Source: http://www.google.ca/patents/US7976465
Timestamp: 2013-05-23 19:05:19
Document Index: 551069684

Matched Legal Cases: ['Application No. 03779099', 'Application No. 03779116', 'Application No. 03809097', 'Application No. 03781337', 'Application No. 03809112', 'Application No. 03809096', 'Application No. 03809096', 'Application No. 03809129', 'Application No. 03781340', 'Application No. 03779123', 'Application No. 03781340', 'Application No. 03779123', 'Application No. 03809129', 'Application No. 03777649', 'Application No. 03779099', 'Application No. 03809097', 'Application No. 03809096', 'Application No. 03779116']

Patent US7976465 - Phase shifting of neurological signals in a medical device system - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Advanced Patent Search | Web History | Sign inAdvanced Patent SearchPatentsMethod and apparatus for phase shifting neurological signals received from monitoring elements of a medical device. The medical device comprises a plurality of monitoring elements each receiving a neurological signal. The device processes the neurological signals and identifies a time shift of the neurological...http://www.google.ca/patents/US7976465?utm_source=gb-gplus-sharePatent US7976465 - Phase shifting of neurological signals in a medical device systemPublication numberUS7976465 B2Publication typeGrantApplication number11/550,294Publication date12 Jul 2011Filing date17 Oct 2006Priority date15 Oct 2002Also published asEP1558129A2EP1558129A4EP1558129B1US7149572US20040133120US20070066915WO2004034886A2WO2004034886A3InventorsMark G. FreiJonathan C. WerderDavid L. CarlsonOriginal AssigneeMedtronic, IncU.S. Classification600/301International ClassificationA61B5/00A61N1/36A61M5/172A61B5/04A61BA61M5/142Cooperative ClassificationA61N1/36082A61B5/048A61M5/1723A61N2/006A61M5/14276A61N1/37229A61N1/36135European ClassificationA61B 5/048A61M 5/172BA61N 1/36A61N 1/36ZA61N 1/36EReferencesPatent Citations (103)Non-Patent Citations (43)Referenced by (1)External LinksUSPTOUSPTO AssignmentEspacenetPhase shifting of neurological signals in a medical device systemUS 7976465 B2Abstract Method and apparatus for phase shifting neurological signals received from monitoring elements of a medical device. The medical device comprises a plurality of monitoring elements each receiving a neurological signal. The device processes the neurological signals and identifies a time shift of the neurological signal relative to each other. The neurological signals are then time shifted according to the time shift upon which the signals are utilized to provide closed-loop feedback control of a treatment therapy.
1. In a medical device system having a plurality of monitoring elements, a method for detecting poor signal quality and for phase shifting neurological signals received from the plurality of monitoring elements, comprising the steps of:
(a) receiving a first neurological signal from a first monitoring element and a second neurological signal from a second monitoring element adjacent to the first monitoring element;
(b) processing the first neurological signal and the second neurological signal;
(c) detecting whether the first neurological signal has poor signal quality by determining either that the first signal exceeds a predetermined boundary for what is expected for the signal from the second monitoring element and other monitoring elements adjacent the first monitoring element, or determining an absolute value of a correlation coefficient between the first and second neurological signals is lower than a predetermined amount;
3. The method of claim 1, wherein the step of ignoring comprises the step of ignoring the received signal experiencing poor signal quality in a closed-loop feedback control of a treatment therapy.
4. The method of claim 1, wherein the step of time shifting comprises the step of computing the time shift by solving a polynomial curve fit equation based on the signal samples.
5. The method of claim 4, wherein the step of computing comprises the step of solving a polynomial curve fit equation selected from the group consisting of a parabolic equation, a linear equation, and a cubic equation.
9. In a medical device system having a plurality of monitoring elements, a method for detecting poor signal quality and for phase shifting neurological signals received from the plurality of monitoring elements, comprising the steps of:
(e) if the first neurological signal does not have poor signal quality, performing (i) sampling the first and second neurological signals at different time instances resulting in a time shift between the first and second neurological signal samples; and (ii) time shifting signal samples of the first neurological signal to correct for the time shift so the first neurological signal is synchronized with the second neurological signal. Description
This application is a continuation of common-owned, co-pending U.S. application Ser. No. 10/687,570 filed on Oct. 15, 2003 and claiming priority to provisional U.S. Application. Nos. 60/418,527 filed Oct. 15, 2002 and 60/503,985 filed Sep. 19, 2003, which are incorporated herein by reference in their entireties.
When the system 100 is set up with the EMU, the externalized ends of the implanted electrodes 101 will connect directly to the bedside device 107. The raw signals collected by the electrodes 101 connected to bedside device 107 are processed by the external system 100 and passed to existing EMU preamplifier 103 and into EMU data collection system 105. Additional electrode connections may occur directly between the patient and existing EMU preamplifier 103 and data collection system 105, by passing the external system 100, to enable recording from a greater number of electrode contacts than used by bedside device 107. By means of a serial cable 111, the bedside device 107 interfaces with the programmer 109 through which system programming, data display, and real-time and/or retrospective analysis may take place.
As an example, the medical device system may monitor excessive flat-lining, which is distinguishable from that which may occur pre- or post-ictally, for a particular neurological signal. For example, a degraded signal may be one in which 40% of the signal values are clipped (e.g., 40% of the data points within the moving window are at the upper �rail�). Such a signal may be considered of poor quality and hence undesirable, as compared to one that has no clipping or only minimal clipping (e.g., 4-5%). The medical device system may therefore take instantaneous signal quality measurements and perform exponential-smoothing or some other averaging over a moving window. The length of the moving window may vary for different embodiments or over time and can have a 60-second duration in one embodiment. The medical device system may thereby compare the level of change between two adjacent data points in the moving window. If the data points' �quality variables� are within a predetermined value of each other, it is indicative of flat-lining. For example, if two adjacent data points are less than 2 bits of signal digital precision from (or, alternatively, within 10% of) each other, it may be determined to be indicative of instantaneous flat-lining. Another indication of flat-lining is where signals from two adjacent monitoring elements are identical. The medical device system may therefore ignore or substitute signals from a specific electrode if the number of flat-line data points in a given rolling window exceeds a predetermined amount or percentage relative to the total number of data points in the time window. Once the number of flat-line data points falls below a second (typically lower) predetermined amount, the medical device system may then re-enable the signal from that electrode to be used for processing (e.g., data analysis or closed-loop feedback control). The system may also provide notification to the physician and/or the patient of these events such as a sensory signal (e.g., alarm). Alternatively, the signal experiencing poor signal quality removed from processing may be replaced with a substituted signal. The substituted signal may be, for example, a signal that provides typical signal characteristics or may provide signal characteristics received from a neighboring monitoring element.
As another example, a neurological signal parameter that may be monitored for signal quality is a �mains� artifact, namely an excessive noise at a certain frequency, for example, approximately 60 Hz. Such a signal may be indicative of outside noise interference (e.g., caused by turning on a lightbulb) and may be indicative of a faulty or high-impedance electrode. Of course, the frequency may vary. For example, in European countries, the AC noise interference has a frequency of approximately 50 Hz. The medical device system may measure instantaneous amplitudes of the signal and calculate a running average for a given moving window, of 60 seconds duration. Once it is determined that the average frequency or amplitude of the signal is excessive, namely above a predetermined threshold, the medical device can remove the associated electrode from consideration in the data analysis process (e.g., a seizure detection algorithm). Once the average frequency or amplitude of the signal returns below a second (typically lower) predetermined level, the associated electrode may then be brought back into consideration.
FIG. 18A shows a 10-second segment of ECoG data (with a seizure beginning at t=5 seconds). FIG. 18B illustrates the same ECoG signal segment, but contaminated by an excessive 60 Hz artifact (0.1 mV noise, beginning at t=2 seconds and ending at t=7 seconds). FIG. 18C illustrates the 60 Hz component of the noisy signal, extracted via the 3 pt. FIR filter with coefficients [0.5, 0, −0.5] (using a MATLAB filter convention for coefficient ordering and sign). FIG. 18D shows a graph of the instantaneous (i.e., 4-point) estimate of 60 Hz. amplitude (1805), along with the resulting �amp60est� signal (1810), computed using the parameter λ60=0.9942 (corresponding to a 0.5 second half-life at sampling rate Fs=240 Hz). The threshold values for �amp60bad� and �amp60restore_good� are also annotated as 0.075 mV and 0.035 mV, respectively. FIG. 18E shows the output of the 60 Hz. artifact detector module, which is set to one at times when the signal is determined to have excessive 60 Hz. noise and zero otherwise.
The embodiment has described monitoring elements taking the form of electrodes sensing electrical activity associated with brain ECoG. Other embodiments of the monitoring element adapted to sense an attribute of a Nervous System Disorder could be used. Alternatively, the monitoring element could detect abnormal concentrations of chemical substances in one location of the brain. Yet another form of the monitoring element would include a device capable of detecting nerve compound action potentials. The monitoring element also may take the form of a device capable of detecting nerve cell or axon activity that is related to the pathways at the cause of the symptom, or that reflects sensations that are elicited by the symptom. The monitoring element may take the form of a transducer consisting of an electrode capable of directly measuring the amount of a particular transmitter substance or its breakdown by-products found in the interstitial space of the central nervous system. The level of the interstitial transmitter substance is an indicator of the relative activity of the brain region. An example of this type of transducer is described in the paper �Multichannel semiconductor-based electrodes for in vivo electrochemical and electrophysiological studies in rat CNS� by Craig G. van Horne, Spencer Bement, Barry J. Hoffer, and Greg A. Gerhardt, published in Neuroscience Letters, 120 (1990) 249-252.
The monitoring element may be external to the body communicating with the implanted portions through telemetry. An example of an external monitoring element is an electrical device that includes an electrode attached to the surface of the skin that passes a small current to measure the skin impedance. An example of this type of the monitoring element is described in the paper �Skin Impedance in Relation to Pain Threshold Testing by Electrical Means�, by Emily E. Meuller, Robert Loeffel and Sedgwick Mead, published in J. Applied Physiology 5, 746-752, 1953. A decrease in skin impedance may indicate an increase in anxiety. Other monitoring elements such as Carbon dioxide gas sensors or other sensors that can detect the physiological parameters such as those listed above will be clear to those skilled in the art.
Y k j = ∑ i = 0 3 ⁢ b i ⁢ X k - i j where b 0 = C j 3 6 - C j 2 2 + C j 3 , b 1 = - C j 3 2 + 2 ⁢ ⁢ C j 2 - 3 ⁢ ⁢ C j 2 , b 2 = C j 3 2 - 5 ⁢ ⁢ C j 2 2 + 3 ⁢ ⁢ C j , b 3 = - C j 3 6 + C j 2 - 11 ⁢ ⁢ C j 6 + 1 , and ⁢ ⁢ C j = 25 - j 8 . ⁢ FIG. 25 shows a flow diagram 2500 for phase shifting in accordance with another exemplary embodiment based on a polynomial interpolation model (e.g., parabolic, linear, cubic, etc.). Step 2501 initiates phase shifting for one the received neurological signals or channels relative to a first neurological signal, which is treated as a reference signal. In step 2502, signal samples for the received neurological signal are collected corresponding to the current sample time and the two previous sample times. In steps 2503 and 2504, unknown variables for the interpolation equation are calculated. In step 2505, a delta time shift is computed for the current channel. In step 2506, the shifted sample output is computed by solving the polynomial curve fit equation at the delta time shift. The received neurological signal may thereby be corrected by shifting the signal samples in time by an amount determined in step 2506 so the neurological signal is synchronized with the default neurological signal. This process may then be repeated for each received neurological signal. The time-shifted neurological signals and the default signal may thereby be utilized to provide closed-loop feedback control of the treatment therapy.
Referring to FIG. 27, when the software blanking timer has expired in step 2715, the algorithm resumes processing corresponding signals in step 2717. If the output ratio remains above the predetermined threshold 2211, indicating that detected activity is continuing as determined in step 2719, the ISI timer is reset and step 2721 determines if the ISI timer has expired. (The ISI timer sets a minimum ISI time between adjacent stimulation pulse trains). If the ISI timer has expired, another stimulation pulse may be applied to the selected electrode or group of electrodes (as executed by step 2709) in accordance with the determined treatment therapy. (Moreover, the determined treatment therapy may apply subsequent stimulation pulses that are separated by a time greater than the minimum ISI time.) If the ISI timer has not expired, step 2719 is repeated. In step 2719, if the seizure detection algorithm 800 determines that the output ratio drops below the predetermined threshold 2211, a cluster timer (corresponding to the time threshold 2215 in FIG. 22) is initiated in step 2723. The cluster timer is also reset in step 2723 If the cluster timer (e.g., corresponding to time threshold 2215) has expired, as determined by step 2725 after reaching step 2723, the end of the detection cluster is recognized in step 2729 and data that is collected during the cluster duration, as well as some prior period of data that may be of interest, may be stored in a loop recording (e.g., SRAM and flash memory 605) in step 2731. (The expiration of the cluster timer is indicative of a maximum time duration that the output threshold can be below a predetermined threshold, e.g., predetermined threshold 2211, while the detection cluster is occurring. In other words, if the output threshold is below the predetermined threshold and the cluster timer expires, process 2700 determines that the detection cluster has ended.) Step 2701 is then repeated. A subsequent detection cluster may occur during the seizure, causing steps 2705-2731 to be repeated.
The medical device system may support multi-modal operation, in which operation is modified in accordance with the configuration of the medical device system. One skilled in the art will recognize that the implanted system 10 (as shown in FIG. 12) may be more limited in functionality and features when compared to hybrid system (e.g., as shown in FIG. 9) due to the need to conserve the limited amount of energy available in a totally implanted system. One approach to expanding the capabilities is to incorporate a rechargeable battery in the implanted system 10. An alternative approach is to partition some of the features, particularly those that consume the most energy and are not used all the time, to the external portion 950 of a hybrid system or another external component of a hybrid system that may be associated with a process step, e.g., step 2025 of process 2000 as described in FIG. 20. (As previously discussed, FIG. 10 shows an exemplary embodiment of the external portion 950 with the associated programmer 1021.) In one embodiment, the medical device system operates as a closed-loop stimulator, in which stimulation therapy is adjusted in accordance with signal measurements and analysis that is preformed. The electronics and software required to operate the closed loop control reside in the external portion 950 of a hybrid system or the external wearable signal processor 1425 in a hybrid system with a telemetry booster stage or relaying module. However, if the closed-loop control is removed from the configuration, corresponding to a removal of the external portion 950, the medical device system may operate as an open loop stimulator, in which electrodes with associated implanted electronics generate stimulation therapy. An external component may be removed for different reasons. For example, the external portion 950 may be removed at times when monitoring or when therapy features executed by the external portion are not required, such as at night, for patients who do not require closed-loop control while sleeping. Also, an external component may be configured in a medical device system in order to invoke optional functionality or enhanced functionality, in which the external component interacts with an implanted component.
Rmin=Dmin=Imin=0, R max=6550, D max=65536(frames), I max=8.
RS(Y)=Round(100*(p 1 +p 2 +p 3)/3)where p 1 =P(R; R min , R max , {R j|cluster j is a TP}), p 2 =P(D; D min , D max , {D j|cluster j is a TP}),and p 3 =P(I; I min , I max , {I j|cluster j is a TP}).
P ⁡ ( x ; z min , z max , { z 1 , � ⁢ , z N } ) = ⁢ { ⁢ 0 ⁢ if ⁢ ⁢ x < z min ⁢ 1 N + 1 ⁢ ( x - z min z 1 - z min ) ⁢ if ⁢ ⁢ z min ≤ x < z 1 ⁢ 1 N + 1 ⁢ ( i + x - z i z i + 1 - z i ) ⁢ if ⁢ ⁢ z i < x < z i + 1 for ⁢ ⁢ some ⁢ ⁢ i ⁢ ⁢ 1 2 ⁢ ( N + 1 ) ⁢ ( 1 + ∑ j = 1 N ⁢ ( 1 { z j ≤ x } + 1 { z j < x } ) ⁢ if ⁢ ⁢ x = z i ⁢ ⁢ for ⁢ ⁢ some ⁢ ⁢ i ⁢ 1 N + 1 ⁢ ( N + x - z N z max - z N ) ⁢ if ⁢ ⁢ z N < x ≤ z max ⁢ 1 ⁢ if ⁢ ⁢ z max < x Here, 1{.} denotes the indicator function of the set {.}. In the second case where
P ⁡ ( x ; z min , z max , { } ) = min ⁢ { max ⁢ { x , z min } , z max } - z min z max - z min N = 0 ⁢ : FIGS. 28-33 depict various examples. As an example, suppose there are 4 scored True Positive detection clusters as follows:
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pp. 1223-1240.Referenced byCiting PatentFiling datePublication dateApplicantTitleUS82961082 Apr 201023 Oct 2012Yugen Kaisha Suwa TorasutoTime series data analyzer, and a computer-readable recording medium recording a time series data analysis programRotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services©2012 Google