Method for treating a patient

• US 9,095,266 B1
• Filed: 08/02/2010
• Issued: 08/04/2015
• Est. Priority Date: 08/02/2010
• Status: Active Grant

• Current Assignee: Chi Yung Fu
• Original Assignee: Chi Yung Fu
• Inventors: Fu, Chi Yung
• Primary Examiner(s): Koharski, Christopher D
• Assistant Examiner(s): KIMBALL, JEREMIAH T
• Application Number:
  US12/804,939
• Time in Patent Office: 1,828 Days
• Field of Search: 607/45, 600/544
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0214 of power generation or supply
• CPC Class Codes: A61B 2562/046 in a matrix array
• CPC Class Codes: A61B 2562/14 Coupling media or elements to improve sensor contact with skin or tissue
• CPC Class Codes: A61B 2576/026 for the brain
• CPC Class Codes: A61B 5/0036 including treatment, e.g., using an implantable medical device, ablating, ventilating
• CPC Class Codes: A61B 5/0042 for the brain
• CPC Class Codes: A61B 5/0075 by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy A61B5/0071 takes precedence
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/246 using evoked responses
• CPC Class Codes: A61B 5/291 for electroencephalography [EEG]
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/377 using evoked responses
• CPC Class Codes: A61B 5/6803 Head-worn items, e.g. helmets, masks, headphones or goggles
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7235 Details of waveform analysis A61B5/349 takes precedence
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61N 1/0408 Use-related aspects
• CPC Class Codes: A61N 1/0456 : Specially adapted for transcutaneous electrical nerve stimulation [TENS]
• CPC Class Codes: A61N 1/0476 : Array electrodes (including any electrode arrangement with more than one electrode for at least one of the polarities)
• CPC Class Codes: A61N 1/0526 : Head electrodes A61N1/0551 takes precedence
• CPC Class Codes: A61N 1/36021 : for treatment of pain
• CPC Class Codes: A61N 1/36025 : for treating a mental or cerebral condition
• CPC Class Codes: A61N 1/36031 : using physiological parameters for adjustment
• CPC Class Codes: A61N 1/40 : Applying electric fields by inductive or capacitive coupling microwave apparatus A61N5/00; Applying radio-frequency signals
• CPC Class Codes: A61N 2/006 : for magnetic stimulation of nerve tissue
• CPC Class Codes: A61N 2/008 : for pain treatment or analgesia
• CPC Class Codes: A61N 2/02 : using magnetic fields produced by coils, including single turn loops or electromagnets A61N2/12 takes precedence
• CPC Class Codes: A61N 5/02 : using microwaves
• CPC Class Codes: A61N 5/10 : X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy A61N5/01 takes precedence
• CPC Class Codes: A61N 7/00 : Ultrasound therapy lithotripsy A61B17/22, A61B17/225; massage using supersonic vibration A61H23/00 ; using ultrasound for introducing media into the body A61M37/0092
• CPC Class Codes: G16H 20/40 : relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 20/70 : relating to mental therapies, e.g. psychological therapy or autogenous training
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass

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.

Claims

1. A method for treating a patient for certain neurological and mental disorders, comprising:

measuring brainwave signals from the patient, the signals being corrupted with noise;

processing the measured brainwave signals to obtain clean brainwave signals;

matching the clean brainwave signals to a database of brainwave signals for neurological and mental conditions to identify the patient'"'"'s mental status;

applying therapeutic treatment to the patient based on the identified condition;

wherein processing the measured brainwave signals comprises;

receiving a signal corrupted with noise;

performing an n-level decomposition of said signal into wavelet components of different scale;

retaining selected decomposition wavelet components;

inputting a retained wavelet component of a selected scale into a corresponding auto-associative neural network to squeeze out noise in said wavelet component in the decomposed domain, 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 wherein the auto-associative neural network has an equal number of input and output neurons in the input and output layers and has fewer hidden layer neurons than input layer neurons;

inputting additional retained wavelet components of different scales into separate corresponding auto-associative neural networks, each to squeeze out noise in a corresponding one of the inputted wavelet components in the decomposed domain; and

performing an inverse decomposition on the outputs from all the auto-associative neural networks to recover a clean signal in the time domain.

2. The method of claim 1, wherein the measured brainwave signals are Electrocephalography (EEG) signals, Magnetoencephalography (MEG) signals, infrared (IR) signals, alkali vapor magnetometer signals, or signals from other sensors that can sense brain activities.

3. The method of claim 1 wherein the clean brainwave signals are matched to a database of brainwave signals for PTSD, depression, pain, epilepsy, ADHD, autistic spectrum disorders, Parkinson'"'"'s disease, or Alzheimer'"'"'s disease.

4. The method of claim 1, wherein the therapeutic treatment comprises Transcranial Magnetic Stimulation (TMS) and repetitive Transcranial Magnetic Stimulation (rTMS).

5. The method of claim 1 further comprising placing a cap on the head of the patient, the cap containing a plurality of microelectrodes or microsensors for measuring the brainwave signals from the patient and a plurality of microcoils for applying therapeutic treatment to the patient.

6. The method of claim 5 wherein the plurality of microcoils comprises a plurality of pairs of cross-coupled intersecting helical solenoidal microcoils.

7. The method of claim 5 wherein the plurality of microcoils comprises a plurality of 3-axis cross-coupled triple intersecting helical solendoidal microcoils.

8. The method of claim 5 further comprising placing a pair of intersecting multi-turn coils around the head of the patient, one oriented substantially horizontally around the head at the forehead area and the other positioned substantially vertically to encircle the head from the top to just below the chin to apply therapeutic treatment to the patient.

9. The method of claim 1 further comprising constructing the neural networks with an equal number of input and output neurons in the input and output layers and with fewer hidden layer neuron than input layer neurons so that most of the signal can pass through but more random noise is mostly blocked.

10. The method of claim 1 wherein the matching is performed by teaching and training a supervised neural network with known data injected with noise so that the neural network can distinguish abnormal brainwaves from normal brainwaves and identify specific abnormalities.

11. The method of claim 1 wherein the matching is performed by an unsupervised technique to automatically sort and classify abnormal brainwaves from normal brainwaves and from each other for identification of particular abnormalities.

12. The method of claim 1, wherein the synaptic weights between the input layer and the output layer include synaptic weights between the input layer and a hidden layer and synaptic weights between the hidden layer and an output layer.

13. The method of claim 1, wherein the auto-associative neural network is a Logicon projection neural network.

14. The method of claim 1, wherein the decomposition is a discrete wavelet transform and the inverse decomposition is an inverse discrete wavelet transform.

15. The method of claim 1 further comprising placing a cap on the head of the patient, the cap containing a plurality of microelectrodes for measuring the brainwave signals from the patient and a plurality of pairs of cross-coupled helical solenoidal microcoils or a plurality of 3-axis cross-coupled triple helical solenoidal microcoils for applying therapeutic treatment to the patient.

16. The method of claim 15 further comprising placing a pair of intersecting multi-turn coils around the head of the patient, one oriented substantially horizontally around the head at the forehead area and the other positioned substantially vertically to encircle the head from the top to just below the chin to apply therapeutic treatment to the patient.

17. The method of claim 1 wherein the therapeutic treatment comprises transcranial direct current stimulation (tDCS) or earpoint electrical stimulation.

18. The method of claim 1 further comprising alternating between measuring the brainwave signals and applying the therapeutic treatment.

19. The method of claim 18, wherein the treatment includes magnetic or electric stimulations.

20. The method of claim 1 further comprising continuing to measure brainwave signals from the patient, to process the measured brainwave signals to obtain clean brainwave signals, and to match the clean brainwave signals to a database of brainwave signals for neurological and mental conditions to identify the patient'"'"'s mental status while applying therapeutic treatment to the patient based on the identified condition;

and terminating the therapeutic treatment when the clean brainwave signals indicate substantially normal status.

21. A method for treating a patient for certain neurological and mental disorders, comprising:

placing a cap on the head of the patient, the cap containing a plurality of pairs of cross-coupled intersecting helical solenoidal microcoils or a plurality of 3-axis cross-coupled triple intersecting helical solendoidal microcoils;

processing brainwave signals measured from the patent to determine a therapeutic treatment; and

flowing current through the cross-coupled microcoils to apply the therapeutic treatment to the patient;

wherein processing the brainwave signals comprises;

performing an n-level decomposition of a brainwave signal into wavelet components of different scale;

retaining selected decomposition wavelet components;

inputting a retained wavelet component of a selected scale into a corresponding auto-associative neural network to squeeze out noise in said wavelet component in the decomposed domain, 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 wherein the auto-associative neural network has an equal number of input and output neurons in the input and output layers and has fewer hidden layer neurons than input layer neurons;

inputting additional retained wavelet components of different scales into separate corresponding auto-associative neural networks, each to squeeze out noise in a corresponding one of the inputted wavelet components in the decomposed domain; and

performing an inverse decomposition on the outputs from all the auto-associative neural networks to recover a clean signal in the time domain.

22. The method of claim 21 further comprising placing a pair of intersecting multi-turn coils around the head of the patient, one oriented substantially horizontally around the head at the forehead area and the other positioned substantially vertically to encircle the head from the top to just below the chin, and flowing current through the coils to apply therapeutic treatment to the patient.

23. The method of claim 21 wherein the treatment is applied to treat PTSD, depression, pain, epilepsy, ADHD, autistic spectrum disorders, obesity, addiction, Parkinson'"'"'s disease, or Alzheimer'"'"'s disease, or to induce anesthesia.

24. The method of claim 21, wherein the synaptic weights between the input layer and the output layer include synaptic weights between the input layer and a hidden layer and synaptic weights between the hidden layer and an output layer.

25. A method for treating a patient for certain neurological/mental disorders, comprising:

placing a pair of intersecting multi-turn coils around the head of the patient, one oriented substantially horizontally around the head at the forehead area and the other positioned substantially vertically to encircle the head from the top to just below the chin;

processing brainwave signals measured from the patent to determine a treatment; and

flowing current through the coils to apply the treatment to the patient;

wherein processing the brainwave signals comprises;

performing an n-level decomposition of a brainwave signal into wavelet components of different scale;

retaining selected decomposition wavelet components;

inputting a retained wavelet component of a selected scale into a corresponding auto-associative neural network to squeeze out noise in said wavelet component in the decomposed domain, 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 wherein the auto-associative neural network has an equal number of input and output neurons in the input and output layers and has fewer hidden layer neurons than input layer neurons;

inputting additional retained wavelet components of different scales into separate corresponding auto-associative neural networks, each to squeeze out noise in a corresponding one of the inputted wavelet components in the decomposed domain; and

performing an inverse decomposition on the outputs from all the auto-associative neural networks to recover a clean signal in the time domain.

26. The method of claim 25, wherein the brainwave signals are measured using Electrocephalography (EEG), Magnetoencephalography (MEG), infrared (IR), alkali vapor magnetometer, or from other sensors that can sense brain activities.

27. The method of claim 25, wherein the synaptic weights between the input layer and the output layer include synaptic weights between the input layer and a hidden layer and synaptic weights between the hidden layer and an output layer.

28. The method of claim 25, wherein the treatment includes inducing anesthesia to the patient.

29. The method of claim 25, wherein further comprising:

inducting anesthesia to patient and treating insomnia by inducing cortical effective connectivity disruption through the coils resulting in loss of consciousness.