DEVICE AND METHOD FOR DETERMINING SPINAL CORD STIMULATION EFFICACY

• US 20160213314A1
• Filed: 09/29/2014
• Published: 07/28/2016
• Est. Priority Date: 09/29/2013
• Status: Active Grant

• Current Assignee: Medasense Biometrics Ltd.
• Original Assignee: Medasense Biometrics Ltd.
• Inventors: Racheli, Noam, Ben-Israel, Nir, Zuckerman-Stark, Galit
• Granted Patent: US 10,231,666 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0075 by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy A61B5/0071 takes precedence
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/0533 Measuring galvanic skin response
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/0836 Measuring rate of CO2 production
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/398 Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
• CPC Class Codes: A61B 5/4824 Touch or pain perception evaluation A61B5/4821 takes precedence
• CPC Class Codes: A61B 5/4836 Diagnosis combined with treatment in closed-loop systems or methods A61B5/0036 takes precedence
• CPC Class Codes: A61B 5/4848 Monitoring or testing the effects of treatment, e.g. of medication
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61N 1/36071 Pain
• CPC Class Codes: A61N 1/36135 using physiological parameters
• CPC Class Codes: A61N 1/36139 with automatic adjustment
• CPC Class Codes: A61N 1/37241 : providing test stimulations
• CPC Class Codes: A61N 1/37247 : User interfaces, e.g. input or presentation means
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Device and method for determining an efficacy of chronic pain treatment including providing a first set of at least one stimulus to a subject, obtaining first measurements of at least two physiological parameters in response to the first set of at least one stimulus, providing chronic pain treatment to the subject, providing a second set of at least one stimulus to the subject, obtaining second measurements of the at least two physiological parameters in response to the second set of at least one stimulus; and determining an efficacy of the chronic pain treatment by applying a classification algorithm on the first and second measurements of the at least two physiological parameters.

Claims

1. A method for determining an efficacy of spinal cord stimulation (SCS) treatment in a subject with chronic pain, the method comprising:

providing a first set of at least one stimulus to the subject;

obtaining first measurements of at least two physiological parameters in response to the first set of at least one stimulus;

providing SCS treatment to the subject;

providing a second set of at least one stimulus to the subject;

obtaining second measurements of the at least two physiological parameters in response to the second set of at least one stimulus and the SCS treatment; and

determining an efficacy of the SCS treatment by applying a classification algorithm on the first and second measurements of the at least two physiological parameters, obtained in response to the first and second set of at least one stimulus, respectively.

2. The method of claim 1, wherein applying the classification algorithm comprises directly or indirectly comparing the first and second measurements to pre-stored data sets of measurements obtained from subjects with known SCS treatment efficacies.

3. The method of claim 1, wherein determining the efficacy of the SCS treatment is further based on patient demographic data.

4. The method of claim 1, wherein providing the SCS treatment comprises providing a trial SCS treatment;

wherein the trial SCS treatment comprises utilizing a temporary spinal cord stimulator comprising stimulating electrodes without implanting an electrical pulse generator.

7. The method of claim 4, further comprising predicting a treatment efficacy of long-term SCS treatment based on the determined efficacy of the trial SCS treatment.

8. The method of claim 7, further comprising providing a treatment recommendation based on the predicted treatment efficacy.

9. The method of claim 1, wherein the at least one stimulus is selected from:

painful stimulus on non-painful area, painful stimulus on painful area, non-painful stimulus on non-painful area, non-painful stimulus on painful area.

10. The method of claim 9, wherein a source of the at least one stimulus is selected from tetanic stimulus, thermal stimulus, pressure stimulus, touch stimulus, electric stimulus, mechanical stimulus, proprioception stimulus or combinations thereof.

11. The method of claim 9, wherein the painful stimulus and the non-painful stimulus is of a different or a same source.

12. The method of claim 1, wherein the at least two physiological parameters are selected from the group consisting of PPG Peak (P) amplitude, mean PPG Peak (P) amplitude, standard deviation (std) of PPG Peak (P) amplitude, Trough (T) amplitude, mean Trough (T) amplitude, std of Trough (T) amplitude;

PPG dicrotic notch (N) amplitude, mean dicrotic notch (N) amplitude, std of dicrotic notch (N) amplitude, PPG peak to peak time intervals, PPG peak to peak interval mean, PPG peak to peak interval std;

power spectrum of the PPG peak to peak intervals;

VLF Power, LF Power and HF Power;

GSR amplitude, GSR mean amplitude, GSR amplitude std;

GSR Peak (P) amplitude, mean Peak (P) amplitude and Peak (P) amplitude std;

GSR peak to peak time intervals, mean GSR peak to peak time interval;

GSR peak to peak time intervals std;

Phasic EDA;

amplitude, mean amplitude and std of amplitude, Temperature amplitude, mean amplitude and std of amplitude;

Temp Peak (P) amplitude, mean amplitude and std of amplitude;

Temperature peak to peak time intervals, mean and std (variability) of interval;

ECG to PPG Pulse Transition time;

PPG to PPG Pulse Transition time;

ECG R to R time intervals, mean and std (variability) of intervals;

Power of VLF, LF and HF frequency bands of power spectrum of the ECG R to R intervals (heart rate variability);

Upper peak amplitude, mean amplitude and STD of amplitude;

Respiratory rate, mean rate and std rate;

Power of the frequency bands of power spectrum of EMG signal;

EMG Power Spectrum Mean frequency;

EMG Power Spectrum Highest Peak Frequency;

Power of the alpha, beta, gamma, delta, theta frequency bands of power spectrum of EEG/FEMG signal;

EMG Power Spectrum Mean frequency;

EMG Power Spectral edge frequency;

Coherence between 2 or more EEG/FEMG channels;

frequency of movement, axis of movement and any combination thereof.

13. The method of claim 1, wherein the at least two physiological parameters are selected from the group consisting of PPG amplitude, PPG amplitude variation, pulse rate (PR) interval, PR variability, GSR fluctuations or any combination thereof.

14. The method of claim 1, wherein the at least two physiological parameters are derived from at least one physiological signal selected from photoplethysmograph (PPG), galvanic skin response (GSR), electrocardiogram (ECG), blood pressure, respiration, internal body temperature, skin temperature, electrooculography (EOG), pupil diameter, electroencephalogram (EEG), frontalis electromyogram (FEMG), electromyography (EMG), electro-gastro-gram (EGG), laser Doppler velocimetry (LDV), dynamic light scattering (DLS), near-infrared spectroscopy (NIRS), partial pressure of carbon dioxide, and accelerometer readings.

15. The method of claim 14, wherein the at least two physiological parameters are derived from a photoplethysmograph (PPG) signal and a galvanic skin response signal.

16. The method of claim 1, comprising obtaining at least three physiological parameters.

5. (canceled)

6. (canceled)

17. (canceled)

18. A method for calibrating spinal cord stimulation (SCS) treatment in a subject, the method comprising:

providing a SCS treatment being characterized by at least one SCS parameter;

varying a value of one SCS parameters along a dynamic range thereof;

obtaining measurements of at least two physiological parameters in response to varying the value of the one SCS parameter along the dynamic range thereof;

determining an efficacy of the SCS treatment along the dynamic range of the varied SCS parameter by applying a classification algorithm to the at least two physiological parameters obtained in response to varying the one SCS parameter along the dynamic range thereof; and

selecting the value of the one SCS parameter yielding the highest efficacy.

19. The method of claim 18, wherein remaining SCS parameters are fixed while varying the one SCS parameter.

20. The method of claim 18, wherein the at least one SCS parameter comprises type of stimulation, stimulation frequency, duration, pulse width, intensity, waveform, wave pattern, signal, amplitude, onset timing, delay, treatment length, treatment period, onset delay or any combination thereof.

21. The method of claim 18, wherein varying the SCS parameter along the dynamic range, comprises making continuous, incremental and/or step wise changes in the value of the SCS parameter.

22. A system for determining efficacy of a SCS treatment, the system comprising a processor configured to:

obtain a first measurement of at least two physiological parameters in response to a first set of at least one stimulus;

obtain a second measurements of the at least two physiological parameters in response to a second set of the at least one stimulus and a SCS treatment; and

determine an efficacy of the SCS treatment by applying a classification algorithm on the first and second measurements of the at least two physiological parameters, obtained in response to the first and second set of at least one stimulus.

23. The system of claim 22, further comprising a stimulus evoking device configured to provide at least one stimulus to the subject.

24. The system of claim 22, further comprising a SCS device.