Apparatus for treating a patient
First Claim
1. An apparatus for treating a patient for certain neurological and mental disorders, comprising:
- a signal measurement module for measuring brainwave signals from the patient, the signals being corrupted with noise;
a signal cleanup module for processing the measured brainwave signals to obtain clean brainwave signals;
a signal matching module for matching the clean brainwave signals to a database of brainwave signals for neurological/mental conditions to identify the patient'"'"'s mental status; and
a therapy signal application module for applying therapeutic treatment to the patient based on the identified condition;
wherein the signal cleanup module comprises;
a signal transformer for iteratively decomposing a signal into a plurality of decomposition wavelet components of different scale, a number of which are selected for further processing;
one or more auto-associative neural network, each of the one or more auto-associative neural network corresponding to one of selected wavelet components, each auto-associative neural network operatively coupled to the signal transformer to receive a corresponding one of the selected wavelet components to squeeze out noise in the decomposed domain from a corresponding one of the selected wavelet components, wherein the auto-associative neural network is self-supervised and has been trained by creating an output behavior that closely matches a noisy input behavior by adjusting synaptic weights between an input layer and an output layer of the auto-associative neural network, and the auto-associative neural network has an equal number of input neurons and output neurons in the input and output layers and has fewer hidden layer neurons than input layer neurons; and
an inverse signal transformer for recovering a clean signal in the time domain from the combined outputs of the at least one auto-associative neural network.
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Abstract
A signal processing method and system combines multi-scale decomposition, such as wavelet, pre-processing together with a compression technique, such as an auto-associative artificial neural network, operating in the multi-scale decomposition domain for signal denoising and extraction. All compressions are performed in the decomposed domain. A reverse decomposition such as an inverse discrete wavelet transform is performed on the combined outputs from all the compression modules to recover a clean signal back in the time domain. A low-cost, non-drug, non-invasive, on-demand therapy braincap system and method are pharmaceutically non-intrusive to the body for the purpose of disease diagnosis, treatment therapy, and direct mind control of external devices and systems. It is based on recognizing abnormal brainwave signatures and intervenes at the earliest moment, using magnetic and/or electric stimulations to reset the brainwaves back to normality. The feedback system is self-regulatory and the treatment stops when the brainwaves return to normal. The braincap contains multiple sensing electrodes and microcoils; the microcoils are pairs of crossed microcoils or 3-axis triple crossed microcoils.
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Citations
21 Claims
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1. An apparatus for treating a patient for certain neurological and mental disorders, comprising:
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a signal measurement module for measuring brainwave signals from the patient, the signals being corrupted with noise; a signal cleanup module for processing the measured brainwave signals to obtain clean brainwave signals; a signal matching module for matching the clean brainwave signals to a database of brainwave signals for neurological/mental conditions to identify the patient'"'"'s mental status; and a therapy signal application module for applying therapeutic treatment to the patient based on the identified condition; wherein the signal cleanup module comprises; a signal transformer for iteratively decomposing a signal into a plurality of decomposition wavelet components of different scale, a number of which are selected for further processing; one or more auto-associative neural network, each of the one or more auto-associative neural network corresponding to one of selected wavelet components, each auto-associative neural network operatively coupled to the signal transformer to receive a corresponding one of the selected wavelet components to squeeze out noise in the decomposed domain from a corresponding one of the selected wavelet components, wherein the auto-associative neural network is self-supervised and has been trained by creating an output behavior that closely matches a noisy input behavior by adjusting synaptic weights between an input layer and an output layer of the auto-associative neural network, and the auto-associative neural network has an equal number of input neurons and output neurons in the input and output layers and has fewer hidden layer neurons than input layer neurons; and an inverse signal transformer for recovering a clean signal in the time domain from the combined outputs of the at least one auto-associative neural network. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. An apparatus for treating a patient for certain neurological and mental disorders, comprising:
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means for measuring brainwave signals from the patient, the signals being corrupted with noise; means for processing the measured brainwave signals to obtain clean brainwave signals; means for matching the clean brainwave signals to a database of brainwave signals for neurological/mental conditions to identify the patient'"'"'s mental status; and means for applying therapeutic treatment to the patient based on the identified condition; wherein the means for processing the measured brainwave signals comprises; a signal transformer for iteratively decomposing a signal into a plurality of decomposition wavelet components of different scale, a number of which are selected for further processing; one or more auto-associative neural network, each of the one or more auto-associative neural network corresponding to one of selected wavelet components, each auto-associative neural network operatively coupled to the signal transformer to receive a corresponding one of the selected wavelet components to squeeze out noise in the decomposed domain from a corresponding one of the selected wavelet components, wherein the auto-associative neural network is self-supervised and has been trained by creating an output behavior that closely matches a noisy input behavior by adjusting synaptic weights between an input layer and an output layer of the auto-associative neural network, and the auto-associative neural network has an equal number of input neurons and output neurons in the input and output layers; and an inverse signal transformer for recovering a clean signal in the time domain from the combined outputs of the at least one auto-associative neural network. - View Dependent Claims (21)
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Specification