Machine-based patient-specific seizure classification system

• US 9,848,793 B2
• Filed: 02/18/2014
• Issued: 12/26/2017
• Est. Priority Date: 02/15/2013
• Status: Active Grant

• Current Assignee: Khalifa University Of Science, Technology & Research
• Original Assignee: Masdar Institute Of Science And Technology
• Inventors: Yoo, Jerald, Altaf, Muhammad Awais Bin
• Primary Examiner(s): BERHANU, ETSUB D
• Application Number:
  US14/182,570
• Publication Number:
  US 20140235990A1
• Time in Patent Office: 1,407 Days
• Field of Search: None
• CPC Class Codes: A61B 2503/04 Babies, e.g. for SIDS detection
• CPC Class Codes: A61B 2503/06 Children, e.g. for attention deficit diagnosis
• CPC Class Codes: A61B 5/0036 including treatment, e.g., using an implantable medical device, ablating, ventilating
• CPC Class Codes: A61B 5/291 for electroencephalography [EEG]
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/4836 Diagnosis combined with treatment in closed-loop systems or methods A61B5/0036 takes precedence
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence

Abstract

This disclosure is directed to a machine-based patient-specific seizure classification system. In general, an example system may comprise a non-linear SVM seizure classification system-on-chip (SoC) with multichannel EEG data acquisition and storage for epileptic patients is presented. The SoC may integrate a hardware-efficient log-linear Gaussian Basis Function engine, floating point piecewise linear natural log, and low-noise, high dynamic range readout circuits. In at least one example implementation, the SoC may consume 1.83 μJ/classification while classifying 8 channel results with an average detection rate, average false alarm rate and latency of 95.1%, 0.94% and <2 s, respectively.

Claims

1. A device, comprising:

an analog front-end (AFE) to generate at least one analog signal representative of electroencephalography (EEG) information;

a digital back end (DBE) including a feature extraction (FE) engine and a non-linear support vector machine (NL-SVM) classification engine comprising circuitry configured to perform an antilog function on a summation of a first input and a log conversion of a second input to determine onset of a seizure based on at least one digital signal corresponding to the at least one analog signal; and

an analog-to-digital converter (A/D) stage to convert the at least one analog signal provided by the AFE to the at least one digital signal for use by the DBE.

2. The device of claim 1, wherein the device is composed entirely on an integrated circuit (IC) in a system-on-chip (SoC) configuration.

3. The device of claim 2, wherein the SoC further comprises electrodes to sense the EEG information, wherein the SoC is configured to be worn by a patient with the electrodes coupled directly to the patient'"'"'s skin.

4. The device of claim 1, wherein the analog front end comprises a plurality of channels, each of the plurality of channels comprising at least a chopper-stabilized capacitive-coupled instrumentation amplifier (CS-CCIA) and a variable gain amplifier.

5. The device of claim 4, wherein the A/D stage comprises a multiplexer to receive analog signals from the plurality of channels and an A/D to convert the received analog signals into digital signals.

6. The device of claim 5, wherein the A/D is a 10-bit successive approximation A/D (10-b SAR-A/D).

7. The device of claim 1, wherein the FE engine comprises a plurality of channels, each of the plurality of channels including a time division multiplex band-pass filter (TDM-BPF) to extract feature vectors (FVs) from the EEG information for use in determining the onset of a seizure.

8. The device of claim 1, wherein the device further comprises a static random access memory to store the EEG information.

9. The device of claim 1, wherein the circuitry configured to perform the log conversion comprises circuitry to linearize the log function into multiple linear segments to approximate the log function.

10. A method, comprising:

receiving at least one analog signal representative of encephalography (EEG) information;

converting the at least one analog signal to at least one digital signal;

extracting feature vectors (FVs) from the at least one digital signal; and

determining onset of a seizure based on the extracted FVs using a linear support vector machine (NL-SVM) classification engine configured to perform an antilog function on a summation of a first input and a log conversion of a second input; and

if the onset of a seizure is determined, causing electrical stimulation to commence to prevent occurrence of the seizure.

11. The method of claim 10, wherein the analog signal comprises a plurality of electroencephalography (EEG) information signals received via an analog front-end (AFE) comprising a plurality of channels, each of the plurality of channels including at least a chopper-stabilized capacitive-coupled instrumentation amplifier (CS-CCIA) and a variable gain amplifier.

12. The method of claim 11, wherein the analog signals are converted into digital signals via an analog-to-digital converter (A/D) stage comprising a multiplexer to receive the plurality of analog signals from the plurality of channels and an A/D to convert the received analog signals into digital signals.

13. The method of claim 12, wherein the A/D is a 10-bit successive approximation A/D (10-b SAR-A/D).

14. The method of claim 10, wherein the FVs are generated by a feature extraction (FE) engine comprising a plurality of channels, each of the plurality of channels comprising a time division multiplex band-pass filter (TDM-BPF) to extract the FVs.

15. The method of claim 14, wherein determining the onset of a seizure based on the FVs comprises mapping the FVs into a seven-dimensional (7-D) feature space and classifying the FVs using said non-linear support vector machine (NL-SVM) classification engine.

16. The method of claim 10, wherein the circuitry configured to perform the log conversion comprises circuitry to linearize the log function into multiple linear segments to approximate the log function.

17. One or more non-transitory computer-readable memories having stored thereon, individually or in combination, instructions that when executed by one or more processors result in the following operations comprising:

receiving at least one digital signal corresponding to at least one analog signal representative of a patient'"'"'s electroencephalography (EEG) information;

extracting feature vectors (FVs) from the digital signal;

determining the onset of a seizure based on the extracted FVs using a non-linear support vector machine (NL-SVM) classification configured to perform an antilog function on a summation of a first input and a log conversion of a second input; and

if the onset of a seizure is determined, causing electrical stimulation to commence to prevent occurrence of the seizure.

18. The one or more non-transitory computer-readable memories of claim 17, wherein determining on the onset of a seizure based on the FVs comprises mapping the FVs into a seven-dimensional (7-D) feature space and classifying the FVs using said non-linear support vector machine (NL-SVM) classification engine.

19. The one or more non-transitory computer-readable memories of claim 17, wherein performing the log conversion comprises linearizing the log function into multiple linear segments to approximate the log function.