CA1213329A - Muscle tone detector for sleep analysis - Google Patents
Muscle tone detector for sleep analysisInfo
- Publication number
- CA1213329A CA1213329A CA000439062A CA439062A CA1213329A CA 1213329 A CA1213329 A CA 1213329A CA 000439062 A CA000439062 A CA 000439062A CA 439062 A CA439062 A CA 439062A CA 1213329 A CA1213329 A CA 1213329A
- Authority
- CA
- Canada
- Prior art keywords
- muscle tone
- tone detector
- sleep
- amplitude
- detector
- 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.)
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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
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
- A61B5/4812—Detecting sleep stages or cycles
-
- 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]
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- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Physics & Mathematics (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
TITLE
MUSCLE TONE DETECTOR FOR SLEEP ANALYSIS
INVENTORS
Roger Broughton Bernardo Da Costa ABSTRACT OF THE DISCLOSURE
The muscle tone detector periodically samples the electromyo-gram (EMG) signal to determine the level of muscle activity for sleep analysis. The signal is sampled periodically for nearly two second periods, the amplitude of the components of the sample that have a real-time frequency greater than 22 Hz, is then monitored and categorized in one of five levels, i.e. below 12 µV, from 12-29 µV, from 29-45 µV, from 45-57 µV and above 57 µV. The detector output is a series of one to five pulses for each sample period.
MUSCLE TONE DETECTOR FOR SLEEP ANALYSIS
INVENTORS
Roger Broughton Bernardo Da Costa ABSTRACT OF THE DISCLOSURE
The muscle tone detector periodically samples the electromyo-gram (EMG) signal to determine the level of muscle activity for sleep analysis. The signal is sampled periodically for nearly two second periods, the amplitude of the components of the sample that have a real-time frequency greater than 22 Hz, is then monitored and categorized in one of five levels, i.e. below 12 µV, from 12-29 µV, from 29-45 µV, from 45-57 µV and above 57 µV. The detector output is a series of one to five pulses for each sample period.
Description
~33Z~
Background of the Invention Thls invention is directed to auto~atic sleep analysers and, ln particular, to detectors for the specific events used in ~taging sleep.
A scoring system for staging the sleep patterns of adult humans has been standardized and is described ln the manual edited by A. Recht-schaffen and A. Kales entitled, A Manual of Standardi~ed Terminology, Techniques and Scoring Sys~em for Sleep Stages of ~uman Sub~ect~ .
In scorlng sleep, three basic signals recorded as electrical activity in the body, are required. These are the sctivity of the brain, the eyes and the muscles. The activity of the braln is represented by an electroencephalographic (EEG) signal obtained from electrodes placed on the head. The activity of the eyes i8 represented by electro-oculo-graphic (EOG) signals obtained from electrodes placed near each eye. The muscle tone activity is represented by an electromyographic (EMG) signal obtained from electrodes usually located under the chin.
The activity signals would normally be recorded on a paper printout, and divided into time segments or epochs9 e.g. of forty sec-onds. Specific ~vents are noted visually during each epoch ln order to classify that epoch as a certain state of sleep or non-sleep. The con-ventlonal seven states of sleep or non-sleep are known as wakefulness, stage 1 sleep9 stage 2 sleep, stage 3 sleep, stage 4 sleep, REM sleep and movement time. These are listed in Table 1 together with the criteria for each epoch state used in the classification. The events used to stage or classify these states are alpha rhythm, sleep spindles, delta activity, and movement ti~e which are observed in the EEG signal, rapid eye movements (REM) which is observed in the EOG signal, and muscle tone which ls observed in the EMG signal.
Criteria for Sleep Staging Event State W 1 2 3 4 RF.M M.T.
_ Alpha + 0 0 0 0 Spindles 0 0 + + + 0 +
Delta 0 0 <20% 20-50%50-100% 0 +
Mov't artlfact ~ 0 o 0 0 0 > 50%
REM + 0 0 0 0 + +
EMG ~ + ~ + 1 0 +
Table 1 lists the events and their levels which ar~ to be observed during an epoch in order to classlfy it into a partlcu]ar state.
However, in addition to this table, certain guidelines exist for staging sleep by which tlle state ln each epoch can only be deter~ined by observ-ing events that occur in previous or subsequent epochs.
Traditional sleep recording with a monitoring technologist is very time consuming and expenslve, involving overnight shift work and slow vlsual analysis of very long paper recordings. The need for a moni-toring technologist can be avoided and, in many cases, be replaced by using portable recordings placed on the subJect to record the required signals continuously in his normal home environment. The slow visual analysis of long paper recordings can be circumvented by the use of auto-matic analysis, at high speed playback, of tape recorded data from either portable or traditional in-laboratory recordings~ Automatic analysis can replace ~uch long recordings by summary statistics and charts, and also improve scoring consistency.
A number of centers have attempted various approaches to auto-matic sleep analysis as a particular extension of the problem of auto-matic E~G analysis. Sleep EEG events have most frequently been detected by spectral analysis, by pattern recognition, and by period analysis of zero-crossing data. As well, digital filters have recently been intro-duced and have potential appllcation in the field. Combinations of these methods have sometimes been used to detect individual sleep EEG events which combine zero-crossing analysis w~th an amplitude criterion, a period discri~inator to determine frequency band (delta, alpha, splndle, beta or muscle potential), plus a pattern criterion. The staging of sleep may be done using detectors based on the above approaches which are then combined in a "hard-wired" processing unit. Alternately, all data processing for sleep staging may be done by a large general purpose com-puter. The hard-wired sleep stagers have the advantage of lower cost, but the great disadvantage of being inflexible. Performing all analyses on digitalized raw data in a necessarily large general purpose computer, on the other hand, is very expensive.
An intermediate approach, in which the present invention is used, has a series of (sometimes modifiable) event detectors as part of a preprocessor unit. The detectors detect essentially onl~ those events ~2~ 3;~Z9 which are used for vLsual analysis. Their outputs can then be analysed for quantification of sleep variables and for sleep staging, either visu-ally, or automatically by a microprocessor or a small general purpose computer. Gaillard and Tissot have chosen a somewhat similar approach, as described in their publication, Principles of automatic analysis of sleep records with a hybrid system , Comp. Biomed. Res~, 1973, 6:1-13.
In this system the outputs of a preprocessor consisting of 12 bandpass filters for EEG analysis, an eye movement analyser, a muscle integrator, an EKG counter, and a galvanic skin response (GSR) counter are coupled to a small general purpose computer. Such an approach combines the advantages of relatively low cost and flexibility.
As described above, the events to be detected are alpha rhythm, sleep splndle, delta activity, and movement artifact in the EEG signal, plus REMs and ~uscle tone.
The alpha rhythm in automatic sleep analysers is generally detected using a classical bandpass filter or zero-crossing detector and a level discriminator. A particularly useful phase-locked loop alpha detector is described in the thesis entitled, A Hybrid Pre-Processor for Sleep Staging Using the EEG , by D. Green, 1977, Chapter 7, pp. 1 to 13.
This detector produces an output, when the EEG signal has a component with a frequency of 8-12 Hz at greater than 25 ~V peak-to-peak amplitude.
The sleep spindle is the sleep EEG event most comprehensively examined to date. The approaches to spindle detection include: zero-crossing methods, classical analogue bandpass filtering, bandpass filter-ing with harmonic analysis, a software Fast Fourier Transform (FFT)approach, a matched filter approach, and a phase-locked loop (PLL) approach. A highly accurate sleep spindle detector is described in the publication by R. Broughton et al, entitled 'A Phase-locked Loop Device for Automatic Detection of Sleep Spindles and Stage 2 , Electroencephalo-graphy and Clinical Neurophysiology, 1978, 44: 677-680. This detector produces an output when the EEG signal has a component with a frequency of 11.5 15 Hz at greater than 14 ~V peak-to-pesk and a minimum burst duration of 0.5 seconds.
Delts activity detection can be performed by using analogue bandpass filters with energy detectors, by zero-crossing analysls using $~33~9 amplitude and period criteria, or by a software approach. A particularly useful delta detector which detects components of the EEG signal having a frequency of 0.5-1.5 Hz at greater than o7 V peak-to-peak, is described in the above noted thesis, chapter 9, pp. 1 to 10.
0~ the three remaining event detectors required for s]eep anal-ysis, a Conjugate Eye Movement Detector For Sleep Analysis is described in co-pending Canadian patent app]ication 439,063 filed on October 14, 1983 by R. Broughton et al and a Movement Arti~act Detector For Sleep Analysis is described in co-pending Canadian patent application 439,061 filed on October 14, 1983 by R. Broug~lton et al.
_mmary of the Invention It is therefore an object of the present invention to provide a muscle tone detector for a sleep analyser, This and other objects are achieved in a muscle tone detector which periodical]v samples an electromyogram (EMG) signal, and selects components of the sampled signal having a predetermined frequency. The amplitude of this signal is monitored in order to provide an output signal indicative of the amplitude level of the sample.
A high-pass filter having a real-time frequency cut-off greater than approximately 22 Hz, may be used to select the desired components in the sampled signal. Four threshold circuits connected in parallel to the high-pass filter, with each subsequent threshold circuit set at a higher threshold voltage than the previous one, may be used to monitor the amplitude of the selected signal components in order to determine at which level it is found. The outputs of the threshold circuits may be read simultaneously and the detector may include a pulse generator for generating a series of one to five pulses as an indication of the range of the amplitude sampled.
Many other objects and aspects of the invention will be clear from the detailed description of the drawings.
Brief Description of the_Drawings In the drawings:
Figure 1 illustrates the EEG, EOG and EMG signals recorded for sleep analysis; and Figure 2 is a muscle tone detector in accordance with the present invention.
Detailed Description The muscle tone detector operates on the electromyogram (EMG) ~3~
signal obtalned from electrodes placed under the chin. The signal i5 usually preamplified and recorded in real-time on one channel of an REG
apparatus or a magnetic tape recorder. An e~ample oE an EM~ signal 11 i8 shown in figure 1 together with the signals on the other channels, i.e.
the electroencephalogram signal 12, the left electro-oculogram signal 13 and the right electro-oculogram signal 14.
The muscle tone detector, in accordance with the present Inven-tion monitors muscle activity. The measure of muscle activity ls based on a periodic sampling taken every two seconds and an output is provided wherein one of five levels are indicated. In order to detect muscle activity events for scoring in accordance with the Rechtschaffen and Kales standard, the EMG signal that is sampled must have ~ real-time frequency greater than 22 Hz. The amplitude of the sampled signal is classified by the detector at one of five levels, i.e. below 12 l1V, from 12 to 29 ~V, from 29 to 45 ~V, from 45 to 57 llV, and above 57 ~V.
In order to save analysis time, the EMG signals may be fed to the detector at a much greater speed than that at whieh they were recorded. The detector will be described in terms of parameters for a ~ignal fed to it at twenty times the original recording rate, and do, of course, depend on the play-back rate.
The above criteria are met by the detector 20 illustrated in figure 2 in which the EMG signal 11 is applied to a high-pass filter 21 set at approximately 440 Hz which is equivalent to a 22 Hz real-time frequency. The output from the filter is fed to four threshold detectors 22, 23, 24 and 25, each of these provide an output when its particular threshold voltage of 12 ~V, 29 ~V, 45 ~V and 57 ~V, respectively, is surpassed. The outputs from detectors 22, 23, 24 and 25 are fed through NOR-gates 26, 27, 28 and 29, respectively, which are controlled by a clock circult 30. The outputs from the NOR-gates are used to clock flip-flops 32, 33, 34 and 35, respectively, which function as latches under the control of a pulseO The clock circuit 30 generates a short (c.500 nsec) pulse once every 100 msec which is equivalent to a 2 second real time period. This pulse i8 used to load the contents of the flip-flops 32, 33, 34, 35, into the shift register 37. At the same time, the clock triggers a one-shot 31 which disables the gates 26, 27, 28, 29, ~-Z~33;~
while loading of the shl~t reglster 37 takes place, so that there are no transitions of the fllp-flops 32, 33, 34, 35, durlng thls period. The end of the pulse from one-shot 31 is used to generate a short pulse from one-shot 36 which clears the flip-flops 32, 33, 34, 35.
Control circuitry 38 to the shfft register 379 is such that the contents of the shiEt register 37, as loaded from the flip-flops 32, 33, 34, 35, are shifted out while the flip-flops are acquiring data for the next sampling period. It is the shifted output of the shift reglster 37 that gives the pulse train indicating the highest level of EMG reached during the preceding sampling period. Thus, whenever the EMG lnput sig-nal exceeds the predetermined level for any sampling period the output from the shift reglster produces a pulse train~of from one to five pulses indicating the highest level reached, as shown in r1gure 1. One pulse is generated if the EMG signal ls below 12 ~V, two pulses if it is between 12-29 ~V, three pulses if it is between 29-45 ~V, four pulses if it is between 45 and 57 ~V, and five pulses if it i5 above 57 ~
Many modifications in the above described embodiments of the invention can be carried out wlthout departing from the scope thereof and, therefore, the scope of the present invention is intended to be limited only by the appended claims.
Background of the Invention Thls invention is directed to auto~atic sleep analysers and, ln particular, to detectors for the specific events used in ~taging sleep.
A scoring system for staging the sleep patterns of adult humans has been standardized and is described ln the manual edited by A. Recht-schaffen and A. Kales entitled, A Manual of Standardi~ed Terminology, Techniques and Scoring Sys~em for Sleep Stages of ~uman Sub~ect~ .
In scorlng sleep, three basic signals recorded as electrical activity in the body, are required. These are the sctivity of the brain, the eyes and the muscles. The activity of the braln is represented by an electroencephalographic (EEG) signal obtained from electrodes placed on the head. The activity of the eyes i8 represented by electro-oculo-graphic (EOG) signals obtained from electrodes placed near each eye. The muscle tone activity is represented by an electromyographic (EMG) signal obtained from electrodes usually located under the chin.
The activity signals would normally be recorded on a paper printout, and divided into time segments or epochs9 e.g. of forty sec-onds. Specific ~vents are noted visually during each epoch ln order to classify that epoch as a certain state of sleep or non-sleep. The con-ventlonal seven states of sleep or non-sleep are known as wakefulness, stage 1 sleep9 stage 2 sleep, stage 3 sleep, stage 4 sleep, REM sleep and movement time. These are listed in Table 1 together with the criteria for each epoch state used in the classification. The events used to stage or classify these states are alpha rhythm, sleep spindles, delta activity, and movement ti~e which are observed in the EEG signal, rapid eye movements (REM) which is observed in the EOG signal, and muscle tone which ls observed in the EMG signal.
Criteria for Sleep Staging Event State W 1 2 3 4 RF.M M.T.
_ Alpha + 0 0 0 0 Spindles 0 0 + + + 0 +
Delta 0 0 <20% 20-50%50-100% 0 +
Mov't artlfact ~ 0 o 0 0 0 > 50%
REM + 0 0 0 0 + +
EMG ~ + ~ + 1 0 +
Table 1 lists the events and their levels which ar~ to be observed during an epoch in order to classlfy it into a partlcu]ar state.
However, in addition to this table, certain guidelines exist for staging sleep by which tlle state ln each epoch can only be deter~ined by observ-ing events that occur in previous or subsequent epochs.
Traditional sleep recording with a monitoring technologist is very time consuming and expenslve, involving overnight shift work and slow vlsual analysis of very long paper recordings. The need for a moni-toring technologist can be avoided and, in many cases, be replaced by using portable recordings placed on the subJect to record the required signals continuously in his normal home environment. The slow visual analysis of long paper recordings can be circumvented by the use of auto-matic analysis, at high speed playback, of tape recorded data from either portable or traditional in-laboratory recordings~ Automatic analysis can replace ~uch long recordings by summary statistics and charts, and also improve scoring consistency.
A number of centers have attempted various approaches to auto-matic sleep analysis as a particular extension of the problem of auto-matic E~G analysis. Sleep EEG events have most frequently been detected by spectral analysis, by pattern recognition, and by period analysis of zero-crossing data. As well, digital filters have recently been intro-duced and have potential appllcation in the field. Combinations of these methods have sometimes been used to detect individual sleep EEG events which combine zero-crossing analysis w~th an amplitude criterion, a period discri~inator to determine frequency band (delta, alpha, splndle, beta or muscle potential), plus a pattern criterion. The staging of sleep may be done using detectors based on the above approaches which are then combined in a "hard-wired" processing unit. Alternately, all data processing for sleep staging may be done by a large general purpose com-puter. The hard-wired sleep stagers have the advantage of lower cost, but the great disadvantage of being inflexible. Performing all analyses on digitalized raw data in a necessarily large general purpose computer, on the other hand, is very expensive.
An intermediate approach, in which the present invention is used, has a series of (sometimes modifiable) event detectors as part of a preprocessor unit. The detectors detect essentially onl~ those events ~2~ 3;~Z9 which are used for vLsual analysis. Their outputs can then be analysed for quantification of sleep variables and for sleep staging, either visu-ally, or automatically by a microprocessor or a small general purpose computer. Gaillard and Tissot have chosen a somewhat similar approach, as described in their publication, Principles of automatic analysis of sleep records with a hybrid system , Comp. Biomed. Res~, 1973, 6:1-13.
In this system the outputs of a preprocessor consisting of 12 bandpass filters for EEG analysis, an eye movement analyser, a muscle integrator, an EKG counter, and a galvanic skin response (GSR) counter are coupled to a small general purpose computer. Such an approach combines the advantages of relatively low cost and flexibility.
As described above, the events to be detected are alpha rhythm, sleep splndle, delta activity, and movement artifact in the EEG signal, plus REMs and ~uscle tone.
The alpha rhythm in automatic sleep analysers is generally detected using a classical bandpass filter or zero-crossing detector and a level discriminator. A particularly useful phase-locked loop alpha detector is described in the thesis entitled, A Hybrid Pre-Processor for Sleep Staging Using the EEG , by D. Green, 1977, Chapter 7, pp. 1 to 13.
This detector produces an output, when the EEG signal has a component with a frequency of 8-12 Hz at greater than 25 ~V peak-to-peak amplitude.
The sleep spindle is the sleep EEG event most comprehensively examined to date. The approaches to spindle detection include: zero-crossing methods, classical analogue bandpass filtering, bandpass filter-ing with harmonic analysis, a software Fast Fourier Transform (FFT)approach, a matched filter approach, and a phase-locked loop (PLL) approach. A highly accurate sleep spindle detector is described in the publication by R. Broughton et al, entitled 'A Phase-locked Loop Device for Automatic Detection of Sleep Spindles and Stage 2 , Electroencephalo-graphy and Clinical Neurophysiology, 1978, 44: 677-680. This detector produces an output when the EEG signal has a component with a frequency of 11.5 15 Hz at greater than 14 ~V peak-to-pesk and a minimum burst duration of 0.5 seconds.
Delts activity detection can be performed by using analogue bandpass filters with energy detectors, by zero-crossing analysls using $~33~9 amplitude and period criteria, or by a software approach. A particularly useful delta detector which detects components of the EEG signal having a frequency of 0.5-1.5 Hz at greater than o7 V peak-to-peak, is described in the above noted thesis, chapter 9, pp. 1 to 10.
0~ the three remaining event detectors required for s]eep anal-ysis, a Conjugate Eye Movement Detector For Sleep Analysis is described in co-pending Canadian patent app]ication 439,063 filed on October 14, 1983 by R. Broughton et al and a Movement Arti~act Detector For Sleep Analysis is described in co-pending Canadian patent application 439,061 filed on October 14, 1983 by R. Broug~lton et al.
_mmary of the Invention It is therefore an object of the present invention to provide a muscle tone detector for a sleep analyser, This and other objects are achieved in a muscle tone detector which periodical]v samples an electromyogram (EMG) signal, and selects components of the sampled signal having a predetermined frequency. The amplitude of this signal is monitored in order to provide an output signal indicative of the amplitude level of the sample.
A high-pass filter having a real-time frequency cut-off greater than approximately 22 Hz, may be used to select the desired components in the sampled signal. Four threshold circuits connected in parallel to the high-pass filter, with each subsequent threshold circuit set at a higher threshold voltage than the previous one, may be used to monitor the amplitude of the selected signal components in order to determine at which level it is found. The outputs of the threshold circuits may be read simultaneously and the detector may include a pulse generator for generating a series of one to five pulses as an indication of the range of the amplitude sampled.
Many other objects and aspects of the invention will be clear from the detailed description of the drawings.
Brief Description of the_Drawings In the drawings:
Figure 1 illustrates the EEG, EOG and EMG signals recorded for sleep analysis; and Figure 2 is a muscle tone detector in accordance with the present invention.
Detailed Description The muscle tone detector operates on the electromyogram (EMG) ~3~
signal obtalned from electrodes placed under the chin. The signal i5 usually preamplified and recorded in real-time on one channel of an REG
apparatus or a magnetic tape recorder. An e~ample oE an EM~ signal 11 i8 shown in figure 1 together with the signals on the other channels, i.e.
the electroencephalogram signal 12, the left electro-oculogram signal 13 and the right electro-oculogram signal 14.
The muscle tone detector, in accordance with the present Inven-tion monitors muscle activity. The measure of muscle activity ls based on a periodic sampling taken every two seconds and an output is provided wherein one of five levels are indicated. In order to detect muscle activity events for scoring in accordance with the Rechtschaffen and Kales standard, the EMG signal that is sampled must have ~ real-time frequency greater than 22 Hz. The amplitude of the sampled signal is classified by the detector at one of five levels, i.e. below 12 l1V, from 12 to 29 ~V, from 29 to 45 ~V, from 45 to 57 llV, and above 57 ~V.
In order to save analysis time, the EMG signals may be fed to the detector at a much greater speed than that at whieh they were recorded. The detector will be described in terms of parameters for a ~ignal fed to it at twenty times the original recording rate, and do, of course, depend on the play-back rate.
The above criteria are met by the detector 20 illustrated in figure 2 in which the EMG signal 11 is applied to a high-pass filter 21 set at approximately 440 Hz which is equivalent to a 22 Hz real-time frequency. The output from the filter is fed to four threshold detectors 22, 23, 24 and 25, each of these provide an output when its particular threshold voltage of 12 ~V, 29 ~V, 45 ~V and 57 ~V, respectively, is surpassed. The outputs from detectors 22, 23, 24 and 25 are fed through NOR-gates 26, 27, 28 and 29, respectively, which are controlled by a clock circult 30. The outputs from the NOR-gates are used to clock flip-flops 32, 33, 34 and 35, respectively, which function as latches under the control of a pulseO The clock circuit 30 generates a short (c.500 nsec) pulse once every 100 msec which is equivalent to a 2 second real time period. This pulse i8 used to load the contents of the flip-flops 32, 33, 34, 35, into the shift register 37. At the same time, the clock triggers a one-shot 31 which disables the gates 26, 27, 28, 29, ~-Z~33;~
while loading of the shl~t reglster 37 takes place, so that there are no transitions of the fllp-flops 32, 33, 34, 35, durlng thls period. The end of the pulse from one-shot 31 is used to generate a short pulse from one-shot 36 which clears the flip-flops 32, 33, 34, 35.
Control circuitry 38 to the shfft register 379 is such that the contents of the shiEt register 37, as loaded from the flip-flops 32, 33, 34, 35, are shifted out while the flip-flops are acquiring data for the next sampling period. It is the shifted output of the shift reglster 37 that gives the pulse train indicating the highest level of EMG reached during the preceding sampling period. Thus, whenever the EMG lnput sig-nal exceeds the predetermined level for any sampling period the output from the shift reglster produces a pulse train~of from one to five pulses indicating the highest level reached, as shown in r1gure 1. One pulse is generated if the EMG signal ls below 12 ~V, two pulses if it is between 12-29 ~V, three pulses if it is between 29-45 ~V, four pulses if it is between 45 and 57 ~V, and five pulses if it i5 above 57 ~
Many modifications in the above described embodiments of the invention can be carried out wlthout departing from the scope thereof and, therefore, the scope of the present invention is intended to be limited only by the appended claims.
Claims (8)
1. A muscle tone detector for a sleep analyser comprising:
- means for periodically sampling an electromyogram (EMG) signal;
- first means for selecting components of the sampled signal above a predetermined frequency;
- second means for monitoring the amplitude of the selected components; and - third means for providing an output signal indicative of the amplitude of the selected components of the sample.
- means for periodically sampling an electromyogram (EMG) signal;
- first means for selecting components of the sampled signal above a predetermined frequency;
- second means for monitoring the amplitude of the selected components; and - third means for providing an output signal indicative of the amplitude of the selected components of the sample.
2. A muscle tone detector as claimed in claim 1 wherein the first means selects components having a real-time frequency greater than approximately 22 Hz.
3. A muscle tone detector as claimed in claim 2 wherein the third means provides an output signal indicative of one of five distinct amplitude levels of the selected components.
4. A muscle tone detector as claimed in claim 3 wherein the third means includes pulse generating means for generating a series of one to five pulses as an indication of the amplitude level.
5. A muscle tone detector as claimed in claim 1 wherein the first means includes a high pass filter having a lower cut-off frequency in the order of 22 Hz.
6. A muscle tone detector as claimed in claim 5 wherein the second means includes four parallel threshold circuits connected in series with the high-pass filter, each subsequent threshold circuit being set at a higher threshold voltage.
7. A muscle tone detector as claimed in claim 6 wherein the third means includes means for simultaneously reading the outputs of the threshold circuits and for providing an output indicative of one of five distinct amplitude ranges.
CLAIMS (cont.):
CLAIMS (cont.):
8. A muscle tone detector as claimed in claim 7 wherein the third means further includes pulse generating means for generating a series of one to five pulses as an indication of the range of the amplitude sampled.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CA000439062A CA1213329A (en) | 1983-10-14 | 1983-10-14 | Muscle tone detector for sleep analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CA000439062A CA1213329A (en) | 1983-10-14 | 1983-10-14 | Muscle tone detector for sleep analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA1213329A true CA1213329A (en) | 1986-10-28 |
Family
ID=4126280
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA000439062A Expired CA1213329A (en) | 1983-10-14 | 1983-10-14 | Muscle tone detector for sleep analysis |
Country Status (1)
| Country | Link |
|---|---|
| CA (1) | CA1213329A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0468340A2 (en) * | 1990-07-24 | 1992-01-29 | BioControl Systems, Inc. | Eye directed controller |
| JP2011108281A (en) * | 2004-10-01 | 2011-06-02 | Sony Pictures Entertainment Inc | System and method for tracking facial muscle and eye motion for computer graphics animation |
| CN109567749A (en) * | 2018-11-09 | 2019-04-05 | 速眠创新科技（深圳）有限公司 | Dormant data processing method, device, computer equipment and storage medium |
-
1983
- 1983-10-14 CA CA000439062A patent/CA1213329A/en not_active Expired
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0468340A2 (en) * | 1990-07-24 | 1992-01-29 | BioControl Systems, Inc. | Eye directed controller |
| EP0468340A3 (en) * | 1990-07-24 | 1992-12-16 | Biocontrol Systems, Inc. | Eye directed controller |
| JP2011108281A (en) * | 2004-10-01 | 2011-06-02 | Sony Pictures Entertainment Inc | System and method for tracking facial muscle and eye motion for computer graphics animation |
| CN109567749A (en) * | 2018-11-09 | 2019-04-05 | 速眠创新科技（深圳）有限公司 | Dormant data processing method, device, computer equipment and storage medium |
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