EP1588280A2 - Systeme et procede de traitement de signaux electromyographiques pour le diagnostic de la maladie de parkinson - Google Patents

Systeme et procede de traitement de signaux electromyographiques pour le diagnostic de la maladie de parkinson

Info

Publication number
EP1588280A2
EP1588280A2 EP03815575A EP03815575A EP1588280A2 EP 1588280 A2 EP1588280 A2 EP 1588280A2 EP 03815575 A EP03815575 A EP 03815575A EP 03815575 A EP03815575 A EP 03815575A EP 1588280 A2 EP1588280 A2 EP 1588280A2
Authority
EP
European Patent Office
Prior art keywords
accordance
wavelet
signals
cross
correlation
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP03815575A
Other languages
German (de)
English (en)
Inventor
Maria Chiara Carboncini
Gennaro De Michele
Bruno Rossi
Stefano Sello
Soo-Kyung Strambi
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Enel Produzione SpA
Original Assignee
Enel Produzione SpA
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Enel Produzione SpA filed Critical Enel Produzione SpA
Publication of EP1588280A2 publication Critical patent/EP1588280A2/fr
Withdrawn legal-status Critical Current

Links

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
    • A61B5/316—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
    • 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
    • A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms

Definitions

  • the present invention relates to a system and a method of processing electromyographic signals particularly useful for the diagnosis of Parkinson's disease and, more particularly, for an objective evaluation of the level of gravity of the illness.
  • the methods currently employed for assessing the level of gravity of Parkinson's disease and monitoring its time evolution are essentially of a clinical and/or an instrumental type.
  • the semi-quantitative methods such as the Unified Parkinson's Disease Rating Scale (U.P.D.R. S . ) , form part of the first type.
  • U.P.D.R. S . Unified Parkinson's Disease Rating Scale
  • This method consists of the filling in by the patient of questionnaires in which the symptoms are attributed numerical values that increase with the gravity of the symptom.
  • the specific scale used for this purpose depends on the particular purpose that the examiner sets himself: Unified Parkinson's Disease Rating Scale (U.P.D.R.S. ) , Hoen Yar (H.Y.), Fatigue scale, etc., Since the semi-quantitative evaluation consists of a questionnaire that has to be completed by the patient under examination, it can easily be altered by subjective factors, among them the emotive state Of the patient, which also exerts a fundamental influence on his motorial performance .
  • U.P.D.R.S. Unified Parkinson's Disease Rating Scale
  • Hoen Yar H.Y.
  • Fatigue scale etc.
  • the second type comprises C.A.T. and N.M.R. analyses of the brain and the analysis of the kinetic and electromyographic (EMG) parameters recorded during specific movements and on particular muscles .
  • EMG electromyographic
  • the instrumental Evaluations are reliable examinations that detect in a detailed manner the areas of the brain involved in the worsening of the pathology.
  • the evaluation of the area of the trace does not always represent a significant index, and this not only because equal areas can correspond to different traces, but also because it is possible for significant variations of the patient's clinical state can give rise to area variations of little statistical significance; b) visual description of the trace.
  • EMG signals are of the non-stationary type with highly complex time-frequency characteristics. They generally consist of brief high-frequency components closely , spaced in time, followed by low-frequency components differing only very slightly in frequency.
  • the mathematical instrument most commonly employed in biomedical applications is the Fourier transform, which decomposes a given signal into its frequency components.
  • biomedical signals are non- stationary and have an intermittent and variable frequency content .
  • the object of the present invention is to solve the aforesaid problems associated with prior art techniques and to provide a method as described in claim 1.
  • the present invention also concerns a system as described in the independent claim 12, specifically . suitable for implementing the aforesaid processing method.
  • Another object of the present invention is to provide a computer product, particularly a software, capable of implementing a method in accordance with the present invention.
  • the principal aim of the present invention is to provide a methodology suitable for determining efficient qualitative and quantitative parameters useful in the assessment of the level of gravity of Parkinson's disease. This is done by means of a particular analysis of the EMG signals: the Wavelet cross-correlation analysis.
  • the principal advantage of the method in accordance with the present invention is that when the Wavelet cross- correlation analysis is carried out on a pair of EMG signals, it makes it possible to find characteristic Wavelet Analysis Maps .(W.A.M.) and a numerical index, known as the Parkinson Disease Index (P.D.I.) , correlated in an unequivocal manner with the level of gravity of the disease.
  • P.D.I. Parkinson Disease Index
  • a second advantage derives from the fact that the processing of the maps and the calculation of the P.D.I, can be carried out also by means of a dedicated hardware system connected with the data acquisition equipment used for detecting the EMG signals, thus making it possible for data about the patient's state to be obtained in real time and to make a quick assessment of the effect of pharmaceuticals and therapies .
  • Figure 1 shows a block diagram of a system in accordance wit the present invention
  • Figure 2 shows a flow diagram of the processing method in accordance with the present invention
  • FIGS. 3 to 9 show schemes and graphs of the tests carried out for the validation of the method in accordance with the present invention. Detailed description of the invention
  • a system in accordance with the present invention comprises means for the acquisition of data corresponding to electromyographic signals (EMG) .
  • EMG electromyographic signals
  • a central control unit comprising means for processing the data, receives these data as input, processes them and, as output, provides corresponding information relating to predetermined parameters in a format suitable for being displayed on a monitor, printed or reproduced in some other manner.
  • the analysis and processing of the input data is carried out off line.
  • the acquisition of the EMG signals is carried out with the help of detection equipment of a conventional type, capable of memorizing the acquired signal in digital format in the form of files on any kind of support (floppy disk, CD or other) .
  • Each file in ASCII format for example, contains some general information for the processing (type of signal, number of data items, acquisition rate) and a series of number pairs indicating respectively the time variable and the value of the selected EMG channel .
  • the files are then read by the control unit, which thereupon commences the processing procedure for the purpose of obtaining the previously indicated maps and gravity indexes .
  • a system in accordance with the present invention comprises the instrumentation for acquiring the EMG signals, their transfer in real time to the central control unit, which processes them in real time and instantaneously provides the results o the analysis.
  • the subsequent Figure 2 shows a flow diagram of the processing method in accordance with the present invention.
  • the macro-operations on the input data are represented in sequence in the diagram of Figure 2.
  • the cross-correlation Wavelet transform of the EMG signals is calculated.
  • the Wavelet cross-relation analysis is applied in an iterative manner to a sample pair of EMG signals acquired during the signal measurement session.
  • the analysis of 10 pairs of EMG signals is sufficient for an adequate statistical valuation (Step S2) of the results that will bring out the most probable values and the respective indeterminacies of the calculated integral and local Wavelet quantities.
  • Wavelet Average map W.A.M.
  • step S4 This makes it possible to display (step S4) the value of the P.D.I, and the morphology of the energy content of the most representative cross-correlation for a given patient both in time and in frequency and to carry out qualitative comparisons between different patients or for a given patient in the course of time.
  • step S4 it makes it possible to assess with considerable sensitivity the effect of a given pharmacological therapy on Parkinson patients by making direct use of the EMG signals.
  • the continuous wavelet transform represents an optimal localized decomposition of time series, x (t) , as a function of both time t and frequency (scale) a:
  • ⁇ is an analyzing wavelet verifying the following admissibility condition:
  • C ⁇ and ⁇ ( ⁇ ) are the admissibility constant and the Fourier transform of the wavelet function, respectively.
  • the parameters a and r denote the dilation (scale factor) and translation (time shift parameter) , respectively.
  • the local wavelet spectrum is then defined as :
  • k 0 denotes the peak frequency of the analyzing wavelet ⁇ .
  • the Morlet wavelet function has been advantageously selected which allows to get amplitude and phase information on the signals under examination at a given scale a-.
  • W f ( -t, T) and W g [a, ⁇ ) be respectively the wavelet transforms of two real signals: f (t) and g (t) .
  • the energy at a given scale is not evenly distributed in time and/or in space and any variable alternates high activity with quiescence.
  • the correction constant 3 is related to the kurtosis of the Gaussian distribution.
  • variable is said to be "intermittent" .
  • the force thus generated could be monitored on an oscilloscope screen.
  • This sequence was repeated ten times at regular intervals of 30 - 120 seconds in order to extract statistically significant results related to the recorded signals.
  • the recording of the electromyographic signals was obtained by means of an integrated system, comprising an 8 -channel electromyograph and two telecameras connected with a main processor (an ELITE system, BTS for example) .
  • the electromyographic activity was measured by means of surface electrodes spaced about 1 centimeter apart and arranged longitudinally with respect to the fibres of the muscle.
  • the recordings of the EMG signals were differentially amplified, analog-to-digital converted (at 500 Hertz, and anti-aliasing filtered with 12-bit resolution) and stored in a computer.
  • analog-to-digital converted at 500 Hertz, and anti-aliasing filtered with 12-bit resolution
  • This scale consists of six sections that define motorial, cognitive, therapeutic and relational functions. Each function is assigned a score ranging from 0 (least ill) to 4 (most ill) and this value provides information regarding the progress of the illness.
  • Figure 4 show an example of the results obtained when the processing method in accordance with the present invention is applied to a pair of EMG signals recorded in the case of a diseased patient during the previously described ballistic movement.
  • Figure 5 provides the same information in the case of a healthy subject.
  • the central panel shows the Wavelet map of the power cross-correlation obtained by using a chromatic scale for correlating the energy intensity of the signal .
  • the horizontal axis represents the tine variable expressed in seconds, while the vertical axis shows the frequency values in accordance with a logarithmic scale.
  • the panel on the right shows the global power spectrum obtained by a time integration of the corresponding local power spectrum.
  • the selected frequency range is comprised between 7.4 and 165 Hz, because this interval contains all the significant components of the cross-correlation energy.
  • a more irregular and dispersed movement is related to the lower muscle synchronization and coordination.
  • the unsteady and intermittent character (more extensive tails than those associated with a Gaussian distribution) quickly appears from a purely qualitative evaluation of the Wavelet maps. More precisely, Figure 6 shows the measure of intermittence as a function of the frequency calculated in both cases .
  • the average intermittency in the case of a diseased patient is generally greater than that of a healthy subject (broken line) .
  • the intermittency derived from the Wavelet analysis represents a typical quantity of immediate interest for diagnostic purposes .
  • the statistical procedure used for this purpose is as follows: as set of possible realizations there is selected the set of test sequences repeated for each subj ect ; the sample parameter is assumed to have a normal distribution; the confidence level (95%) is evaluated for the first two distribution moments; an F-test is carried out (using a Fischer-
  • test sequence was carried out and recorded for ten consecutive times in the case of each patient.
  • Figure 8 shows the global Wavelet power (P.D.I.) together with the associated statistical uncertainty.
  • the experimental data show that there exists a threshold between the two classes of subjects. This threshold corresponds to a P.D.I, value of about 3500 (a.u. ) .
  • Table 1 shows the results with the quantitative U.P.D.R.S. classification.

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  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • Pathology (AREA)
  • Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Physics & Mathematics (AREA)
  • Biophysics (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Neurology (AREA)
  • Neurosurgery (AREA)
  • Developmental Disabilities (AREA)
  • Physiology (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

L'invention concerne un procédé de traitement de signaux électromyographiques (f(t), g(t)) pour le diagnostic de la maladie de Parkinson chez un patient, ce procédé comprenant une étape fondée sur une analyse en ondelettes desdits signaux électromyographiques, cette analyse permettant de déterminer une valeur (P.D.I.) de l'énergie de corrélation croisée globale, laquelle indique le degré de gravité de la maladie chez le patient.
EP03815575A 2003-01-31 2003-07-31 Systeme et procede de traitement de signaux electromyographiques pour le diagnostic de la maladie de parkinson Withdrawn EP1588280A2 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
IT000024A ITFI20030024A1 (it) 2003-01-31 2003-01-31 Sistema e metodo di elaborazione di segnali elettromiografici
ITFI20030024 2003-01-31
PCT/IT2003/000486 WO2004066832A2 (fr) 2003-01-31 2003-07-31 Systeme et procede de traitement de signaux electromyographiques pour le diagnostic de la maladie de parkinson

Publications (1)

Publication Number Publication Date
EP1588280A2 true EP1588280A2 (fr) 2005-10-26

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Application Number Title Priority Date Filing Date
EP03815575A Withdrawn EP1588280A2 (fr) 2003-01-31 2003-07-31 Systeme et procede de traitement de signaux electromyographiques pour le diagnostic de la maladie de parkinson

Country Status (4)

Country Link
EP (1) EP1588280A2 (fr)
AU (1) AU2003253298A1 (fr)
IT (1) ITFI20030024A1 (fr)
WO (1) WO2004066832A2 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108270543A (zh) * 2017-11-22 2018-07-10 北京电子科技学院 一种基于小波空域相关法的侧信道攻击预处理方法

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2006008334A1 (fr) * 2004-07-20 2006-01-26 Mega Elektroniikka Oy Procede et dispositif d'identification, mesure et analyse de reponses neurologiques anormales
US8386025B2 (en) 2007-04-30 2013-02-26 IctalCare A/S, Delta Device and method for monitoring muscular activity
US10226209B2 (en) 2010-10-15 2019-03-12 Brain Sentinel, Inc. Method and apparatus for classification of seizure type and severity using electromyography
CN102274016B (zh) * 2011-07-08 2013-01-23 重庆大学 一种颅内压信号特征峰识别方法
AU2017253093A1 (en) 2016-04-19 2018-11-15 Brain Sentinel, Inc. Systems and methods for characterization of seizures
CN105997064B (zh) * 2016-05-17 2018-10-23 成都奥特为科技有限公司 一种用于人体下肢表面肌电信号的辨识方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See references of WO2004066832A2 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108270543A (zh) * 2017-11-22 2018-07-10 北京电子科技学院 一种基于小波空域相关法的侧信道攻击预处理方法

Also Published As

Publication number Publication date
AU2003253298A1 (en) 2004-08-23
WO2004066832A3 (fr) 2004-12-29
ITFI20030024A1 (it) 2004-08-01
WO2004066832A2 (fr) 2004-08-12
AU2003253298A8 (en) 2004-08-23

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