Apparatus for treating a patient

Abstract

At least one embodiment of the disclosure is directed to a method for processing brainwave signals for treating a patient having neurological disorder or mental disorder or a combination of neurological and mental disorder. The method comprises: measuring a brainwave signal from the patient, the measured brainwave signal containing noise; denoising the brainwave signal to obtain a clean brainwave signal; matching the clean brainwave signal to a database of brainwave signals for neurological or mental conditions or a combination of neurological and mental conditions to identify the patient's neurological or mental status or a combination of neurological and mental conditions; and applying a therapeutic treatment to the patient based on the identified neurological or mental status or a combination of neurological and mental conditions.

1 . A method for processing brainwave signals for treating a patient having neurological disorder or mental disorder or a combination of neurological and mental disorder, comprising:
 measuring a brainwave signal from the patient, the measured brainwave signal containing noise;   denoising the brainwave signal to obtain a clean brainwave signal;   matching the clean brainwave signal to a database of brainwave signals for neurological or mental conditions or a combination of neurological and mental conditions to identify the patient's neurological or mental status or a combination of neurological and mental conditions; and   applying a therapeutic treatment to the patient based on the identified neurological or mental status or a combination of neurological and mental conditions.   
     
     
         2 . The method of  claim 1 , wherein the step of denoising comprises:
 performing an n-level decomposition of the brainwave signal into wavelet components of different scales to separate the signals from noise;   for each individual wavelet component among the wavelet components of different scales, inputting the individual wavelet component into a corresponding compression module to squeeze out the noise from the individual wavelet component in a decomposed domain, wherein the compression module is self-supervised and has been trained by creating an output behavior of the compression module that closely matches an input behavior of the compression module; and   performing an inverse decomposition on outputs from the compression modules to recover the clean brainwave signal in a time domain.   
     
     
         3 . The method of  claim 1 , wherein the step of applying the therapeutic treatment comprises:
 applying the therapeutic treatment to the patient based on the identified neurological or mental status via a self-regulatory feedback system, wherein the self-regulatory feedback system stops the therapeutic treatment when the brainwave signal return approximately to normal.   
     
     
         4 . The method of  claim 1 , wherein the database of brainwaves signals includes sample brainwave signals for PTSD, depression, pain, epilepsy, ADHD, autistic spectrum disorder, Parkinson's disease, Alzheimer's disease, sleep disorder, schizophrenia, alcohol or drug craving, or anxiety or panic disorder. 
     
     
         5 . The method of  claim 1 , wherein the step of measuring the brainwave signal and the step of applying the therapeutic treatment are alternated for protection of ultra-sensitive measuring electronics. 
     
     
         6 . The method of  claim 1 , wherein the amplifiers for the signal collection such as the EEG amplifiers have a low slew rate, the low slew rate keeping the amplifiers from responding to higher slew rate of energetic pulses from the treatment. 
     
     
         7 . The method of  claim 1 , wherein the patient's neurological disorders or mental disorders or a combination of neurological and mental disorders are identified using Kohonen self-organizing map, vector quantization, or Bayesian statistical calculation methodologies. 
     
     
         8 . The method of  claim 1 , wherein the patient's neurological disorders or mental disorders or a combination of neurological and mental disorders are identified using a meta-analysis combination multiple methodologies from Kohonen self-organizing map, vector quantization, or Bayesian statistical calculation methodologies, to increase accuracy of the matching and to improve diagnosis accuracy. 
     
     
         9 . The method of  claim 1 , wherein the patient's neurological or mental status is identified for earlier diagnosis resulting in pre-emptive intervention and monitoring, resulting in improving a chance of recovery or containment of ailment. 
     
     
         10 . The method of  claim 1 , wherein treatment components for performing the therapeutic treatment are identical in hardware, and each treatment component has a local lookup table to readily and easily generate tailored waveforms by non-engineers to flexibly vary treatment for different disorders or for tailoring treatments to different individuals having the same disorders. 
     
     
         11 . The method of  claim 1 , wherein treatment components for performing the therapeutic treatment are identical in hardware, and each treatment component has its own local lookup table easily tailored or configurable to generate different waveforms for having the flexibility of different treatments at different sites simultaneously in a single treatment session such as having neurological inhibitions at certain sites and simultaneously having excitations at other locations. 
     
     
         12 . The method of  claim 1 , wherein the therapeutic treatment is performed by an electronically controlled iontophoretic transdermal system or an electronically controlled morphine delivery system to adaptively control pain in real time based on the brainwave signal for detection of pain level. 
     
     
         13 . The method of  claim 12 , wherein the therapeutic treatment minimizes or eliminates a need to use anti-nausea medications when narcotic drugs are administrated for pain control. 
     
     
         14 . The method of  claim 1 , wherein the therapeutic treatment together with diagnostic monitoring provide anesthesia adaptive to individual or cognitive enhancement. 
     
     
         15 . The method of  claim 14 , wherein the cognitive enhancement of the therapeutic treatment improves memory, language processing or increases alertness. 
     
     
         16 . The method of  claim 1 , wherein the examination of neurological condition or mental condition or a combination of neurological and mental conditions is used for detection of deception and determining type of the deception. 
     
     
         17 . The method of  claim 1 , wherein the therapeutic treatment relieves back pain or dental pain. 
     
     
         18 . The method of  claim 1 , wherein the therapeutic treatment comprises Transcranial Magnetic Stimulation (TMS), repetitive Transcranial Magnetic Stimulation (rTMS), the transcranial direct current stimulation (tDCS) or ear-point stimulation. 
     
     
         19 . The method of  claim 18 , wherein the therapeutic treatment is an on-demand therapy which stops when measured signals approximately match the normal signals in the database. 
     
     
         20 . The method of  claim 18 , wherein the Transcranial Magnetic Stimulation (TMS), or repetitive Transcranial Magnetic Stimulation (rTMS) or the transcranial direct current stimulation (tDCS) is used as localized diagnostic probe by probing or stimulating local sites to invoke responses to improve or to initialize diagnosis of regional dysfunctions are causes or triggering events for disorders or as an investigative tool for cognitive enhancement. 
     
     
         21 . The method of  claim 18 , wherein the Transcranial Magnetic Stimulation (TMS), or repetitive Transcranial Magnetic Stimulation (rTMS) or the transcranial direct current stimulation (tDCS) are to target different brain regions using different learning or analysis stimulations to speed up the overall learning or analysis process, for highly complex problems, including problems in national security intelligence, problems in business and educational sectors, or problems requiring a combination of different types of learning at different regions in the brain or for adaptively tailoring for any particular individuals. 
     
     
         22 . The method of  claim 18 , wherein the Transcranial Magnetic Stimulation (TMS), or repetitive Transcranial Magnetic Stimulation (rTMS) or the transcranial direct current stimulation (tDCS) are to target the brain craving regions for reduction of craving for food or cessation of drugs, alcohol and smoking habits. 
     
     
         23 . The method of  claim 1 , wherein the therapeutic treatment is a hyperthermia treatment of body ailments, hyperthermia treatment of cancer by heat, the hyperthermia treatment including introducing nanomaterials with efficient magnetic or electric absorption properties to improve the focusing of heat at a targeted sites. 
     
     
         24 . The method of  claim 1 , wherein the therapeutic treatment is automatic using optimization theory to adaptively set desired controlling parameters so as to steer the measured output towards desired outputs. 
     
     
         25 . The method of  claim 1 , wherein the clean brainwave signal includes Electroencephalography (EEG) signal, infrared (IR) signal, magnetometer signal from alkali vapor magnetometer or superconducting quantum interference device (SQUID), or signal from other sensor that can sense brain activities. 
     
     
         26 . A method for processing brainwave signals:
 measuring the brainwave signal from the person, the measured brainwave signal containing noise;   receiving a brainwave signal, the brainwave signal containing noise;   performing an n-level decomposition of the brainwave signal into wavelet components of different scales;   for each individual wavelet component among the wavelet components of different scales, inputting the individual wavelet component into a corresponding compression module to squeeze out the noise from the individual wavelet component in a decomposed domain, wherein the compression module is self-supervised and has been trained by creating an output behavior of the compression module that closely matches an input behavior of the compression module;   performing an inverse decomposition on outputs from the compression modules to recover the clean brainwave signal in a time domain; and   matching a clean brainwave signal obtained after denoising to a database of brainwave signals for neurological or mental conditions or a combination of neurological and mental conditions to identify the person's neurological or mental status or a combination of neurological and mental conditions.   
     
     
         27 . The method of  claim 26 , further comprising:
 performing a dictation by recognizing textual data from the clean brainwave signal derived from the person's neurological status or mental status or a combination of neurological and mental statuses.   
     
     
         28 . The method of  claim 26 , further comprising:
 performing a cardiac defibrillation process by stimulating a heart using magnetic induction and avoiding the pain induced by the massive current going through chest wall and muscles from traditional electric current based defibrillator for shocking the heart.   
     
     
         29 . The method of  claim 26 , further comprising:
 implementing a brain-computer interface for assisting quadriplegics, or for implementation of neuro-prostheses, or for brain-based video-game control, or for brain-based cockpit control, based on the identified neurological status or mental status or a combination of neurological and mental statuses resulting in thought-based control instead of joy-stick based interactions.