is effectively higher than the noise activity except in very few points (6 points in 20s of data) validating the performance of the sigma detection loop.
  • FIG. 17 shown therein are experimental measurement results 1700 of the spike detection system 720 of Figure 7A to input noise with varying standard deviation 1702, ED output with the automatically generated threshold 1704, in-loop comparator (i.e., the comparator 726) output indicating 1s density 1706, and identification of falsely detected spikes 1708, according to an embodiment.
  • the results show responses to Gaussian noise characterized by a temporally varying standard deviation fed to the recording front-end which indicate the feedback loop’s capability to ascertain sigma E D, which fluctuates in accordance with the amplitude of the background noise.
  • the final plot 1808 showcases that spikes with extended duration are not falsely counted multiple times, owing to the refractory period mechanism integrated within the spike detection algorithm. This refractory period ensures that once a spike is detected, a certain time elapses before another spike can be identified, thereby preventing overcounting and improving the fidelity of spike detection.
  • FIGS. 19A and 19B shown therein are experimental measurement results of performance evaluation of spike detection in a low-noise scenario 1900 and a high noise scenario 1901 , respectively, using prerecorded neural data, according to an embodiment.
  • the results showcase the efficacy of the ED operator, such as ED operator 506 of Figure 5, in SNR enhancement and spike identification.
  • the experimental measurement results include a comparative evaluation of the presented spike detection circuit in environments with different levels of background noise, using a dataset of prerecorded neural signals.
  • the higher background noise poses a challenge for spike detection.
  • the initial plot 1910 once again shows the input signal which is the ground through to validate the effectiveness of the ED and adaptive threshold loop in identifying the spiking activity.
  • the recording output 1912 with increased noise level, underscores the difficulty in setting an appropriate threshold for the raw data.
  • the application of the ED operator 506 transforms the signal in such a manner that threshold setting becomes feasible, as shown in the third plot 1914.
  • the ED operator 506 effectively enhances the spikes, making them distinguishable from the noisy background.
  • the final plot 1916 in the sequence illustrates the detected spikes, confirming that, despite the presence of substantial noise, the system is able to accurately identify spikes, thereby validating the effectiveness of the ED operator 506 in high- noise conditions.
  • the post processing of 5 data epochs (each comprising 100-ms windows of prerecorded neural signals) shows the spike detection circuit achieves a 97% sensitivity and 0.4% false alarm rate in low noise conditions and 92% sensitivity and 3.8% false alarm rate in high noise condition.

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Abstract

Sont divulgués un procédé et un système de détection d'au moins un pic dans un signal électrophysiologique correspondant à une activité neurologique. Le procédé consiste à recevoir et à améliorer le signal électrophysiologique au moyen d'une technique d'amélioration du rapport signal sur bruit (SNR) pour obtenir un signal amélioré. Le procédé consiste en outre à calculer un écart-type du signal amélioré, en fonction d'une constante de distribution correspondant au signal à SNR amélioré. Le procédé consiste encore à calculer un seuil en fonction de l'écart-type. Le procédé consiste en outre à détecter au moins un pic en fonction du seuil.
PCT/CA2025/050221 2024-02-21 2025-02-21 Étalonnage de seuil adaptatif pour détection de pic Pending WO2025175394A1 (fr)

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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110071766A1 (en) * 2008-05-28 2011-03-24 Koninklijke Philips Electronics N.V. Method and system for determining a threshold for spike detection of electrophysiological signals
US20120041293A1 (en) * 2008-12-23 2012-02-16 Commissariat A L'energie Atomique Et Aux Ene Alt Methods and devices for processing pulse signals, and in particular neural action potential signals
US20140330102A1 (en) * 2013-05-03 2014-11-06 The Florida International University Board Of Trustees Low noise analog electronic circuit design for recording peripheral nerve activity
US20170296081A1 (en) * 2014-10-16 2017-10-19 Agency For Science, Technology And Research Frame based spike detection module
US20190012515A1 (en) * 2015-08-24 2019-01-10 UNIVERSITé LAVAL System and method for detecting spikes in noisy signals
US20190059767A1 (en) * 2015-03-10 2019-02-28 Nuvo Group Ltd. Apparatuses for tracking physiological parameters of mother and fetus during pregnancy
CN113057655A (zh) * 2020-12-29 2021-07-02 深圳迈瑞生物医疗电子股份有限公司 用于脑电信号干扰的识别方法以及识别系统、检测系统

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110071766A1 (en) * 2008-05-28 2011-03-24 Koninklijke Philips Electronics N.V. Method and system for determining a threshold for spike detection of electrophysiological signals
US20120041293A1 (en) * 2008-12-23 2012-02-16 Commissariat A L'energie Atomique Et Aux Ene Alt Methods and devices for processing pulse signals, and in particular neural action potential signals
US20140330102A1 (en) * 2013-05-03 2014-11-06 The Florida International University Board Of Trustees Low noise analog electronic circuit design for recording peripheral nerve activity
US20170296081A1 (en) * 2014-10-16 2017-10-19 Agency For Science, Technology And Research Frame based spike detection module
US20190059767A1 (en) * 2015-03-10 2019-02-28 Nuvo Group Ltd. Apparatuses for tracking physiological parameters of mother and fetus during pregnancy
US20190012515A1 (en) * 2015-08-24 2019-01-10 UNIVERSITé LAVAL System and method for detecting spikes in noisy signals
CN113057655A (zh) * 2020-12-29 2021-07-02 深圳迈瑞生物医疗电子股份有限公司 用于脑电信号干扰的识别方法以及识别系统、检测系统

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