Abstract:
A method for determining an occurrence of a spike in one of a plurality of neural signals is provided, the spike relating to an action potential in the one of the plurality of neural signals. The method includes sampling the plurality of neural signals at a sampling frequency; extracting frequency features from each of the plurality of neural signals during sampling of the plurality of neural signals; and monitoring the extracted frequency features to determine characteristics of the one of the plurality of neural signals indicative of an occurrence of a spike.

Description:
PRIORITY CLAIM 
       [0001]    The present application claims priority to Singaporean Patent Application No. 201306771-5, filed on 9 Sep. 2013. 
       FIELD OF INVENTION 
       [0002]    The present invention relates broadly to neural signal monitoring and more particularly to a device for detecting a spike in one of a plurality of neural signals. 
       BACKGROUND 
       [0003]    An important aspect of research related to neuroscience is the detection and analysis of neural signals. Neural signals relate to electrically excitable cells that process and transmit information through electrical and chemical signals. With current technology, recordings have been made of electrical neurons in neural networks such as the brain, including the human brain, cell cultures and in vivo tissue. 
         [0004]    In order to record neural signals, a neural signal recording device has to be utilized. Conventionally, in order to acquire signals from different neurons in a region of the brain, a recording device could be implanted in the brain for recording neural signals. Another conventional technique to acquire signals is to position one or more electrode sensors on a subject&#39;s brain for recording neural signals. 
         [0005]    It is of particular importance to detect an occurrence of a spike during recording of the neural signals. A spike represents an action potential that typically results from a spontaneous or stimulus-evoked activity. As such, a spike can be seen as a sharp transient that is visibly different from background noise. Since the spike is a rare event in neural signals, it is desirable to detect the spike in order to minimize power dissipation while preserving the essential neural activity. 
         [0006]    However, conventional spike detection methods and devices typically suffer from shortcomings that prevent their use in low-power devices of sufficiently small size for implanting in a subject. A typical conventional spike detection technique makes use of amplitude detection in the neural signals and a shortcoming of such technique is that it does not perform adequately since it has been proven to lack robustness to DC shifts. 
         [0007]      FIG. 1  shows a typical neural signal according to R. Muller, S. Cambini, and J. M. Rabaey, “A 0.013 mm 2 , 5 μW, DC-Coupled Neural Signal Acquisition IC with 0.5V Supply,”  IEEE J. Solid - State Circuit , published in 2012. The neural signal is categorized to two different potentials. One is a local field potential (LPF) which is an averaged electrophysiological signal caused by the electrical current flowing from all dendritic synaptic activity within a volume of tissue. The other is an Action Potential (AP), also known as the spike that is generated by each neuron. The LPF has relatively low frequency from 1 to 300 Hz, while the spike is represented in 300 to 10 KHz. In order to eliminate DC offset and separate the spike from the LPF, conventional neural recording system makes use of high-pass or band-pass filter as shown in  FIG. 2( a ) . 
         [0008]      FIG. 2( a )  shows a conventional neural recording system. The system includes a low-noise amplifier (LNA) for amplifying sampled signals from a sample and holder device (S/H), an analog-to-digital converter (ADC) and a radio integrated circuit to transmit digitized signal. 
         [0009]      FIGS. 2( b ) and 2( c )  show a conventional neural recording system including a spike detector in analog and digital domains, respectively. The spike detectors shown in  FIGS. 2( b ) and 2( c )  compare the signal potential to threshold potential. However, the absolute values of the spike potentials may vary depending on the sites on where the electrodes are positioned. For example, electrodes positioned far away from a neuron in a subject&#39;s brain may detect a different spike potential than the electrodes positioned close to a neuron in the subject&#39;s brain. As such, this technique may deteriorate the accuracy of the spike detection in a plurality of neural signals. 
         [0010]    Thus, what is needed is a method and device for detecting a spike in one of a plurality of neural signals by using other characteristics together with the amplitude information, which minimizes power dissipation and increases its accuracy. Furthermore, other desirable features and characteristics will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and this background of the disclosure. 
       SUMMARY 
       [0011]    According to the Detailed Description, a method for determining an occurrence of a spike in one of a plurality of neural signals is provided, the spike relating to an action potential in the one of the plurality of neural signals. The method includes sampling the plurality of neural signals at a sampling frequency; extracting frequency features from each of the plurality of neural signals during sampling of the plurality of neural signals; and monitoring the extracted frequency features to determine characteristics of the one of the plurality of neural signals indicative of an occurrence of a spike. 
         [0012]    In accordance with another aspect, a device for determining an occurrence of a spike in one of a plurality of neural signals is provided, the spike relating to an action potential in the one of the plurality of neural signals. The device includes a sampling unit and a spike detector. The sampling unit is adapted to sample the plurality of neural signals at a sampling frequency. The spike detector is coupleable to the sampling unit and adapted to extract frequency features from each of the plurality of neural signals during sampling of the plurality of neural signals. The spike detector is further adapted to monitor the extracted frequency features to determine characteristics of the one of the plurality of neural signals indicative of an occurrence of a spike. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0013]    The accompanying figures where like reference numerals refer to identical or functionally similar elements throughout the separate views and which together with the detailed description below are incorporated in and form part of the specification, serve to illustrate various embodiments and to explain various principles and advantages in accordance with a present embodiment. 
           [0014]      FIG. 1  shows a typical neural signal. 
           [0015]      FIG. 2( a )  shows a conventional neural recording system. 
           [0016]      FIG. 2( b )  shows a conventional neural recording system including a spike detector in an analog domain. 
           [0017]      FIG. 2( c )  shows a conventional neural recording system including a spike detector in a digital domain. 
           [0018]      FIG. 3  shows a block diagram of a device for detecting a spike in one of a plurality of neural signals in accordance with an embodiment. 
           [0019]      FIG. 4  shows a schematic diagram of the delta sampling architecture used in  FIG. 3 . 
           [0020]      FIG. 5  shows simulation timing waveforms of the delta sampling architecture shown in  FIG. 4 . 
           [0021]      FIG. 6  shows some sampling points on a representative neural signal. 
           [0022]      FIGS. 7( a )-7( c )  show schematic diagrams of the device shown in  FIG. 1  in accordance with example embodiments. 
           [0023]      FIG. 8( a )  shows a schematic diagram for detecting an amount of change in at least two consecutive signals based on charge redistribution when there is a positive sign in accordance with an example embodiment. 
           [0024]      FIG. 8( b )  shows a schematic diagram for detecting an amount of change in at least two consecutive signals based on charge redistribution when there is a negative sign in accordance with an example embodiment. 
           [0025]      FIG. 9  shows simulation results of the delta sampling architecture shown in  FIG. 4 . 
           [0026]      FIG. 10  shows simulation results of the device shown in  FIG. 3  with a spike. 
           [0027]      FIG. 11  shows simulation results of the device shown in  FIG. 3  without a spike. 
       
    
    
     DETAILED DESCRIPTION 
       [0028]    It should further be appreciated that the exemplary embodiments are only examples, and are not intended to limit the scope, applicability, operation, or configuration of the invention in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing an exemplary embodiment of the invention, it being understood that various changes may be made in the function and arrangement of elements and method of operation described in an exemplary embodiment without departing from the scope of the invention as set forth in the appended claims. 
         [0029]      FIG. 3  shows a block diagram of a device  300  for detecting a spike in one of a plurality of neural signals in accordance with an embodiment. The device  300  includes a low-noise amplifier (LNA)  302  coupled to a delta sample and holder (delta S/H) or delta sampling architecture  304  in an example embodiment. The LNA  302  is adapted to amplify analog signals for inputting into the delta S/H  304 . 
         [0030]    A typical S/H, also “track-and-hold” device, is an analog device that samples (captures, grabs) the voltage of a continuously varying analog signal and holds (locks, freezes) its value at a constant level for a specified minimum period of time. They are typically used together with ADC to eliminate variations in input signal that can corrupt the conversion process. In an embodiment, the delta S/H  304  is adapted to sample and hold the analog signals and extract amplitude and frequency information of the analog signals. Further, the delta S/H  304  is also adapted to achieve high-pass filtering functionality which eliminates the need to use a high-pass or band-pass filter typically used in a conventional neural recording system. 
         [0031]    In an embodiment, the delta S/H  304  is coupled to analog memories  306 . Analog memories store consecutive output signal of the delta S/H and the spike detector  310  recognizes a spike signal by analyzing stored data. Once the spike signal is detected, one of analog memories  306  is fed to an ADC  308 . In the instance that the spike detector  310  extracts a spike in the analog signals, the spike detector  310  may be so controlled to trigger an enable signal to the ADC  308 . In this manner, operations of the ADC  308  are controlled to be activated when a spike is detected. 
         [0032]    The device  300  is configured to detect the spike during sampling of the analog signals. Advantageously, this allows the ADC  308  and a transmitter  312  to only consume electronic power necessary for the spike signal. This effectively results in minimizing power dissipation by reducing a dynamic range of neural signals. 
         [0033]      FIG. 4  shows a schematic diagram of the delta sampling architecture  400  used in  FIG. 3 . In an embodiment, the delta sampling architecture  400  includes a switch  402  coupled to a capacitor  404 . The switch  402  is typically a field effect transistor switch and the capacitor  404  is used to store electric charges. 
         [0034]    The capacitor  404  is coupled to at least one operational amplifier  406 . The amplifier  406  is adapted to charge or discharge the capacitor  410  so that the voltage across the capacitor is practically equal or proportional to an input voltage. 
         [0035]    In hold mode, the switch  402  disconnects the capacitor  404  from the amplifier  406  and the voltages across the capacitors  404  and  410  are maintained during the operation. 
         [0036]    The delta sampling architecture  400  uses multiple feedback capacitors  404  and  410  that can function as analog memories. Advantageously, it helps to preserve a spike signal. On the other hand, conventional techniques typically lose data pertaining to neural signals before detecting a spike because feedback capacitors are not used as analog memories. 
         [0037]      FIG. 5  shows simulation timing waveforms of the delta sampling architecture shown in  FIG. 4 . Waveform  502  shows a signal for RST switch  408  shown in  FIG. 4 . When the RST switch is turned on, the output of the delta S/H is reset to V REF . After resetting the delta S/H, the output is changed by the sampled voltage V x . Waveform  504  shows a signal for the S/H switch  402  shown in  FIG. 4 . Waveform  506  represents the input voltage V in . Waveform  508  represents the sampled voltage V x  in response to the S/H switch  402  operation. Waveform  510  represents the output voltage V out  of the delta sampling architecture. 
         [0038]    The output voltage V out  of the delta sampling architecture is given by: 
         [0000]    
       
         
           
             
               
                 
                   
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         [0039]    Assuming that the input signal is sinusoidal wave and the gain is 1, the output voltage V out  of the delta sampling architecture is given by: 
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         [0000]    where A and f are amplitude and frequency of the signal, and f s  and n are sampling frequency and rate, respectively. The above equations show how delta sampling by the delta sampling architecture  400  can ignore low frequency component of the neural signals because its output level in high sampling rate should be lower than input referred noise. In an embodiment, the sampling frequency is chosen from 10 to 40 kHz. Advantageously, this prevents the spike signal from aliasing error. As such, the different signals will not become indistinguishable (or aliasing of one another). This also helps to preserve the spike detection. 
         [0040]      FIG. 6  shows some sampling points  602  on a representative neural signal  600 .  FIG. 6  shows how high frequency component can be suppressed by observing how the signal changes from successive recordings (or signals) from the delta sampling architecture. The factors that affect the signal change of the successive recordings include the sampling frequency and the number of samples. The observation of the successive recordings from the delta sampling architecture is indicative of the characteristics of the spike. As such, the signs of the consecutive recordings can be observed from the delta sampling architecture and utilized as a low pass filter. In order to increase the accuracy of the spike detection, it is necessary to discriminate the spike from noise. 
         [0041]      FIG. 6  shows that the change from a sampling point  602 ( a ) to a next consecutive sampling point  602 ( b ) has a smaller amplitude (or amount of change) in comparison to the change from a sampling point  602 ( c ) to sampling point  602 ( d ). Points  602 ( c ) and  602 ( d ) are arranged on a rising portion of the signal  600  which represents a spike. As such, this shows that the amount of change between any two consecutive points in the spike is much larger than other points of the signal. In an embodiment, a spike detector is used to compare the amount of change between two consecutive points with a pre-determined value, thereby distinguishing the spike from noise. 
         [0042]      FIGS. 7( a )-7( c )  show schematic diagrams of the device shown in  FIG. 1  in accordance with example embodiments.  FIG. 7( a )  shows a device  700  including a delta sampling architecture  702 . The delta sampling architecture  702  includes four feedback capacitors  704 ,  706 ,  708  and  710  as analog memories. Each of the plurality of feedback capacitors helps to detect a signal. Advantageously, having a plurality of feedback capacitors functioning as analog memories helps to preserve a spike signal. This cannot be achieved by the conventional techniques of detecting a spike because information pertaining to a spike is usually lost. 
         [0043]      FIG. 7( b )  shows a device  720  including four D flip-flops  722 ,  724 ,  726  and  728  for latching the sign of each data. The output of the sign detector SIGN_P or SIGN_N goes high when all of signs into the four D flip-flops  722 ,  724 ,  726  and  728  are either positive or negative. Advantageously, this makes it possible to detect a spike if four consecutive signals are having a same direction of change, e.g., all four signals are having a positive or negative direction of change. 
         [0044]      FIG. 7( c )  shows a device  740  including a size detector module  750 . The size detector module  750  includes capacitors  754 ,  756  and  758  suitable for successive approximation ADC by charge redistribution. The successive approximation register or size detector module  750  is adapted to control the capacitors  754 ,  756  and  758  for generating pre-defined threshold level that is compared with each sampled data. Further, the device  740  includes four D flip-flops  742 ,  744 ,  746  and  748  for latching comparison result of each data, which represents the size of each data. Similar to the device shown in  FIG. 7( b ) , the output of the size detector SIZE_P or SIZE_N goes high when the direction of change detected by the four D flip-flops  742 ,  744 ,  746  and  748  are either positive or negative. 
         [0045]      FIG. 8( a )  shows a schematic diagram of a circuit  800  for detecting size by charge redistribution when there is a positive sign in accordance with an example embodiment. The circuit  800  includes a plurality of switches  802 ,  804 ,  806 ,  808  and  810 . In the event that the result of the size detection is positive, one of the switches  802 ,  804 ,  806 ,  808  and  810  is changed from reference level to ground, thereby pushing down the potential in a negative node of the comparator  812 . 
         [0046]      FIG. 8( b )  shows a schematic diagram of a circuit  850  for detecting size by charge redistribution when there is a negative sign in accordance with an example embodiment. The circuit  850  includes a plurality of switches  852 ,  854 ,  856 ,  858  and  860 . In the event that the result of the size detection is negative, the charge redistribution increases the potential. As shown, the circuits  800 ,  850  shown in  FIGS. 8( a ) and 8( b )  can detect the spike signal with both positive and negative peak by comparing the changed sample data (subtracted value between sampled data and charge-redistributed value) with the reference voltage. 
         [0047]    A spike is considered detected if the change in several numbers of consecutive signals has the same sign and one of the signal changes is determined to be larger than the generated (or predetermined) potential level. In an embodiment, the number of consecutive signals is four. It is apparent to a person skilled in the art that any numbers of size and sign considerations are possible to determine a spike in a plurality of neural signals. 
         [0048]      FIG. 9  shows simulation results  900  of the delta sampling architecture shown in  FIG. 4 . Simulation result  902  represents the input voltage V in a sine waveform. Simulation result  904  represents the output voltage V output  in a cosine waveform because the delta sampling architecture gives the 1 st  order derivative of input signal which is also shown in equation 2. 
         [0049]      FIG. 10  shows simulation results  1000  of the device shown in  FIG. 3  with a spike. An input spike  1002  represents a signal that is set to a sine waveform with 2 kHz frequency and 40 mV amplitude and it is fed into the device shown in  FIG. 3 . A gain of 100 is set to LNA  302  ( FIG. 3 ) so that the spike has 400 μV amplitude. The sampling frequency is 42 kHz and the power output is set at 0.5V in this simulation. 
         [0050]    A delta signal  1006  is represented by a cosine waveform and a delayed signal  1008  is shown. Three enable signals  1010 ,  1012  and  1014  are detected for triggering the operations in the ADC  308  shown in  FIG. 3 . As shown in  FIG. 10 , before the triggering of the first enable signal  1010 , four consecutive output of the delta S/H have identical signs and the amount of signal change is large enough. As such, the detection result goes high and the ADC operation is triggered. 
         [0051]      FIG. 11  shows simulation results  1100  of the device shown in  FIG. 3  without a spike. An input spike  1102  and a delta signal  1104  are shown. As it can be seen, when the delta signal is not large enough to be considered as a spike (e.g., the amount of change in consecutive signals is not large enough), an enable signal will not be generated. 
         [0052]    In order to generate the simulation results from the delta sampling architecture shown in  FIG. 3 , the power consumed is expected to be less than 0.2 nW/ch, without taking into consideration the LNA and the bias circuitry. 
         [0053]    Accordingly, the above description presents a technique for detecting a spike that can achieve low power dissipation. This provides a spike detector having a low-power usage according to the proposed technique, making it safe to be used on a subject&#39;s head or to be implemented in the subject&#39;s brain, without sacrificing the essential recording of the neural signals. 
         [0054]    The proposed technique measures consecutive signal changes and extracts frequency features from the plurality of neural signals. This helps to resolve the shortcoming of the conventional techniques which make use of only extracting amplitude features. Accordingly, the proposed technique solves the problem of lacking robustness to DC shifts posed by such amplitude extracting techniques. 
         [0055]    In an embodiment, the proposed technique extracts both amplitude and frequency features from the plurality of neural signals. Beneficially, since the proposed technique extracts and monitors frequency and amplitude features from each of the plurality of neural signals, it is able to improve the accuracy in spike detection even when signals with different amplitude are received in a probe. 
         [0056]    It should further be appreciated that the exemplary embodiments are only examples, and are not intended to limit the scope, applicability, operation, or configuration of the invention in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing an exemplary embodiment of the invention, it being understood that various changes may be made in the function and arrangement of elements and method of operation described in an exemplary embodiment without departing from the scope of the invention as set forth in the appended claims.