Systems and methods for clinical decision making for a patient receiving a neuromodulation therapy based on deep learning

Abstract

Information relevant to making clinical decisions for a patient is identified based on electrical activity records of the patient's brain and electrical activity records of other patients' brains. A deep learning algorithm is applied to an electrical activity record of the patient, i.e., an input record, and to a set of electrical activity records of other patients, i.e., a set of search records, to obtain an input feature vector of the patient and a set of search feature vectors, each including features extracted by the deep learning algorithm. A similarities algorithm is applied to the input feature vector and the set of search feature vectors to identify a subset of search records most like the input record. Clinical information associated with one or more search records in the identified subset of search records is extracted from a database and used to make decisions regarding the patient's neuromodulation therapies.

What is claimed is: 
     
         1 . A method of determining stimulation parameter settings that define a stimulation therapy deliverable by a neurostimulation system of a patient, the method comprising:
 applying a similarities algorithm to at least one input feature vector derived from at least one input record of the patient and a set of search feature vectors, each search feature vector derived from a search record of another patient, to identify a subset of search records having a threshold measure of similarity with the at least one input record; and   extracting from a database, clinical information associated with one or more of the search records of other patients in the identified subset of search records of other patients, the clinical information comprising the stimulation parameter settings comprising at least one of amplitude, pulse width, burst duration, and frequency.   
     
     
         2 . The method of  claim 1 , wherein the at least one input feature vector is a single input feature vector, and further comprising:
 applying a deep learning algorithm to the at least one input record of the patient to extract a plurality of different features from the input record and derive the single input feature vector.   
     
     
         3 . The method of  claim 1 , wherein the at least one input record comprises a plurality of input records and the at least one input feature vector is a single input feature vector, and further comprising:
 for each of the plurality of input records, applying a deep learning algorithm to the input record to extract a plurality of different features and derive an input feature vector; and   applying a similarities algorithm to the input feature vectors to identify one of the input feature vectors as the single input feature vector.   
     
     
         4 . The method of  claim 1 , wherein the at least one input record comprises a plurality of input records and the at least one input feature vector comprises a plurality of input feature vectors, and further comprising:
 for each of a plurality of input records of the patient, applying a deep learning algorithm to the input record to extract a plurality of different features and derive an input feature vector;   grouping the plurality of input feature vectors into sets of input feature vectors based on information included in each of the plurality of input records; and   for each set of input feature vectors, applying a similarities algorithm to the input feature vectors in the set to identify one of the input features vectors in the set as one of the plurality of input feature vectors.   
     
     
         5 . The method of  claim 4 , wherein the information associated with the input records indicates whether an input record resulted from one of: 1) detection of an abnormal neurological event, 2) patient initiated recording and storage, 3) periodic automated recording and storage, or 4) periodic recording and storage of baseline, normal neurological activity. 
     
     
         6 . The method of  claim 4 , wherein applying a similarities algorithm comprises, for each of the plurality of input feature vectors:
 applying the similarities algorithm to the input feature vector and the set of search feature vectors to identify a separate subset of search records having a threshold measure of similarity.   
     
     
         7 . The method of  claim 1 , wherein each search feature vector in the set of search feature vectors comprises a plurality of different features extracted by a deep learning algorithm from a corresponding search record. 
     
     
         8 . The method of  claim 1 , wherein the at least one input record and each search record included in the set of search records are in a same format comprising one of data sample of a time series waveform, a time-series waveform image, and a spectrogram image. 
     
     
         9 . The method of  claim 1 , wherein the clinical information further comprises at least one of associated patient clinical responses, clinical history, past detection parameter settings, past stimulation settings, and past and current drug type and dosage information. 
     
     
         10 . The method of  claim 1 , further comprising:
 extracting one or more search records included in the identified subset of search records from the database; and   providing an output to a user interface, the output comprising information that enables a user device to display the one or more search records.   
     
     
         11 . A processor configured to determine stimulation parameter settings that define a stimulation therapy deliverable by a neurostimulation system of a patient, the processor comprising:
 a similarities module configured to applying a similarities algorithm to at least one input feature vector derived from at least one input record of the patient and a set of search feature vectors, each search feature vector derived from a search record of another patient, to identify a subset of search records having a threshold measure of similarity with the at least one input record; and   an extraction module configured to extract from a database, clinical information associated with one or more of the search records of other patients in the identified subset of search records of other patients, the clinical information comprising the stimulation parameter settings comprising at least one of amplitude, pulse width, burst duration, and frequency.   
     
     
         12 . The processor of  claim 11 , wherein the at least one input feature vector is a single input feature vector, and further comprising a feature extraction module configured to:
 apply a deep learning algorithm to the at least one input record of the patient to extract a plurality of different features from the input record and derive the single input feature vector.   
     
     
         13 . The processor of  claim 11 , wherein the at least one input record comprises a plurality of input records and the at least one input feature vector is a single input feature vector, and further comprising a feature extraction module configured to:
 for each of the plurality of input records, apply a deep learning algorithm to the input record to extract a plurality of different features and derive an input feature vector; and   apply a similarities algorithm to the input feature vectors to identify one of the input feature vectors as the single input feature vector.   
     
     
         14 . The processor of  claim 11 , wherein the at least one input record comprises a plurality of input records and the at least one input feature vector comprises a plurality of input feature vectors, and further comprising a feature extraction module configured to:
 for each of a plurality of input records of the patient, apply a deep learning algorithm to the input record to extract a plurality of different features and derive an input feature vector;   group the plurality of input feature vectors into sets of input feature vectors based on information included in each of the plurality of input records; and   for each set of input feature vectors, apply a similarities algorithm to the input feature vectors in the set to identify one of the input features vectors in the set as one of the plurality of input feature vectors.   
     
     
         15 . The processor of  claim 14 , wherein the information associated with the input records indicates whether an input record resulted from one of: 1) detection of an abnormal neurological event, 2) patient initiated recording and storage, 3) periodic automated recording and storage, or 4) periodic recording and storage of baseline, normal neurological activity. 
     
     
         16 . The processor of  claim 14 , wherein the similarities module applies a similarities algorithm by being further configured to, for each of the plurality of input feature vectors:
 apply the similarities algorithm to the input feature vector and the set of search feature vectors to identify a separate subset of search records having a threshold measure of similarity.   
     
     
         17 . The processor of  claim 11 , wherein each search feature vector in the set of search feature vectors comprises a plurality of different features extracted by a deep learning algorithm from a corresponding search record. 
     
     
         18 . The processor of  claim 11 , wherein the at least one input record and each search record included in the set of search records are in a same format comprising one of data sample of a time series waveform, a time-series waveform image, and a spectrogram image. 
     
     
         19 . The processor of  claim 11 , wherein the clinical information further comprises associated patient clinical responses, clinical history, past detection parameter settings, past stimulation settings, and past and current drug type and dosage information. 
     
     
         20 . The processor of  claim 11 , wherein the extraction module is further configured to:
 extract one or more search records included in the identified subset of search records from the database; and   provide an output to a user interface, the output comprising information that enables a user device to display the one or more search records.