Physiological signal-based encryption and EHR management

• US 9,626,521 B2
• Filed: 04/16/2015
• Issued: 04/18/2017
• Est. Priority Date: 04/16/2014
• Status: Active Grant

• Current Assignee: Arizona Board of Regents (University of Arizona)
• Original Assignee: Arizona Board of Regents (University of Arizona)
• Inventors: Gupta, Sandeep, Banerjee, Ayan
• Primary Examiner(s): Paliwal, Yogesh
• Application Number:
  US14/688,181
• Publication Number:
  US 20150304101A1
• Time in Patent Office: 733 Days
• Field of Search: 380 28
• CPC Class Codes: A61B 5/0004 characterised by the type of physiological signal transmitted
• CPC Class Codes: G06F 21/606 by securing the transmission between two devices or processes
• CPC Class Codes: G06F 2221/2107 File encryption
• CPC Class Codes: H04B 13/005 Transmission systems in which the medium consists of the human body
• CPC Class Codes: H04L 2209/34 Encoding or coding, e.g. Huffman coding or error correction
• CPC Class Codes: H04L 2209/805 Lightweight hardware, e.g. radio-frequency identification [RFID] or sensor
• CPC Class Codes: H04L 2209/88 Medical equipments
• CPC Class Codes: H04L 63/0428 wherein the data content is protected, e.g. by encrypting or encapsulating the payload
• CPC Class Codes: H04L 9/0866 involving user or device identifiers, e.g. serial number, physical or biometrical information, DNA, hand-signature or measurable physical characteristics
• CPC Class Codes: H04W 12/02 Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]
• CPC Class Codes: H04W 12/04 Key management, e.g. using generic bootstrapping architecture [GBA]
• CPC Class Codes: H04W 12/33 using wearable devices, e.g. using a smartwatch or smart-glasses
• CPC Class Codes: H04W 12/50 Secure pairing of devices
• CPC Class Codes: H04W 4/80 Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication

Abstract

Systems and methods are provided for encoding and decoding data (such as, for example, an encryption key) using a physiological signal. A data item string is separated into a defined number of component segments and each component segment is used as a coefficient of a polynomial equation. A plurality of signal features are then identified from a physiological signal and a plurality of ordered pairs are created based on the plurality of identified signal features using the polynomial equation. A data package including the plurality of ordered pairs and obfuscated by a plurality of chaff points is transmitted to another system. The receiver system uses a corresponding physiological signal to filter out the chaff points and to reconstruct the polynomial equation, for example, by LaGrangian interpolation. The coefficients of the reconstructed polynomial equation are then used to derive the encoded data item string.

Claims

1. A method of decoding a data item string from a receiving data package using a physiological signal, the method comprising:

identifying a plurality of signal features from a physiological signal;

accessing a plurality of data pairs from the data package;

identifying a subset of data pairs from the plurality of data pairs from the data package that correspond to one or more of the plurality of identified signal features from the physiological signal;

reconstructing a polynomial equation based on the subset of data pairs such that a second element in each data pair of the subset of data pairs corresponds to an output of the reconstructed polynomial equation when a corresponding first element in the data pair is used as an input to the reconstructed polynomial equation; and

deriving the data item string based on the coefficients of the reconstructed polynomial equation,

wherein the physiological signal is an ECG signal, and wherein identifying the plurality of signal features includes identifying an R-peak corresponding to each cardiac rhythm cycle.

2. The method of claim 1, further comprising detecting the physiological signal from a body of a user through a sensor system.

3. The method of claim 1, further comprising accessing a previously-stored physiological signal associated with a specific user from a non-transitory computer-readable memory, and wherein identifying the plurality of signal features from the physiological signal includes identifying a plurality of signal features from the accessed, previously-stored physiological signal associated with the specific user.

4. The method of claim 1, further comprising:

generating a synthesized physiological signal for a specific user based on a generative signal model initialized for the specific user, and

wherein identifying the plurality of signal features from the physiological signal includes identifying a plurality of signal features from the synthesized physiological signal.

5. The method of claim 4, wherein the act of generating a synthesized physiological signal for a specific user is performed by an electronic health records storage system.

6. The method of claim 5, further comprising:

receiving, by the electronic health records storage system, a subsequent data package;

using the data item string to decode data from the subsequent data package; and

storing the decoded data from the subsequent data package as an electronic health record on a non-transitory computer-readable memory of the electronic health records storage system.

7. The method of claim 1, further comprising:

identifying a plurality of chaff points from the plurality of data pairs from the data package, wherein the plurality of chaff points include points that do not correspond to any of the identified signal features from the physiological signal; and

disregarding the identified chaff points while reconstructing the polynomial equation.

8. The method of claim 1, wherein reconstructing the polynomial equation based on the subset of data pairs includes reconstructing the polynomial equation using LaGrangian interpolation.

9. The method of claim 1, wherein identifying the plurality of signal features includes performing a Fourier transformation on a physiological signal sample of a defined duration and identifying a plurality of peaks in the Fourier-transformed physiological signal.

10. The method of claim 1, further comprising receiving the data package from a PPG sensor, wherein the data points of the data package correspond to R-peaks detected in a PPG sensor signal.

11. The method of claim 1, further comprising receiving the data package from an EEG sensor system, wherein the data points of the data package correspond to components in the EEG signal corresponding to the R-peaks.

12. A method of decoding a data item string from a receiving data package using a physiological signal, the method comprising:

identifying a plurality of signal features from a physiological signal;

accessing a plurality of data pairs from the data package;

identifying a subset of data pairs from the plurality of data pairs from the data package that correspond to one or more of the plurality of identified signal features from the physiological signal;

reconstructing a polynomial equation based on the subset of data pairs such that a second element in each data pair of the subset of data pairs corresponds to an output of the reconstructed polynomial equation when a corresponding first element in the data pair is used as an input to the reconstructed polynomial equation; and

deriving the data item string based on the coefficients of the reconstructed polynomial equation

wherein the physiological signal is an EEG signal and further comprising;

detecting elements in the EEG signal that correspond temporally to an R-peak in an ECG signal;

generating a synthesized ECG signal based on the EEG signal; and

receiving the data package from an ECG sensor system,

wherein identifying the plurality of signal features includes performing a Fourier transformation on the synthesized ECG signal sample of a defined duration and identifying a plurality of peaks in the Fourier-transformed synthesized ECG signal, and

wherein the data points of the data package correspond to a plurality of peaks identified in a Fourier-transformed ECG signal detected by the ECG sensor system.

13. The method of claim 12, further comprising accessing a previously-stored physiological signal associated with a specific user from a non-transitory computer-readable memory, and wherein identifying the plurality of signal features from the physiological signal includes identifying a plurality of signal features from the accessed, previously-stored physiological signal associated with the specific user.

14. The method of claim 12, further comprising:

generating a synthesized physiological signal for a specific user based on a generative signal model initialized for the specific user, and

wherein identifying the plurality of signal features from the physiological signal includes identifying a plurality of signal features from the synthesized physiological signal.

15. The method of claim 14, wherein the act of generating a synthesized physiological signal for a specific user is performed by an electronic health records storage system.

16. The method of claim 15, further comprising:

receiving, by the electronic health records storage system, a subsequent data package;

using the data item string to decode data from the subsequent data package; and

storing the decoded data from the subsequent data package as an electronic health record on a non-transitory computer-readable memory of the electronic health records storage system.

17. The method of claim 12, further comprising:

identifying a plurality of chaff points from the plurality of data pairs from the data package, wherein the plurality of chaff points include points that do not correspond to any of the identified signal features from the physiological signal; and

disregarding the identified chaff points while reconstructing the polynomial equation.

18. The method of claim 12, wherein reconstructing the polynomial equation based on the subset of data pairs includes reconstructing the polynomial equation using LaGrangian interpolation.