System and method for interfacing with biological tissue

Abstract

There is provided a system and method for interfacing with biological tissue. The system includes: a feature extraction module to implement an extraction approach to extract one or more features from the one or more physiological recording signals; a machine learning module to apply a machine learning model based on input data to detect a physiological event or condition for classification, the input data including the extracted features, the machine learning model trained using a training set including feature vectors of time-series data labelled with known occurrences of the physiological event or condition; and an output module to output the classification of the machine learning module.

1 . A system for interfacing with biological tissue, the system comprising one or more processors and one or more memory units, the one or more processors in communication with the one or more memory units, the one or more processors configured to execute:
 a feature extraction module to implement an extraction approach to extract one or more features from the one or more physiological recording signals;   a machine learning module to apply a machine learning model based on input data to detect a physiological event or condition for classification, the input data comprising the extracted features, the machine learning model trained using a training set comprising feature vectors of time-series data labelled with known occurrences of the physiological event or condition; and   an output module to output the classification of the machine learning module.   
     
     
         2 . The system of  claim 1 , wherein the system is connectable to one or more electrodes implantable in the biological tissue via an analog front-end, the analog front-end comprising one or more physiological signal acquisition circuits in communication with the biological tissue and a convertor, the analog front-end communicating one or more physiological recording signals to the one or more processors. 
     
     
         3 . The system of  claim 2 , wherein the system is connectable to one or more physiological stimulation channels, the one or more physiological stimulation channels in communication with the biological tissue, the one or more physiological stimulation channels connectable to the one or more processors, the system further comprising a stimulation controller for generating and delivering one or more electronic signals to the one or more physiological stimulation channels, the one or more electronic signals comprising an arbitrary shape waveform. 
     
     
         4 . The system of  claim 3 , wherein the biological tissue comprises tissue within a central nervous system or a peripheral nervous system. 
     
     
         5 . The system of  claim 3 , wherein the one or more physiological signal acquisition circuits comprise neural signal recording channels. 
     
     
         6 . The system of  claim 5 , wherein the physiological signal acquisition circuits comprise at least one of: signal samplers, amplifiers, filters and analog-to-digital convertors. 
     
     
         7 . The system of  claim 5 , wherein the one or more physiological stimulation channels comprises one or more neurostimulation channels. 
     
     
         8 . The system of  claim 3 , wherein the one or more physiological stimulation channels generate and deliver at least one of current, charge, voltage, ultrasound, and magnetic signals. 
     
     
         9 . The system of  claim 3 , wherein the extraction approach comprises a dimensionality reduction approach, the dimensionality reduction approach comprising at least one of: an autoencoder neural network, a principal component analysis (PCA), an independent component analysis (ICA). 
     
     
         10 . The system of  claim 3 , wherein the one or more features comprise at least one of: signals band energy, signals phase locking feature, signals cross-frequency coupling, signals temporal correlation, and signals spatial correlation. 
     
     
         11 . The system of  claim 3 , wherein the physiological event or condition comprises at least one of: a pathological brain state, a non-pathological brain state, and a physiological event in a peripheral nervous system. 
     
     
         12 . The system of  claim 3 , wherein the arbitrary shape comprises at least one of biphasic pulses, monophasic pulses, sinusoids, and functions of sinusoids. 
     
     
         13 . The system of  claim 3 , wherein the stimulator generates the one or more electronic signals in a temporal or spatial periodic pattern. 
     
     
         14 . The system of  claim 3 , wherein the stimulator generates the one or more electronic signals when the physiological event is detected by the machine learning module. 
     
     
         15 . The system of  claim 14 , wherein the detected physiological event is a pathological brain state. 
     
     
         16 . The system of  claim 3 , wherein the stimulator comprises a charge balancer for generating charge-balanced physiological stimulation waveforms. 
     
     
         17 . The system of  claim 16 , wherein the charge balancer comprises a charge balance monitor. 
     
     
         18 . The system of  claim 3 , wherein the one or more physiological stimulation channels are in communication with the brain via at least one of: the one or more electrodes, electromagnetic coils, antennas, ultrasound sources, light sources, and reservoirs comprising molecular, chemical, or biochemical content. 
     
     
         19 . The system of  claim 2 , wherein the machine learning model is continuously updated using new inputs from the one or more physiological recording signals. 
     
     
         20 . A method for interfacing with biological tissue, the method executable on one or more processors, the method comprising:
 extracting one or more features from one or more physiological signals by an extraction approach;   applying a machine learning model based on input data to detect a physiological event or condition for classification, the input data comprising the extracted features, the machine learning model trained using a training set comprising feature vectors of time-series data labelled with known occurrences of the physiological event or condition;   generating and delivering one or more electronic signals to the one or more physiological stimulation channels, the one or more electronic signals comprising an arbitrary shape waveform.   
     
     
         21 . A computer-implemented method for sampling time-series data, comprising:
 receiving new data from the time-series data stream;   receiving contemporary data from a time-series data stream;   applying a sampling recursive window to the contemporary data;   accessing previously received data from the time-series data stream;   applying a temporal function to the previously received data;   subtracting the previously received data with the temporal function applied from the contemporary data; and   outputting the contemporary data after the subtraction has been applied.   
     
     
         22 . A system for sampling time-series data, the system comprising one or more processors and one or more memory units, the one or more processor configured to execute a sampling module to:
 receive new data from the time-series data stream;   receive contemporary data from a time-series data stream;   apply a sampling recursive window to the contemporary data;   access previously received data from the time-series data stream;   apply a temporal function to the previously received data;   subtract the previously received data with the temporal function applied from the contemporary data; and   output the contemporary data after the subtraction has been applied.   
     
     
         23 . A computer-implemented method for arbitrary waveform generation for physiological stimulation, comprising:
 generating an arbitrary function signal;   passing the arbitrary function signal through a charge balance monitor to monitor compliance with predetermined charge limits;   applying a physiological stimulation with the signal; and   applying binary exponential charge recovery (BECR) to the signal by determining a net stimulus integral and applying a reverse charge when the integral is not zero due to arbitrary waveform stimulation or due to predetermined limits having been exceeded.   
     
     
         24 . A system for arbitrary waveform generation for physiological stimulation, the system connectable to one or more electrodes implantable in a brain, the system comprising:
 an arbitrary waveform generator (AWG) to generate an arbitrary function signal;   a charge balance monitor to receive the arbitrary function signal and monitor compliance with predetermined charge limits;   a physiological stimulator to apply a physiological stimulation with the signal; and   a binary exponential charge recovery (BECR) unit to apply BECR to the signal by determining a net stimulus integral and applying a reverse charge when the integral is not zero due to arbitrary waveform stimulation or due to the predetermined limits having been exceeded.   
     
     
         25 . A computer-implemented method for classifying time-series data for identifying a state, the time-series data comprising a series of samples, the method comprising:
 training a machine learning model to classify occurrences of the state by classifying a representative feature vector, using a respective training set, the respective training set comprising feature vectors of the time-series data labelled with occurrences of the state;   receiving a new time-series data stream;   determining whether a current sample in the new time-series data stream corresponds to an occurrence of the state by determining a classified feature vector, the classified feature vector determined by passing the current sample and samples in at least one continuous sampling window into the trained machine learning model, each continuous sampling window comprising one or more preceding samples from the time-series data, an epoch for each respective continuous sampling window determined according to a temporal function; and   outputting the determination of whether the current sample corresponds to an occurrence of the state.   
     
     
         26 . The method of  claim 25 , wherein each continuous sampling window is recursively defined based on the epoch of a previous iteration of the respective window subtracted by the respective temporal function multiplied by the epoch of such previous iteration. 
     
     
         27 . The method of  claim 25 , wherein the at least one continuous sampling window comprises at least two continuous sampling windows, the epoch of each of the continuous sampling windows are defined by different temporal function parameters. 
     
     
         28 . The method of  claim 25 , wherein each of the temporal functions comprise a decay rate, and wherein each exponential decay rate is a reciprocal of a power of 2. 
     
     
         29 . The method of  claim 28 , wherein each exponential decay rate is in the range of 1/2 to 1/(2 16 ). 
     
     
         30 . The method of  claim 27 , wherein each epoch is on the order of minutes or less. 
     
     
         31 . The method of  claim 27 , wherein the state vector machine learning model uses one of linear, polynomial and radial-basis function (RBF) kernels. 
     
     
         32 . The method of  claim 26 , wherein the at least one continuous sampling window comprises a plurality of continuous sampling windows organized into at least two banks of continuous sampling windows, each bank comprising at least one continuous sampling window, the continuous sampling windows in each bank having a different temporal function parameters than the continuous sampling windows in the other banks. 
     
     
         33 . The method of  claim 27 , wherein the time-series data comprises physiological signals and the state comprises a physiological event or condition. 
     
     
         34 . The method of  claim 33 , wherein the time-series data comprises electroencephalography (EEG) signals and the state comprises one or more onset biomarkers associated with a seizure. 
     
     
         35 . A system for classifying time-series data for state identification, the system comprising one or more processors and one or more memory units, the one or more memory units storing the time-series data comprising a series of samples, the one or more processors in communication with the one or more memory units and configured to execute:
 a training module for training a machine learning model to classify occurrences of the state by classifying a representative feature vector, using a respective training set, the respective training set comprising feature vectors of the time-series data labelled with occurrences of the state;   an input module for receiving a new time-series data stream comprising a plurality of samples;   a temporal function module for defining at least one continuous sampling window, each continuous sampling window comprising one or more samples from the time-series data preceding a current sample, an epoch for each respective continuous sampling window determined according to a respective temporal function;   a support vector module for determining whether a current sample in the new time-series data stream is an occurrence of the state by determining a classified feature vector, the classified feature vector determined by passing the current sample and samples in the at least one continuous sampling window into the trained machine learning model; and   an output module for outputting the determination of whether the current sample is an occurrence of the state.   
     
     
         36 . The system of  claim 35 , wherein each continuous sampling window is recursively defined based on the epoch of a previous iteration of the respective window subtracted by the respective temporal function multiplied by the epoch of such previous iteration. 
     
     
         37 . The system of  claim 36 , wherein the at least one continuous sampling window comprises at least two continuous sampling windows, the epoch of each of the continuous sampling windows are defined by defined by different temporal function parameters. 
     
     
         38 . The system of  claim 34 , wherein each of the temporal functions comprise a decay rate, and wherein each exponential decay rate is a reciprocal of a power of 2. 
     
     
         39 . The system of  claim 38 , wherein each exponential decay rate is in the range of 1/2 to 1/(2 16 ). 
     
     
         40 . The system of  claim 37 , wherein each epoch is on the order of minutes or less. 
     
     
         41 . The system of  claim 37 , wherein the state vector machine learning model uses one of linear, polynomial and radial-basis function (RBF) kernels. 
     
     
         42 . The system of  claim 35 , wherein the temporal function module defines a plurality of continuous sampling windows organized into at least two banks of continuous sampling windows, each bank comprising at least one continuous sampling window, the continuous sampling windows in each bank having different temporal function parameters than the continuous sampling windows in the other banks. 
     
     
         43 . The system of  claim 37 , wherein the time-series data comprises physiological signals and the state comprises a physiological event. 
     
     
         44 . The system of  claim 43 , wherein the time-series data comprises electroencephalography (EEG) signals captured by electrodes in communication with the system, and the state comprises one or more onset biomarkers associated with a seizure.