Adaptive method and apparatus for forecasting and controlling neurological disturbances under a multi-level control
First Claim
1. A method for predicting and controlling the electrographic and clinical onset of a seizure and other neurological events in an individual, comprising the acts of:
- generating data that is acquired from a plurality of input signals obtained from at least one sensor located in or on the individual;
fusing the data to combine information from the at least one sensor that is connected to at least one transducer;
selecting and extracting a plurality of features from the fused data;
determining from the extracted features if a seizure or other neurological event is likely to occur within a plurality of specified time frames, and the probability of having a seizure for each specified time frame;
providing an alarm to the individual to inform him of an imminent seizure or neurological event when the probability of seizure is higher than an adaptive threshold; and
applying a control rule to initiate an intervention measure that is commensurate with the probability of the electrographical onset of a seizure for each specified time frame.
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Abstract
A method and apparatus for forecasting and controlling neurological abnormalities in humans such as seizures or other brain disturbances. The system is based on a multi-level control strategy. Using as inputs one or more types of physiological measures such as brain electrical, chemical or magnetic activity, heart rate, pupil dilation, eye movement, temperature, chemical concentration of certain substances, a feature set is selected off-line from a pre-programmed feature library contained in a high level controller within a supervisory control architecture. This high level controller stores the feature library within a notebook or external PC. The supervisory control also contains a knowledge base that is continuously updated at discrete steps with the feedback information coming from an implantable device where the selected feature set (feature vector) is implemented. This high level controller also establishes the initial system settings (off-line) and subsequent settings (on-line) or tunings through an outer control loop by an intelligent procedure that incorporates knowledge as it arises. The subsequent adaptive settings for the system are determined in conjunction with a low-level controller that resides within the implantable device. The device has the capabilities of forecasting brain disturbances, controlling the disturbances, or both. Forecasting is achieved by indicating the probability of an oncoming seizure within one or more time frames, which is accomplished through an inner-loop control law and a feedback necessary to prevent or control the neurological event by either electrical, chemical, cognitive, sensory, and/or magnetic stimulation.
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Citations
187 Claims
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1. A method for predicting and controlling the electrographic and clinical onset of a seizure and other neurological events in an individual, comprising the acts of:
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generating data that is acquired from a plurality of input signals obtained from at least one sensor located in or on the individual;
fusing the data to combine information from the at least one sensor that is connected to at least one transducer;
selecting and extracting a plurality of features from the fused data;
determining from the extracted features if a seizure or other neurological event is likely to occur within a plurality of specified time frames, and the probability of having a seizure for each specified time frame;
providing an alarm to the individual to inform him of an imminent seizure or neurological event when the probability of seizure is higher than an adaptive threshold; and
applying a control rule to initiate an intervention measure that is commensurate with the probability of the electrographical onset of a seizure for each specified time frame. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187)
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59. A computer readable medium containing a computer program product for predicting and controlling the electrographic and clinical onset of a seizure and other neurological events in an individual, the computer program product comprising:
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program instructions that generate data acquired from a plurality of input signals obtained from at least one sensor located in or on the individual;
program instructions that fuse the data to combine information from the at least one sensor that is connected to at least one transducer;
program instructions that select and extract a plurality of features from the fused data;
program instructions that determine from the extracted features if a seizure or other neurological event is likely to occur within a plurality of specified time frames, and the probability of having a seizure for each specified time frame;
program instructions that generate an alarm to the individual to inform him of an imminent seizure or neurological event when the probability of seizure is higher than an adaptive threshold; and
program instructions that apply a control rule to initiate an intervention measure that is commensurate with the probability of the electrographical onset of a seizure. - View Dependent Claims (60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103)
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104. A system for predicting and controlling the electrographic and clinical onset of a seizure and other neurological disturbances in an individual, comprising:
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a data generation component to acquire physiological signals from the individual;
an intelligent data processing unit to preprocess the physiological signals, to extract and select a plurality of features, and to provide an estimation of the probability of seizure for at least one time frame; and
a low level controller connected to the intelligent data processing unit to automatically activate a therapeutic intervention measure to control the onset of a seizure in the individual in response to the probability of seizure exceeding a threshold. - View Dependent Claims (105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133)
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134. An adaptive multi-level hierarchical control system for predicting and controlling the electrographic onset of a seizure and other neurological disturbances in an individual, comprising:
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a data generation component that acquires physiological signals from the individual;
an intelligent data processing device that processes the physiological signals to extract features which are analyzed and classified and selected to form a feedback vector;
a low level controller including a stimulation device that is activated to apply an intervention measure in response to the feedback vector to control the onset of seizure and to adjust internal parameter settings of the actuators in the stimulation device; and
a high level supervisory controller including a knowledge database and a processor that adapts to feedback vector changes over time and re-tunes the intelligent data processing device parameter settings dynamically. - View Dependent Claims (135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155)
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156. A method for predicting and controlling the electrographic onset of a seizure in an individual using a multi-level hierarchical control system including an implanted device, comprising the acts of:
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installing at least one sensor on or in the individual to detect input signals indicative of brain activity;
implanting the device into the brain of the individual;
initializing and tuning a plurality of parameters in the implanted device;
installation of an external portable module that contains an external communications unit, a settings adjustment unit with a display and a keypad and an intermediate storage device;
selecting features to extract from the input signals;
analyzing and classifying the selected features extracted from the input signals in order to predict the probability of having a seizure in a plurality of time frames;
activating a closed-loop control system in the implanted device through the external portable module; and
applying a multi-level control to the implanted device to initiate an intervention measure that is based on the probability of having a seizure in a plurality of time frames. - View Dependent Claims (157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172)
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Specification