IP Library › Granted Patent US 12,191,034
Granted Patent B2
US 12,191,034 · App. 18/605,638 · Granted Jan 7, 2025

Neuromodulation waveform watermarking and prescribing

Inventor: Sayed Emal Wahezi (New Rochelle, NY)
G16H40/67A61N1/36021A61N1/3603A61N1/36062A61N1/36071A61N1/36139G16H10/60G16H20/40
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Quick Facts
Patent No.
US 12,191,034
App. No.
18/605,638
Granted
Jan 7, 2025
Kind
B2
Abstract

Disclosed apparatus and associated methods relate to collecting signal data sampled using an input electrode contacting a patient while a neuromodulation device (NMD) applies a neuromodulation waveform using an output electrode contacting the patient, identifying characteristics of the applied neuromodulation waveform determined as a function of the collected signal data, and generating a notification if the applied neuromodulation waveform matches any known predetermined neuromodulation waveform, based on the identified characteristics. The applied neuromodulation waveform may include waveform identification data. The waveform identification data may be a watermark added by the NMD. Identification data may be encoded by varying amplitude or timing of the applied neuromodulation waveform or by modulating a carrier wave added to the applied neuromodulation waveform. An implementation may advantageously detect use of proprietary neuromodulation waveforms in real time, permitting automatic invoicing and treatment protocol conformance verification triggered by usage detection and increasing access to effective neuromodulation waveforms.

Claims (43)

1. A method comprising:

receiving, by a waveform prescription deployment server ( 1625 ), a digital prescription for a neuromodulation device (NMD) to apply one or more prescription waveform protocols ( 1605 ) to a patient ( 115 );

sending, by the waveform prescription deployment server ( 1625 ), to a computing device associated with the NMD, a request for patient ( 115 ) authentication by the computing device associated with the NMD;

in response to receiving, by the waveform prescription deployment server ( 1625 ), an indication of successful patient ( 115 ) authentication, generating and sending, by the waveform prescription deployment server ( 1625 ) to the computing device associated with the NMD, a prescription activation data package activating the NMD to apply the one or more prescription waveform protocols ( 1605 ) to the patient ( 115 ), wherein the waveform prescription deployment server ( 1625 ) generates a shared secret during registration, based on a trusted public key received from the computing device associated with the NMD, and wherein the NMD activates to apply the one or more prescription waveform protocols ( 1605 ) to the patient ( 115 ) when a correct TOTP is received from the computing device associated with the NMD; and

sending, by the waveform prescription deployment server ( 1625 ) to a waveform identification server ( 1640 ), an electronic message configured to cause the waveform identification server ( 1640 ) to determine if signal characteristics of the one or more prescription waveform protocols ( 1605 ) applied to the patient ( 115 ) match signal characteristics of any of a selection of known waveform protocols ( 133 ) from a library of individually available known waveforms ( 121 ), using neuromodulation waveform data collected using at least one input electrode in contact with the patient ( 115 ) while the one or more prescription waveform protocols are applied to the patient ( 115 ) by the NMD using at least one output electrode in contact with the patient ( 115 ).

2. The method of claim 1 , wherein the NMD is an implantable pulse generator (IPG) 106 or a wearable pulse generator (WPG) 1610 .

3. The method of claim 1 , wherein the method further comprises receiving, by the waveform prescription deployment server ( 1625 ), a registration request uniquely identifying the patient ( 115 ) and the NMD, wherein the registration request further comprises a trusted public key associated with the NMD, and wherein the registration request is received from the computing device associated with the NMD.

4. The method of claim 1 , wherein the computing device associated with the NMD is a mobile device ( 118 ) configured to be operably coupled with the NMD.

5. The method of claim 1 , wherein the method further comprises receiving, by the waveform prescription deployment server ( 1625 ), from the NMD or the computing device associated with the NMD, a request for an approval to treat the patient ( 115 ) using the one or more prescription waveform protocols ( 1605 ) for a predetermined time.

6. The method of claim 5 , wherein the request for the approval to treat the patient ( 115 ) further comprises a request to use the one or more prescription waveform protocols ( 1605 ) within a radius of a location determined based on location sensor data captured by the NMD or the computing device associated with the NMD.

7. The method of claim 5 , wherein the method further comprises sending, by the waveform prescription deployment server ( 1625 ), to the NMD or the computing device associated with the NMD, the approval to treat the patient ( 115 ) using the one or more prescription waveform protocols ( 1605 ).

8. The method of claim 1 , wherein the prescription activation data package further comprises an indication of a default waveform protocol and a prescription expiration time, and the NMD is configured to cease applying the one or more prescription waveform protocols ( 1605 ) and begin applying the default waveform protocol when: the prescription expiration time is reached or when the NMD moves outside a radius of an NMD location configured in the NMD, wherein when the NMD moves outside the configured radius is determined as a function of location sensor data captured by the NMD or the computing device associated with the NMD.

9. The method of claim 1 , wherein the method further comprises downloading the one or more prescription waveform protocols ( 1605 ) to the NMD using an application deployed to the computing device associated with the NMD.

10. The method of claim 1 , wherein patient ( 115 ) authentication further comprises the computing device associated with the NMD receiving and verifying patient ( 115 ) time-based one-time-password (TOTP) input against a TOTP configured to change at least once per hour, wherein a correct TOTP is determined as a function of time and a shared secret generated by the waveform prescription deployment server ( 1625 ), and wherein the TOTP is generated based on RFC 4226 or RFC 6238.

11. The method of claim 1 , wherein the method further comprises the NMD refraining from applying the one or more prescription waveform protocols ( 1605 ) to the patient ( 115 ) unless a correct TOTP is received from the computing device associated with the NMD, using the NMD.

12. The method of claim 1 , wherein receiving the indication of successful patient ( 115 ) authentication further comprises receiving and validating a security token generated by the computing device associated with the NMD in response to user biometric input verified against a stored biometric template, wherein the security token is derived by the computing device associated with the NMD from the successful patient ( 115 ) authentication and the shared secret.

13. The method of claim 1 , wherein patient ( 115 ) authentication further comprises receiving patient ( 115 ) biometric input and verifying the patient ( 115 ) biometric input against a stored biometric template, using the computing device associated with the NMD.

14. The method of claim 13 , wherein the patient ( 115 ) biometric input and the stored biometric template further comprises data captured from at least one of: a fingerprint for fingerprint identification, a voice for speaker identification, a face for facial recognition, a retina for eye identification or a gesture.

15. The method of claim 1 , wherein the method further comprises receiving, by the waveform identification server ( 1640 ), collected neuromodulation waveform data comprising sampled waveform signal data or measured waveform signal characteristics, the neuromodulation waveform data collected while one or more waveforms were applied to the patient ( 115 ).

16. The method of claim 15 , wherein the collected neuromodulation waveform data was collected using at least one input electrode in contact with the patient ( 115 ) while the one or more waveforms were applied to the patient ( 115 ) using at least one output electrode in contact with the patient ( 115 ).

17. The method of claim 15 , wherein the method further comprises subtracting or attenuating, from the collected neuromodulation waveform data, at least one electrically evoked compound action potential (ECAP) signal resulting from the one or more waveforms that were applied to the patient ( 115 ).

18. The method of claim 15 , wherein the method further comprises identifying, in the one or more waveforms applied to the patient ( 115 ), waveform signal characteristics ( 1630 ) determined as a function of the collected neuromodulation waveform data, using at least one digital signal processing (DSP) algorithm ( 1635 ).

19. The method of claim 18 , wherein the method further comprises determining if the one or more waveforms applied to the patient ( 115 ) match any known predetermined waveforms, based on comparing the identified waveform signal characteristics ( 1630 ) of the one or more waveforms applied to the patient ( 115 ) to waveform signal characteristics ( 1630 ) of any known predetermined waveforms.

20. The method of claim 19 , wherein the method further comprises: in response to determining the one or more waveforms applied to the patient ( 115 ) matched any known predetermined waveform based on comparing the identified waveform signal characteristics ( 1630 ), generating a notification indicating the one or more waveforms applied to the patient ( 115 ) matched at least one known predetermined waveform, determined as a function of matched signal characteristics, and wherein the method further comprises sending the notification to the waveform prescription deployment server ( 1625 ), the NMD or the computing device associated with the NMD.

21. The method of claim 15 , wherein the method further comprises: determining if any known digital watermark is detected in the one or more waveforms applied to the patient ( 115 ), determined as a function of the collected neuromodulation waveform data, using at least one watermark detection algorithm ( 1655 ), and wherein in response to determining at least one known digital watermark is detected in the one or more waveforms applied to the patient ( 115 ), the method further comprises generating and sending a notification that at least one known digital watermark was detected in the one or more waveforms applied to the patient ( 115 ), and wherein determining if any known digital watermark is detected further comprises demodulating a binary sequence from the collected neuromodulation waveform data and comparing the demodulated binary sequence to at least one known digital watermark.

22. The method of claim 21 , wherein demodulating the binary sequence further comprises decoding information encoded with amplitude modulation (AM), frequency modulation (FM) or pulse position modulation (PPM) in the one or more waveforms applied to the patient ( 115 ).

23. The method of claim 21 , wherein demodulating the binary sequence further comprises decoding a forward error correction (FEC) code and using the FEC code to verify and/or restore integrity of the binary sequence, and wherein the FEC code is selected from the group consisting of Reed-Solomon, Golay, Hamming and Bose-Chaudhuri-Hocquenghem (BCH).

24. The method of claim 1 , wherein the method further comprises:

receiving, from the waveform identification server ( 1640 ), an indication that a known predetermined waveform is in use to treat a patient ( 115 ) by the NMD, using the computing device associated with the NMD;

authenticating the patient ( 115 ), comprising receiving and verifying patient ( 115 ) biometric or security credential input, using the computing device associated with the NMD;

in response to determining patient ( 115 ) authentication was successful, generating a security token derived from the successful patient ( 115 ) authentication and a shared secret distributed by the waveform prescription deployment server ( 1625 ), using the computing device associated with the NMD;

sending, to the waveform prescription deployment server ( 1625 ), the security token with a request for approval for the patient ( 115 ) to continue using the known predetermined waveform, using the computing device associated with the NMD;

receiving, from the waveform prescription deployment server ( 1625 ), an approval for the patient ( 115 ) to continue using the known predetermined waveform, using the computing device associated with the NMD, the approval comprising an activation expiration time and a time-based one-time-password (TOTP) generated by the prescription deployment server ( 1625 ) based on the security token and the shared secret, wherein the shared secret is retained by the waveform prescription deployment server ( 1625 ) and the computing device associated with the NMD; and

sending the TOTP to the NMD, wherein the NMD is configured to be activated by the TOTP to continue applying the known predetermined waveform to the patient ( 115 ) until the activation expiration time.

25. The method of claim 1 , wherein the method further comprises:

applying a first neuromodulation waveform to a patient ( 115 ), using an NMD;

collecting waveform signal data sampled while the first neuromodulation waveform is applied to the patient ( 115 ), using the NMD;

applying a digital signal processing (DSP) algorithm ( 1635 ) to the collected waveform signal data to identify waveform signal characteristics ( 1630 ) of the first waveform, using the NMD;

determining that the first neuromodulation waveform, based on the identified waveform signal characteristics ( 1630 ), matches a second neuromodulation waveform, the second neuromodulation waveform being a known predetermined neuromodulation waveform, using the NMD; and

generating a notification that the first neuromodulation waveform being applied to the patient ( 115 ) is a known predetermined waveform, using the NMD.

26. The method of claim 1 , the method further comprises applying a digital watermark to the one or more prescription waveform protocols ( 1605 ) based on encoding digital watermark data as a binary sequence and modulating the binary sequence in the one or more prescription waveform protocols ( 1605 ) using amplitude modulation (AM), frequency modulation (FM), phase modulation (PM) or pulse position modulation (PPM).

27. The method of claim 26 , wherein modulating the binary sequence further comprises adding a carrier wave ( 2505 ) having an amplitude of 10% or less of paresthesia (0.1-0.2 mA) to at least one waveform of the one or more prescription waveform protocols ( 1605 ).

28. The method of claim 1 , wherein the NMD is a WPG ( 1610 ) in contact with the patient ( 115 ) exterior, and wherein the one or more prescription waveform protocols ( 1605 ) are applied to the patient ( 115 ) to create a current directly over a targeted nerve to move a limb or digits.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 12, 2025
From: WAHEZI, SAYED EMAL
To: UBIQUISTIM HOLDING LLC
Reel/Frame 071993/0662 →
Continuity (3)
Continuation In Part 18229743 · Aug 3, 2023
Provisional Application 63456806 · Apr 3, 2023
Related Publication 20240331858A1 · Oct 3, 2024
References Cited (99)
US 5961467A · Shimazu et al. · 1999 [cited by applicant]
US 6923770B2 · Narimatsu · 2005 [cited by applicant]
US 7192402B2 · Amano et al. · 2007 [cited by applicant]
US 7483747B2 · Gliner et al. · 2009 [cited by applicant]
US 7489966B2 · Leinders et al. · 2009 [cited by applicant]
US 8099164B2 · Gillberg et al. · 2012 [cited by applicant]
US 8244360B2 · Heruth et al. · 2012 [cited by applicant]
US 8380318B2 · Kishawi et al. · 2013 [cited by applicant]
US 8473063B2 · Gupta et al. · 2013 [cited by applicant]
US 8612018B2 · Gillbe · 2013 [cited by applicant]
US 8773239B2 · Phillips et al. · 2014 [cited by applicant]
US 8862214B2 · Ghodrati · 2014 [cited by applicant]
US 9199089B2 · Perryman et al. · 2015 [cited by applicant]
US 9492667B1 · Kent et al. · 2016 [cited by applicant]
US 9504832B2 · Lubbus et al. · 2016 [cited by applicant]
US 9737717B2 · Moffitt et al. · 2017 [cited by applicant]
US 10667747B2 · Annoni et al. · 2020 [cited by applicant]
US 10758732B1 · Heldman · 2020 [cited by examiner]
US 10905894B2 · Karpf · 2021 [cited by applicant]
US 10940311B2 · Gozani · 2021 [cited by examiner]
US 11154710B2 · Belson et al. · 2021 [cited by applicant]
US 11170793B2 · Jin et al. · 2021 [cited by applicant]
US 11623092B2 · Peyman et al. · 2023 [cited by applicant]
US 11854682B2 · Lin et al. · 2023 [cited by applicant]
US 20040186386A1 · Kolluri et al. · 2004 [cited by applicant]
US 20160038048A1 · Ting et al. · 2016 [cited by applicant]
US 20160287110A1 · Morris et al. · 2016 [cited by applicant]
US 20170003046A1 · Gould · 2017 [cited by applicant]
US 20170095670A1 · Ghaffari · 2017 [cited by examiner]
US 20170157410A1 · Moffitt · 2017 [cited by examiner]
US 20170304636A1 · Steinke et al. · 2017 [cited by applicant]
US 20170359339A1 · Hevizi · 2017 [cited by examiner]
US 20190065731A1 · Brocious · 2019 [cited by examiner]
US 20190111255A1 · Errico · 2019 [cited by examiner]
US 20200384270A1 · Doan · 2020 [cited by examiner]
US 20210299446A1 · Errico et al. · 2021 [cited by applicant]
US 20220096822A1 · Schepis et al. · 2022 [cited by applicant]
US 20220203107A1 · Nobles et al. · 2022 [cited by applicant]
US 20230060761A1 · Doan · 2023 [cited by applicant]
US 20230121038A1 · John et al. · 2023 [cited by applicant]
AU 2013306411B2 · 2016 [cited by examiner]
CN 106512208A · 2017 [cited by applicant]
EP 3747507B1 · 2023 [cited by applicant]
WO 8707511A2 · 1987 [cited by applicant]
Peng et al., “Mechanisms and Applications of Neuromodulation Using Surface Acoustic Waves—A Mini-Review”, Frontiers in Neuroscience, vol. 15, Article 629056, Jan. 2021, 10 pages. [cited by applicant]
Ploner et al., “Brain Rhythms of Pain”, Trends in Cognitive Sciences, Feb. 2017, vol. 21, No. 2 http://dx.doi.org/10.1016/j.tics.2016.12.001. [cited by applicant]
Potas et al., “Waveform Similarity Analysis: A Simple Template Comparing Approach for Detecting and Quantifying Noisy Evoked Compound Action Potentials”, PLOS One | DOI:10.1371/journal.pone.0136992 Sep. 1, 2015, 18 page… [cited by applicant]
Pouromran et al., “Exploration of physiological sensors, features, andmachine learning models for pain intensity estimation”, PLOS One | https://doi.org/10.1371/journal.pone.0254108 Jul. 9, 2021, 17 pages. [cited by applicant]
Quintana et al., “Considerations in the assessment of heart rate variability in biobehavioral research”, Frontiers in Neuroscience, vol. 5, Article 805, Jul. 2014, 10 pages. [cited by applicant]
Rojas et al., “A systematic review of neurophysiological sensing for the assessment of acute pain”, Digital Medicine (2023) 6:76 ; https://doi.org/10.1038/s41746-023-00810-1. [cited by applicant]
Rojas et al., “Multimodal physiological sensing for the assessment of acute pain”, Frontiers in Pain Research, Jun. 19, 2023, 11 pages. [cited by applicant]
Sacco et al., “The Relationship Between Blood Pressure and Pain”, The Journal of Clinical Hypertension vol. 15, No. 8, Aug. 2013, pp. 600-605. [cited by applicant]
Sebastiao et al., “Analysis of Physiological Responses during Pain Induction”, Sensors 2022, 22, 9276. https://doi.org/10.3390/s22239276. [cited by applicant]
Tennant, “Treat the Pain . . . Save a Heart”, MedCentral, vol. 10, Issue 8, Feb. 25, 2011. 6 pages. [cited by applicant]
Tousignant-Laflamme et al., “Establishing a Link Between Heart Rate and Pain in Healthy Subjects: A Gender Effect”, The Journal of Pain, vol. 6, No. 6, Jun. 2005: pp. 341-347. [cited by applicant]
Trautmann et al., “Design, Calibration, and Evaluation of Real-Time Waveform Matching on an FPGA-based Digitizer at 10 GS/s”, ACM Transactions on Reconfigurable Technology and Systems vol. 17Issue 2Article No. 24pp. 1-2… [cited by applicant]
Trautmann et al., “Real-Time Waveform Matching with a Digitizer at 10 GS/s”, 2022 32st International Conference on Field-Programmable Logic and Applications, 8 pages. [cited by applicant]
Verrills et al., “A review of spinal cord stimulation systems for chronic pain”, Journal of Pain Research 2016:9 481-492. [cited by applicant]
Wahezi et al., “Current Waveforms in Spinal Cord Stimulation and Their Impact on the Future of Neuromodulation: A Scoping Review”, Neuromodulation: Technology at the Neural Interface vol. 27, Issue 1, Jan. 2024, pp. 47-… [cited by applicant]
Wang, “5 Basics of EEG 101: Data Collection, Processing & Analysis”, Best Practice, Apr. 20, 2021, 11 pages. [cited by applicant]
Yang et al., “Continuous Pain Assessment Using Ensemble Feature Selection from Wearable Sensor Data”, Proceedings (IEEE Int Conf Bioinformatics Biomed). Nov. 2019 ; 2019: 569-576. doi:10.1109/bibm47256.2019.8983282. [cited by applicant]
Yao, “A brief tutorial of the Waveform Matched Filter Technique”, Georgia Tech, Feb. 21, 2016, 14 pages. [cited by applicant]
Yoshida et al., “Analgesia nociception index and high frequency variability index: promising indicators of relative parasympathetic tone”, Journal of Anesthesia (2023) 37:130-137, https://doi.org/10.1007/s00540-022-0312… [cited by applicant]
Yu et al., “To tailor the conical beam by using planar superstrate”, Electronics Letters, vol. 57, No. 1, Jan. 2021, pp. 1-44. [cited by applicant]
Claron, et al., “The Supplementary Eye Field Tracks Cognitive Efforts,” bioRxiv The preprint server for Biology, Cold Spring Harbor Laboratory, Jan. 25, 2021, 22 pages, doi: https://doi.org/10.1101/2021.01.14.426722. [cited by applicant]
Abbott, “Proclaim™ XR SCS System and Proclaim™ DRG Therapy, Patient Controller App User Guide”, 2020, 28 pages. [cited by applicant]
Abdullayev et al., “Analgesia Nociception Index: assessment of acute postoperative pain”, Rev Bras Anestesiol., 2019, 69(4), pp. 396-402. [cited by applicant]
Baliki et al., “Chronic Pain and the Emotional Brain: Specific Brain Activity Associated with Spontaneous Fluctuations of Intensity of Chronic Back Pain”, The Journal of Neuroscience, Nov. 22, 2006 ⋅ 26(47):12165-12173. [cited by applicant]
Boston Scientific, “Vercise Genus Deep Brain Stimulation System”, 2023, 4 pages. [cited by applicant]
Butson, “Computational Models of Neuromodulation”, International Review of Neurobiology, vol. 107, 2012 ISSN 0074-7742. [cited by applicant]
Chanques et al., “Analgesia nociception index for the assessment of pain in critically ill patients: a diagnostic accuracy study”, British Journal of Anaesthesia, 119 (4): 812-20 (2017). [cited by applicant]
Culaclii et al., “A Biomimetic, SoC-Based Neural Stimulator for Novel Arbitrary-Waveform Stimulation Protocols”, Frontiers in Neuroscience, vol. 15, Jul. 2021, pp. 1-17. [cited by applicant]
Dayoub et al., “Does Pain Lead to Tachycardia? Revisiting the Association Between Self-reported Pain and Heart Rate in a National Sample of Urgent Emergency Department Visits”, Mayo Clin Proc. Aug. 2015 ; 90(8): 1165-11… [cited by applicant]
Dinsmoor et al., “Using evoked compound action potentials to quantify differential neural activation with burst and conventional, 40 Hz spinal cord stimulation in ovines”, Pain Reports, vol. 7, 2022, pp. 1-10. [cited by applicant]
Erdy et al., “Preliminary Intraoperative Validation of the Nociception Level Index”, Anesthesiology, vol. 125, No. 1, Jul. 2016, pp. 193-203. [cited by applicant]
Ferguson et al., “Wireless communication with implanted medical devices using the conductive properties of the body”, Expert Rev Med Devices, vol. 8, No. 4, 2011, pp. 427-433. [cited by applicant]
Forte et al., “Heart Rate Variability and Pain: A Systematic Review”, Brain Sci. vol. 12, 2022, 25 pages. [cited by applicant]
Heathers, Everything Hertz: methodological issues in short-term frequency-domain HRV:, Frontiers in Physiology, vol. 5, Article 177, May 2014, 15 pages. [cited by applicant]
Heros et al., “Objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic pain”, Bioelectronic Medicine, vol. 9, No. 13, 2023, 13 pages. [cited by applicant]
Kardan et al., “Supplementary materials for: Distinguishing cognitive effort and working memory load using scale-invariance and alpha suppression in EEG”, vol. 211, May 2020, 116622. [cited by applicant]
Kim et al., “Pain Assessment Using the Analgesia Nociception Index (ANI) in Patients Undergoing General Anesthesia: A Systematic Review and Meta-Analysis”, J. Pers. Med. 2023, 13, 1461. [cited by applicant]
Kimszal, “Can Pain Cause High Blood Pressure?”, Verywell Health, May 7, 2023, 6 pages. [cited by applicant]
Korving et al., “Physiological Measures of Acute and Chronic Pain within Different Subject Groups: A Systematic Review”, Pain Research and Management vol. 2020, Article ID 9249465, 10 pages. [cited by applicant]
Kriek et al., “Preferred frequencies and waveforms for spinal cord stimulation in patients with complex regional pain syndrome: A multicentre, double-blind, randomized and placebo-controlled crossover trial”, Eur J Pain… [cited by applicant]
Physical Computing, Lesson 2: Comparing Signals (Time Domain), Mar. 12, 2024, 1 page. [cited by applicant]
Ledowski et al., “Analgesia nociception index: evaluation as a new parameter for acute postoperative pain”, British Journal of Anaesthesia 111 (4): 627-9 (2013). [cited by applicant]
Li et al., “Focal Mechanism Determination Using High Frequency Waveform Matching and Its Application to Small Magnitude Induced Earthquakes”, Geophysical Journal International, vol. 184, Issue 3, Mar. 2011, pp. 1261-127… [cited by applicant]
Ma et al., “Template matching for simple waveforms with low signa-lto-noise ratio and its application to icequake detection”, Earthq Sci (2020)33: 256-263. [cited by applicant]
Martini et al., “Ability of the Nociception Level, a Multiparameter Composite of Autonomic Signals, to Detect Noxious Stimuli during Propofol-Remifentanil Anesthesia”, Anesthesiology, vol. 123, No. 3, Sep. 2015, pp. 524… [cited by applicant]
Medasense, “Introducing the NOL® (Nociception Level) Index Algorithm A Technical Overview”, 2022, 11 pages. [cited by applicant]
Meijer et al., “Reduced postoperative pain using Nociception Level-guided fentanyl dosing during sevoflurane anaesthesia: a randomised controlled trial”, British Journal of Anaesthesia, 125 (6): 1070e1078 (2020). [cited by applicant]
Mirza et al., “Closed-Loop Implantable Therapeutic Neuromodulation Systems Based on Neurochemical Monitoring”, Frontiers in Neuroscience, vol. 13, Article 808, Aug. 2019, pp. 1-18. [cited by applicant]
Naranjo-Hernandez, “Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review”, Sensors 2020, 20, 365; doi:10.3390/s20020365. [cited by applicant]
Nelson et al., “Wireless Technologies for Implantable Devices”, Sensors (Basel), vol. 20 (16) Aug. 2020, 27 pages. [cited by applicant]
Nijhuis et al., “First Report on Real-World Outcomes with Evoked Compound Action Potential (ECAP)-Controlled Closed-Loop Spinal Cord Stimulation for Treatment of Chronic Pain”, Pain Ther (2023) 12:1221-1233. [cited by applicant]
O'Leary et al., “NURIP: Neural Interface Processor for Brain-State Classification and Programmable-Waveform Neurostimulation”, IEEE Journal of Solid-State Circuits, 2018, 13 pages. [cited by applicant]
Osborne, “Scientists Decode Brain Waves Linked to Chronic Pain”, Smithsonianmag.com, May 24, 2023, 4 pages. [cited by applicant]
Parker et al., “Evoked Compound Action Potentials Reveal Spinal Cord Dorsal Column Neuroanatomy”, Basic Research, vol. 23, issue 1, Jan. 2020, pp. 82-95. [cited by applicant]
Patterson et al., “Objective wearable measures correlate with self-reported chronic pain levels in people with spinal cord stimulation systems”, npj Digital Medicine (2023) 6:146 ; https://doi.org/10.1038/s41746-023-008… [cited by applicant]