Machine-based patient-specific seizure classification system
AssigneeMasdar Institute of Science and Technology
InventorsJerald Yoo, Muhammad Awais Bin Altaf
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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.
Claims19
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.
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.
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.
Dependencies are inferred from the claim text.