MACHINE-BASED PATIENT-SPECIFIC SEIZURE CLASSIFICATION SYSTEM

• US 20140235990A1
• Filed: 02/18/2014
• Published: 08/21/2014
• 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
• Granted Patent: US 9,848,793 B2
• Time in Patent Office: Days
• US Class Current: 600/383
• 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 1.83W/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 sense electroencephalography (EEG) information;

a digital back end (DBE) including at least a feature extraction (FE) engine and a non-linear support vector machine (NL-SVM) classification engine to determine onset of a seizure based on the EEG information; and

an analog-to-digital converter (A/D) stage to convert analog signals provided by the AFE for use by the DFE.

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, the SoC being configurable 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 NL-SVM classification engine utilizes a log-linear Gaussian basis function (LL-GBF) to determine the onset of a seizure.

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

10. A method, comprising:

receiving analog electroencephalography (EEG) information in a device;

converting the analog EEG information into digital EEG information;

extracting feature vectors (FVs) from the digital EEG information; and

determining onset of a seizure based on the extracted FVs.

11. The method of claim 10, wherein the analog EEG information is 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 EEG information is converted into digital EEG information via an analog-to-digital converter (A/D) stage comprising a multiplexer to receive 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 a non-linear support vector machine (NL-SVM) classification engine.

16. The method of claim 10, further comprising, if the onset of a seizure is determined, causing electrical stimulation to commence to prevent occurrence of the seizure.

17. At least one machine-readable storage medium having stored thereon, individually or in combination, instructions that when executed by one or more processors result in the following operations comprising:

receiving analog electroencephalography (EEG) information;

converting the analog EEG information into digital EEG information;

extracting feature vectors (FVs) from the digital EEG information; and

determining on the onset of a seizure based on the extracted FVs.

18. The medium of claim 17, wherein the analog EEG information is 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-CLIA) and a variable gain amplifier.

19. The medium of claim 18, wherein the analog EEG information is converted into digital EEG information via an analog-to-digital converter (A/D) stage comprising a multiplexer to receive analog signals from the plurality of channels and an A/D to convert the received analog signals into digital signals.

20. The medium of claim 19, wherein the A/D is a 10-bit successive approximation A/D (10-b SAR-A/D).

21. The medium of claim 17, 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.

22. The medium of claim 21, 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 a non-linear support vector machine (NL-SVM) classification engine.

23. The method of claim 17, further comprising instructions that when executed by one or more processors result in the following operations comprising:

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