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WO2006008334A1 - Procede et dispositif d'identification, mesure et analyse de reponses neurologiques anormales - Google Patents

Procede et dispositif d'identification, mesure et analyse de reponses neurologiques anormales Download PDF

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Publication number
WO2006008334A1
WO2006008334A1 PCT/FI2005/000330 FI2005000330W WO2006008334A1 WO 2006008334 A1 WO2006008334 A1 WO 2006008334A1 FI 2005000330 W FI2005000330 W FI 2005000330W WO 2006008334 A1 WO2006008334 A1 WO 2006008334A1
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WIPO (PCT)
Prior art keywords
measuring
accordance
muscles
emg
signal
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PCT/FI2005/000330
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English (en)
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WO2006008334A8 (fr
Inventor
Arto Remes
Markku KANKAANPÄÄ
Juha Kylliäinen
Pekka Tolvanen
Original Assignee
Mega Elektroniikka Oy
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Application filed by Mega Elektroniikka Oy filed Critical Mega Elektroniikka Oy
Priority to EP05771542A priority Critical patent/EP1838214A4/fr
Publication of WO2006008334A1 publication Critical patent/WO2006008334A1/fr
Publication of WO2006008334A8 publication Critical patent/WO2006008334A8/fr
Priority to US11/606,418 priority patent/US20070118044A1/en

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
    • A61B5/4082—Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
    • A61B5/296—Bioelectric electrodes therefor specially adapted for particular uses for electromyography [EMG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/389—Electromyography [EMG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/389—Electromyography [EMG]
    • A61B5/397—Analysis of electromyograms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms

Definitions

  • the present invention relates to a method and device for identifying, measuring and analyzing abnormal neurological responses.
  • Abnormal neurological responses are caused among other things by some neuro ⁇ logical diseases, pains, fatigue, problems in power producing ability or in active- ness.
  • Electromyography measured from the skin surface describes activation and functioning of muscle cells/motor units during the contraction of a skeletal muscle. It is commonly known that by means of an EMG- signal it is possible to measure activity levels of muscles and from those calculate various quantities describing the functioning of muscles and the body. EMG-signal is usually measured through electrodes placed on the skin overlying muscles.
  • the central nervous system recruits in increasing amount muscle cells/motor units in order the muscle power to achieve the func ⁇ tional level to enable the growing mechanical performance.
  • muscle power supply e.g. the energy trans ⁇ form of a muscle (e.g. the function of mitochondria) and the distribution of muscle cell types, which depend on the degree of mechanic experience of the muscle, have effect for their part on endurance property of muscle cells.
  • Fatigue of a muscle is in accordance with current knowledge also the result of satu ⁇ ration of recruiting processes and response systems on central nervous system level.
  • the recruiting of muscle cells changes as the result of the influence of changes in function of the central nervous system and the response systems, in which case the function of motor units in the muscle synchronizes.
  • the measured EMG-signal changes to be less complex and its frequency gets lower.
  • the EMG- signal of a healthy person is stochastic and even nonstationary / chaotic in some respect.
  • abnormal situations e.g. pain, a disease of the central nervous system, Parkinson's disease, lowering of activeness, chronical fatigue, de ⁇ pression
  • motor control and its response systems on central nervous system level which changes lead to decreasing of complexity of the EMG- signal.
  • diseases on central nervous system level there are typically changes in the functions of neurotransmitters of the brain (e.g. dopamine in Parkinson's disease and serotonin in depression) which partly change the motor firing system and also affect the functions of response systems disadvantageously.
  • EMG-signal is usually measured in two different ways depending on what kind of measuring accuracy or analysis of results are wanted or, on the other hand, what kind is the technical operating capacity of measuring equipment.
  • the most versatile measuring and analyzing options are achieved by measuring EMG bi-polar as so- called RAW EMG-signal.
  • RAW EMG-signal By utilizing sufficiently high sampling frequency, for ex ⁇ ample 1000 - 10000 Hz it is possible to make analysis from a signal, precise enough for scientific research by means of FFT spectrum calculation.
  • Equipment, measuring RAW signal has to possess great capacity of processing and great mem- ory capacity. Another typical way of measuring is so-called averaging i.e.
  • AVERAGE-EMG method in which method the EMG-signal received from a mus ⁇ cle is first rectified and after that averaged by sliding 0,1 - 1 seconds window over the data area. Typically, loading of muscles is estimated, loading comparisons are made, activating rates and times of muscles are examined and so on by analyzing the AVERAGE-EMG-signal. Measuring of AVERAGE EMG-signal may in princi ⁇ ple be made with a rather simple equipment.
  • Each EMG-signal forms a so-called profile, in which active and passive states of the measured muscle may be seen as changes in amplitude of the signal.
  • the frequency spectrum of the signal contains information on the function of the mus- cle. Quantities describing the function of muscles among others are: - muscle load and changes in it
  • EMG-signals are transmitted typically either to be presented in real-time on a display device or signals are stored in file format for later examination and analyses.
  • the received EMG-surface sig ⁇ nal is unstationar, in which case properties and character of signal variation is not possible to be described sufficiently in traditional manners.
  • Linear methods such as FFT-analysis may be applied limited for analysing of an EMG-signal while a linear analysis of an unstationar and stochastic signal averages the result and therefore a part of the real information from the signal is lost.
  • Symptoms which may not be perceived by traditional methods from EMG, are e.g. indication of pain response and response of changes caused by a signal of a neurological disease.
  • nonlinear calculation methods For analyzing of biosignals also nonlinear calculation methods may be used, addi ⁇ tional information from which is not available using traditional methods.
  • Nonlinear methods are more applicabable for analysis of stochastic EMG-signal. These kinds of algorithms are e.g. entropy, determinism, complexity-algorithms as well as en- tropic and fractal analyses.
  • entropy e.g. entropy, determinism, complexity-algorithms as well as en- tropic and fractal analyses.
  • Several nonlinear calculation methods are commonly known. In literature e.g. RQA (recurrence quantification analysis) and Lempel-Ziv complexity have been described. They are proved to be more applicabable than lin ⁇ ear algorithms for analyzing biosignals and therefore their use applied for diagnostic use and monitoring treatment of diseases is essentially better manner.
  • Lempel-Ziv-complexity analysis and applying of it in measurements of biosignals is more closely described.
  • Lempel-Ziv complexity C(n) searches peri- odicities and regularities in an EMG-signal. "Randomization" of a finite symbol line is therefore estimated by the algorithm.
  • a simple line 0000... has the complexity of 2, as 0 and the end part of zero have to be added to it by copying.
  • the complexity of line 010101... is 3.
  • the complex- ity of a symbol line is defined by a complexity counter c(n), which is received by calculating the number of different occurence forms (words) in given symbol line.
  • the number of these occurence forms reveals the complexity or regularity of a sig ⁇ nal.
  • Usually received complexity values are normalized to 0-1 or 0-100%.
  • RQA Recurrence Quantification Analysis
  • Eckmann was the first to publish RQA in 1987 and Webber introduced it in physio ⁇ logical studies in 1994.
  • RQA method is not dependent e.g. on the quantity of data or stationarity or statistical distribution of data content, RQA is well applica ⁇ bable in physiological systems, to which sudden changes, variations of levels, noise and so on originating in living organisms are often related. For example, in measur ⁇ ing fatigue the RQA method detects the starting moment faster and more sensitively than the FFT method.
  • these analyses have not been used in disease grad ⁇ ing in diagnostic sense.
  • the objective of the invention is to provide a method and a device for identifying, measuring and analyzing abnormal neurological responses, with the use of which it is possible to identify easier, more objective and better than with present diagnostic methods phenomena of the central nervous system and the neuromuscular system and symptoms connected with abnormalities.
  • the objective of the inven ⁇ tion is to provide a method, witch enables an early diagnozing of neurological dis- eases such as Parkinson's disease and utilizing in dosing medicine.
  • neurological diseases, pains, fatigue, power producing ability and/or activeness and/or symptoms related to those are es- timated by measuring signals produced by muscles and by analyzing signals with nonlinear calculation methods.
  • Signals produced by muscles are in this context re ⁇ ferred to as signals received by measuring in various ways from muscles and from their functioning.
  • nonlinear calculation methods are not described very precise, and the method is not limited to any particular nonlinear calculation method but in the method, one or some nonlin ⁇ ear calculation methods may be used.
  • electrical signals of muscles are measured with a measuring device.
  • measuring may be made non- invasive on the skin by means of different kinds of measuring sensors.
  • the measur ⁇ ing device may be designed to be small by size, easy to handle, wearable and the processing of results may be realized by means of a computer or an embedded measuring system.
  • Advantageously electrical signals of active muscles are measured.
  • pa- rameters to be calculated from measuring signals may be defined fast and reliably.
  • other signals produced by muscles than electrical signals may be measured with a measuring device on the skin by means of suitable measuring sensors.
  • acceleration, pressure and tem ⁇ perature are these kinds of signals.
  • tremor or functional disorders cause abnormal shapes of acceleration and pressure signals, which shapes may be identified in signal analysis phase.
  • time-related stored image se ⁇ ries or bit map describing the recruitment of different parts of muscles produced by means of magnetic resonance imaging (MRI) or ultrasound imaging may represent this kind of measuring manner.
  • sensors are not placed on the skin.
  • the results received by the method are compared with the reference results stored in a database. In this case, differences between normal and abnormal result, changes and the stage of pathol ⁇ ogy may be defined.
  • the results received by the method are transmitted to a user by means of a feedback device.
  • the user may be e.g. a relative, a person doing or monitoring the study in a hospital, in a nursing home, at home with the person to be examined or in some other place.
  • a dosing of medicine used for treating e.g. Parkinson's disease or some other disease or for some other reason may be optimized in accordance with received results.
  • the results received by the method are transmitted to the person examined by means of a personal feedback de ⁇ vice.
  • the person him/herself may carry out the test or similar, and get feedback about changes in his/her activeness, development of motoric skills or need for dosing of medicine at home, and while needed get in touch with the person treat ⁇ ing him/her.
  • Nonlinear analysis is more applicabable than linear methods for analysis of biosig- nals due to stochastic and non-stationary character of signals.
  • analysis more information e.g. about pain reflection and disorders of the motoric and premo- toric functioning model as well as from changes on cerebellum and corpus callosum level may be received.
  • weakening of neurotransmitters such as dopa ⁇ mine and serotonin levels affect the complexity of an EMG-signal measured on a muscle and so-called fatigue changes are stronger than on a normal group.
  • EEG-signal Functioning of the central nervous system is commonly examined with measuring of so-called EEG-signal, which is carried out through electrodes placed on the scalp.
  • EEG measures electrical functioning in the brain. For controlling the body and especially muscles also impulses going through the central and the peripheral nervous system are needed.
  • EMG describes the control of the central nervous sys ⁇ tem and the motoric cortex more precise than EEG, while an EEG-signal is con- ducted through the scalp while measuring and EEG contains also responses related to control of vital functions and sense perception.
  • the nonlinear estimation method application of an EMG-signal enables e.g. follow ⁇ ing applications:
  • results received by the method are compared to a database with reference results stored in it from patients suffering from neurological diseases and, on the other hand, from healthy test persons, it can be seen if the EMG-signal of the person examined has characteristics referring to a disease or deviation of calculation pa- rameters from normal reference values.
  • Measurements made by the method and results from those may, while needed to be transmitted to the person who made the study, i.e. the user or to the person who was examined or to relatives by means of various display devices or corresponding feedback devices or to the person who was examined by means of a personal port- able feedback device for possible further actions.
  • the method is applied by means of measuring system e.g. for diagnozing of pain and Parkinson's disease, for follow-up of treatment of Parkinson's disease, for ob ⁇ jective measuring of muscle fatigue and in general for follow-up of changes in ac ⁇ tiveness of the central nervous system, in which method from the EMG-signals re- ceived from muscles by means of nonlinear calculation parameters connected with earlier mentioned diseases and symptoms are measured and compared if necessary to reference values gathered in database or to own earlier values concluding from changes or deviations proceeding of a disease, pain status or mentioned application action.
  • measuring system e.g. for diagnozing of pain and Parkinson's disease, for follow-up of treatment of Parkinson's disease, for ob ⁇ jective measuring of muscle fatigue and in general for follow-up of changes in ac ⁇ tiveness of the central nervous system, in which method from the EMG-signals re- ceived from muscles by means of nonlinear calculation parameters connected with earlier mentioned diseases and symptoms are measured and
  • the method is utilized in diagnosing and treating of Parkinson's disease.
  • Patients with Parkinson's disease suffer from changes in motoric control and its response system on the central nervous system level, which changes are caused by changes of dopamine level. These changes may be seen as decreasing of complexity of the EMG-signal, which may be detected by a nonlinear analysis such as e.g. RQA. Based on this the follow-up of Parkinson's disease patients or risk groups may be realized before outbreak of the disease.
  • EMG measured on the skin measures responses of motor units of a muscle, but util ⁇ izing nonlinear methods it is possible to analyze primitive mechanisms controlling muscle functioning of the central nervous system level.
  • neurological diseases es ⁇ pecially in Parkinson's disease
  • these primitive controlling mechanisms are dam ⁇ aged and insufficient by function.
  • Parkinson's disease is the most important disease of so-called extrapyramidal system of the brain, the cause of which is unknown.
  • 60-80 % of the dopaminergic nigrostriatal pathways of the so-called substantia nigra located in the midbrain has been damaged or they are dysfunc ⁇ tional.
  • Parkinson's disease The four main symptoms of Parkinson's disease are resting tremor, rigidity (stiffness), hypokinesia (slowness of movements) and slowening of the correction reflex of balance.
  • the most typical symptom is the resting tremor.
  • the diagnosis of Parkinson's disease is clinical, which is made on grounds of symptoms and the clinical detections of a physician. There are no objective methods as routine in clini ⁇ cal use. Before Parkinson's disease has been clinically ascertained, the disease proc- ess itself has already proceeded far. By means of newer EMG analysis methods, it is possible to trace the disease already in the early phase of the disease. This would es ⁇ sentially help to make the diagnosis and to start the treatment earlier.
  • Figure 1 illustrates an example of an EMG-signal during contraction
  • Figure 2a illustrates an EMG-signal of a healthy person
  • Figure 2b illustrates an EMG-signal of a Parkinson patient.
  • EMG-signal is a sum signal of individual action potentials caused by motor units in a muscle. Normal motoric control of a muscle causes always slight fading in an EMG-signal even visible in the figure as well as separate and oc- casional spikes. Proportion of various deviations on a healthy person is quite small and no regularities may be found.
  • FIG 2a and 2b there are EMG measurements of three seconds duration on a healthy control person (figure 2a) and on a Parkinson patient (2b) presented.
  • the measurements have been made during an isometric muscle contraction when the load of muscles remains as constant as possible on the average. Differences in re ⁇ cruiting of motor units on the healthy test person and on the Parkinson disease pa ⁇ tient are visible with the eye.
  • the signal of the healthy person some individual spikes higher than the variation of the base level may be seen.
  • the signal of the Parkinson patient clearly more signal spikes stronger than the base level may be seen and they repeat more regularly and more organized than on the healthy test person. Increasing of strong spikes is caused by interruptions in motoric control.
  • the motor units of the muscle function more synchronized among each other, which means growing of the signal level in the EMG-signal.
  • the excessive synchronization of motor units means tremor of muscles and absence of fine motoric control necessary for even motion.
  • the diagnosis method of Parkinson's disease is based on measuring made on a test person with an EMG-equipment and comparing of measuring results to comparison data in a database.
  • an EMG-measurement is carried out on the muscles of the arm as the motoric control of arms is the most developed form of neuromuscular function.
  • EMG-signal samples are taken with various standard motion course and power tests, in which case comparison of results with the database is possible.
  • the motion courses to be tested are e.g. removing of an object of certain size and weight from one place to another along defined course. Along the course, the arm and the wrist must simultaneously e.g. clasp the object, move in horizontal and vertical di ⁇ rections as well as twist from one extreme position to another.
  • the spon ⁇ taneous EMG-responses of the muscle may also be analyzed and separate the healthy and the Parkinson patients on grounds of the measuring data by a normal every day muscle activation analysis.
  • the comparison database contains sample data both from the healthy and from the patients in various phases of Parkinson's disease.
  • the EMG-sample from a test person may give suggestions not only about the first symptoms of the disease but also about the proceeding degree of the disease.
  • the results are normal ⁇ ized on grounds of data base comparison such that the proceeding degree may be categorized with a suitable scale.
  • the method may also be applied in dosing of medical treatment of Parkinson's dis ⁇ ease.
  • the patient has an automatic dosing apparatus adjusted, which e.g. infuses medicine to the blood circulation or to the small intestine of the patient by means of estimation of the dopamine level based on EMG-measuring.
  • EMG-measuring is made, for example, by means of a measuring wristband, which is easy to use and continuously functioning.
  • a measuring wristband or other sensor is placed, for ex ⁇ ample, on the hand, feet and so on.
  • Response from the measuring signal is analyzed at regular time intervals, for example every 5 min.
  • the analysis of which has been carried out by means of some nonlinear algorithms men- tioned has visible signs of falling of the dopamine level, the information is trans ⁇ mitted to the device the patient is carrying.
  • the device may be a dosing pump or some other dozer of medicine, which has e.g. an alarm system for starting the dos ⁇ ing or for a dose of medicine to be taken internally.
  • sy ⁇ ringing may start automatically through an invasive canule. It is also characteristic to Parkinson's disease that the symptoms appear first on one limb, in which case by comparing signals both on the left and the right limb the ending of the influence of medicine may be anticipated in good time.
  • Overdosing of medicine may also cause adaptation of a patient to dopamine as the result of which the influence of medicine dose weakens. At the worst adaptation may facilitate the progression of the disease or cause premature death.
  • a possible medicine overdosing may be avoided.
  • pain response experienced by pa ⁇ tients suffering from back pain in comparison with feelings of patients with similar by performance ability of muscles but with no pain.
  • Condition of back muscles of patients with back problems has weakened and a part of patients suffers from pain also while loading muscles.
  • Patients with back pain may be separated from healthy patients who have been through treatment/training by means of FFT method based on linear analysis, but the method does not separate individually pain patients. Identification of pain patients is successful while using the nonlinear RQA method and the method makes it possible to make a measurement from a measuring signal before rehabilitating exercise part and after it, whereas earlier the measurement had to be carried out with a linear method during the whole performance.
  • FFT calculation gives suggestive results also during dy ⁇ namic motion but muscle contractions varying continuously and interferences due to motion change the frequency content of the EMG-signal such that the results from FFT calculations are not as precise and reliable as in static measurement.
  • FFT calculation made of dynamic motion may be specified by making use of motion sensors attached to a person during the test, by means of which sensors the interfer ⁇ ences created in signals may be eliminated or limit the areas included in FFT calcu ⁇ lation to advantageous in terms of calculation. In this case fatigue may be measured reliably e.g. during work or sports performance with a measuring/displaying device to be carried with a person.
  • An advantageous application of the invention measures development of motoric skills of a person.
  • the method is utilized e.g. in rehabilitation after a person has been disabled, in training of professions requiring great accuracy or e.g. in sports training.
  • EMG-signal is measured from one or several muscles during a perform- ance. During the measurement a specific performance and its training part are aimed to be specified e.g. a golf swing or a tennis serve.
  • the measuring sensors are placed on the skin overlying muscles participating in the performance at the middle of the muscle.
  • the measuring is carried out through disposable sensors or through sensors integrated in clothing.
  • the information of measuring is stored in a measuring device on the wrist, on the waist or in the pocket, which measures the EMG-signal with recognized methods.
  • the measuring signal is calculated by means of various calcu ⁇ lation algorithms, by calculating e.g. complexity values during the performance.
  • the performance is tried to be separated into various phases and the result of the performance is fed also into the memory of the device.
  • Complexity values from the entire performance are calculated and stored into the memory.
  • the change is compared to the received performance results. Later on, the values may be utilized while restoring and further developing motoric skills.
  • the results may be compared to the values of the data- base and in this way estimate the development of motoric learning and skill objec ⁇ tively.
  • the method is utilized for measur ⁇ ing the activeness level of a person which is necessary for follow-up of changes in activeness state of the central nervous system due to various reasons e.g. in connec ⁇ tion with diseases such as dementia, Alzheimer disease, depression, chronic/acute fatigue and so on. Control of the state of fatigueness is useful also in some working positions requiring continuous concentrating such as surgery, flying/air control and so on.
  • the median value of a person is defined for complexity, which is compared to reference data.
  • the person to be measured carries a measuring device, which measures at regular time intervals e.g. EMG-signal from the muscles of the arms, from which signal some of the parameters complexity, entropy or determin ⁇ ism is calculated. From the value, e.g. the mean value of a day or a week is calcu ⁇ lated.
  • the change with respect to the mean value on a long time period describes the change in activeness, variation in serotonin level, motoric learning and so on.
  • the device in accordance with the invention comprises sensors to be placed in the body or on the body surface for measuring signals produced by muscles, a device for analysis of the signals by nonlinear calculation methods and a feedback device for giving feedback.
  • the device comprises advantageously sensors especially for measuring electrical signals produced by muscles.
  • the sensors in themselves are conventional sensors for this purpose.
  • the device for analysis of signals processes and analyzes signals and it has the necessary programmes for processing and ana ⁇ lyzing signals with nonlinear calculation methods as earlier presented.
  • the device comprises one or several recognized feedback devices, with which the user, the person examined or possibly some other person may receive information about the study and its results in real-time or later. Naturally, the information is also stored for later use.

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Abstract

La présente invention concerne un procédé et un dispositif d'identification, de mesure et d'analyse de réponses neurologiques anormales. Selon ce procédé, pour évaluer les pathologies neurologiques, la douleur, la fatigue, l'aptitude à produire de l'énergie et/ou l'activité et/ou les symptômes en liaison avec elles, on mesure les signaux produits par les muscles et on analyse les signaux avec des procédés de calcul non linéaires. Le dispositif de l'invention comprend des capteurs à placer dans le corps ou à la surface du corps de façon à mesurer les signaux produits par les muscles, un analyseur de signal pour analyser les signaux avec des procédés de calcul non linéaire, et un module de rétroaction pour donner de l'information en retour.
PCT/FI2005/000330 2004-07-20 2005-07-18 Procede et dispositif d'identification, mesure et analyse de reponses neurologiques anormales WO2006008334A1 (fr)

Priority Applications (2)

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EP05771542A EP1838214A4 (fr) 2004-07-20 2005-07-18 Procede et dispositif d'identification, mesure et analyse de reponses neurologiques anormales
US11/606,418 US20070118044A1 (en) 2005-07-18 2006-11-30 Method and device for identifying; measuring and analyzing abnormal neurological responses

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US58992804P 2004-07-20 2004-07-20
US60/589,928 2004-07-20

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Cited By (9)

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EP1859737A1 (fr) * 2006-05-24 2007-11-28 Casio Computer Co., Ltd. Appareil et système de mesure d'informations de corps vivant
WO2008122082A1 (fr) * 2007-04-04 2008-10-16 Brc Ip Pty Ltd Système et procédé de mesure de paramètres de fonctionnement du cerveau
WO2008131782A1 (fr) * 2007-04-30 2008-11-06 Ictalcare A/S Dispositif et procédé pour surveiller une activité musculaire
US8983591B2 (en) 2010-10-15 2015-03-17 Brain Sentinel, Inc. Method and apparatus for detecting seizures
US9186105B2 (en) 2011-07-05 2015-11-17 Brain Sentinel, Inc. Method and apparatus for detecting seizures
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WO2008122082A1 (fr) * 2007-04-04 2008-10-16 Brc Ip Pty Ltd Système et procédé de mesure de paramètres de fonctionnement du cerveau
WO2008131782A1 (fr) * 2007-04-30 2008-11-06 Ictalcare A/S Dispositif et procédé pour surveiller une activité musculaire
US8386025B2 (en) 2007-04-30 2013-02-26 IctalCare A/S, Delta Device and method for monitoring muscular activity
US8983591B2 (en) 2010-10-15 2015-03-17 Brain Sentinel, Inc. Method and apparatus for detecting seizures
US10226209B2 (en) 2010-10-15 2019-03-12 Brain Sentinel, Inc. Method and apparatus for classification of seizure type and severity using electromyography
US9186105B2 (en) 2011-07-05 2015-11-17 Brain Sentinel, Inc. Method and apparatus for detecting seizures
WO2017165970A1 (fr) * 2016-03-29 2017-10-05 The Governors Of The University Of Alberta Système d'évaluation de la colonne vertébrale
US10736525B2 (en) 2016-04-19 2020-08-11 Brain Sentinel, Inc. Systems and methods for characterization of seizures
EP3415083A1 (fr) * 2017-06-15 2018-12-19 Bittium Biosignals Oy Procédé et module de surveillance portable pour surveiller une pluralité de biosignaux électriques d'une personne
US11197629B2 (en) 2017-06-15 2021-12-14 Bittium Biosignals Oy Method and portable monitoring module for monitoring a plurality of electrical biosignals of a person

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