Method and apparatus for analyzing uterine electrical activity from surface measurements for obstetrical diagnosis

A method and apparatus is presented for recording uterine electrical activity from the surface of the abdomen or vagina for the purpose of diagnosing contractile patterns of the uterus or abdominal muscles in pregnant and nonpregnant patients. The present invention provides data analysis techniques for analyzing electromyographic data measured from the surface of a patient to characterize uterine activity. The method and apparatus described include algorithms for the systematic analysis of electrical signals recorded from the abdominal surface. Such processing comprises integration of signals, frequency spectral analysis, 3-dimensional power density mesh plots, vector analysis, fast wavelet transform, and joint time-frequency characteristics. These techniques and apparatus are appropriate for use in a clinic or through communication lines for use as a remote or home uterine monitoring system. As such, uterine electrical activity may be measured at a remote location and processed at a central facility through on-line communications channels, such as a telephone line. The techniques and apparatus disclosed are also useful in predicting successful treatment for cases where either the uterus fails to develop forceful contractions at term or begins to contract pre-term.

BACKGROUND OF THE INVENTION 
This invention relates to a method and apparatus for recording and 
analyzing uterine or vaginal electrical activity. More specifically, the 
invention relates to a method and apparatus useful for determining the 
contractility of the uterus or abdominal muscles by recording spontaneous, 
mechanically or electrically stimulated, or drug-evoked electrical 
activity of the myometrium of the uterus from the abdominal, cervical or 
vaginal surface. The invention further relates to the analysis of surface 
electromyographic data corresponding to uterine or abdominal muscle 
electrical activity for obstetrical diagnosis. 
Presently there is no objective manner with which to evaluate the 
contractility of the uterus. This is true either in nonpregnant patients 
where hypercontractility is associated with dysmenorrhea or in pregnant 
patients where the uterus is sometimes active prior to term. Normally the 
uterus is quiescent in nonpregnant women and during most of pregnancy. 
However, at the end of pregnancy the myometrium undergoes a series of 
changes that lead to synchronous, rhythmic uterine contractions (labor). 
The diagnosis of labor is the most significant problem faced by 
obstetricians. In addition, preterm labor, which occurs in about 10% of 
pregnant patients, is difficult to diagnose. Frequently term or preterm 
labor require adjuvant therapy to either stimulate or inhibit 
contractility of the uterus. 
Since there is some minor spontaneous uterine activity at all times during 
pregnancy, it is often not possible to distinguish between this 
physiological activity and term or preterm labor. The state of the cervix 
is commonly used as a predictor of labor. However, the softening of the 
cervix may occur relatively late in labor. In addition, labor and changes 
in the cervix can occur independently. Alternatively, the frequency of 
contractions is used to diagnose labor, sometimes recorded with a 
tocodynamometer. However, these methods give only crude subjective 
estimates of uterine contractility. 
The uterus does not contract vigorously throughout most of pregnancy and 
this provides a tranquil environment for the growing fetus. At term the 
uterus normally begins to contract forcefully in a phasic manner (labor) 
to expel the fetus. Contractions of the uterus are directly proportional 
to the underlying electrical activity of the muscle. The frequency, 
duration and magnitude of a uterine contraction are directly proportional 
respectively to frequency of bursts of action potentials, the duration of 
a burst of action potentials, and the propagation (also referred to as 
conduction) of action potentials over the uterus and the recruitment of 
muscle cells. A similar situation exists in heart muscle although heart 
and uterine muscle are different with respect to structure and 
configuration of the action potentials. The action potentials are 
accompanied by the influx of calcium into the muscle cells to activate the 
contractile apparatus. Between bursts of action potentials, the uterus 
relaxes and recovers. The relaxation phase in uterus, although perhaps not 
as critical as in the heart where refilling must occur, is still very 
important in providing a respite for both the muscle and the fetus. 
Thus, by recording uterine electrical activity one can assess the 
contractility of the myometrium. Technology has been used to record 
cardiac electrical activity to determine the normal or abnormal function 
of the heart. Electrical events in muscle reflect the opening and closing 
of ion channels. In the case of the electrocardiograph (ECG), the ability 
to diagnose molecular defects, at the level of the ion channels 
themselves, has been clearly demonstrated. The ECG also allows you to 
study the conduction pathway in the heart, which produces a highly 
stereotyped spatial pattern of activity. The uterus may lack such a 
pathway; none has ever been identified. Little is presently known about 
the spatial organization of excitation in the uterus, although the 
importance of low-resistance pathways between adjacent cells is known to 
be crucial. The present invention provides an apparatus for mapping the 
electrical activity of the uterus. It is known that the uterus is more 
active during the night than during the day, and this pattern may grow 
even more pronounced as labor becomes imminent. The present invention also 
offers the opportunity to assess the predictive value of diurnal 
variations in uterine activity in relation to the onset of labor. 
During labor, the patient may actively contribute to the labor process by 
consciously contractingher abdominal muscles. Such muscle contractions 
produce striated muscles in the patient's abdominal wall, which can be 
detected from an examination of the underlying electrical activity of the 
muscle. The present invention is also directed toward distinguishing 
electrical activity indicative of abdominal muscle contraction, from that 
indicative of myometrial contractions. 
Many studies have previously recorded uterine myometrial electrical 
activity using electromyography (EMG) where electrodes are placed directly 
on the uterus. These studies show that the myometrium generates little 
electrical activity prior to labor but activity increases tremendously 
during labor reflecting the mechanical events. Studies of interest are 
demonstrated in publications by Csapo, Chapter 43, "Force of Labor," 
Principles and Practice of Obstetrics and Perinatology, Ed. by L. Iffy and 
H. A. Kaminetzky Publishing, John Wiley and Sons 761-799, 1981; Garfield 
et al., "Control of Myometrial Contractility: Role and Regulation of Gap 
Junctions," Oxford Rev. Reprod. Biol. 10:436-490; 1988; Wolfs and Van 
Leeuwen, "Electromyography observations on the human uterus during labor," 
Acta Obstet. Gynecol. Scand. Suppl.! 90:1-62, 1979; and more recently by 
Devedeux et al., "Uterine Electromyogiaphy: A Critical Review," Am J. 
Obstet. Gynecol, 169:1636-1653, 1993. One may measure and use uterine EMG 
activity by direct contact with the uterus to predict normal and abnormal 
uterine contractions. However, it is not practical to place electrodes 
directly on the uterus. To do this under the present level of 
understanding one must surgically implant electrodes on the uterine 
surface or introduce a catheter electrode through the vaginal canal and 
puncture the fetal membranes. 
It would be desirable to record uterine EMG activity from the abdominal or 
vaginal surface. However, previous studies of electrical activity of the 
uterus recorded with electrodes placed on the abdominal surface have 
failed to record bursts of action potentials from the uterus and generally 
show no association of uterine electrical activity with contractility. 
Studies of interest are included in the above-noted publications by Wolfs 
and Van Leeuwen, and by Devedeux et al. Wolfs and Van Leeuwen summarized 
all studies prior to 1979 and concluded that "it has never been clearly 
shown that the potential fluctuations obtained by means of electrodes 
attached to the abdominal wall, do indeed represent the electrical 
activity of the uterus." (Page 7.) Similarly, Devedeux et al state that 
abdominal monitoring of uterine electrical activity "requires further 
investigation." (Page 1649.) 
Part of the difficulty in interpretation of electrical activity recorded 
from the uterus lies in the fact many investigators, including Wolfs and 
Van Leeuwen and Devedeux et al. have failed to recognize that action 
potentials drive the uterus to contract. Action potentials are not 
responsible for contraction of some smooth muscle tissues such as airway 
muscle and some vascular muscles and therefore many researchers confound 
the uterus with other smooth muscle tissues. Thus, many of these studies 
have attempted to correlate electrical activity with mechanical 
contractions in order to show that electrical activity is responsible for 
contractions. However, no study has measured uterine and surface EMG 
simultaneously and correlated these to contractions. Furthermore, it is 
now clear (from publications by Marshall, "Regulation of Activity in 
Uterine Smooth Muscle," Physiol. Rev. 42:213-227, 1962; Csapo, Chapter 43, 
"Force of Labor," Principles and Practice of Obstetrics and Perinatology, 
Ed. by L. Iffy and H. A. Kaminetsky, John Wiley & Sons, 761-799, 1981; 
Garfield et al., "Control of Myometrial Contractility: Role and Regulation 
of Gap Junctions," Oxford Rev. Reprod. Biol, 10:436-490, 1988; Garfield, 
Chapter 3, "Role of cell-to-cell Coupling in Control of Myometrial 
Contractility and Labor," Control of Uterine Contractility, Ed. by R. E. 
Garfield and T. Tabb, CRC Press, 39-81, 1994), that action potentials 
activate the uterus to contract and that by measuring uterine electrical 
activity one can indirectly estimate contractility, although none of these 
studies provide a detailed explanation of how to analyze uterine 
electrical activity in order to indirectly estimate contractility and to 
distinguish contractility from other physical phenomena, such as abdominal 
muscle contraction. 
SUMMARY OF THE INVENTION 
The present invention presents a method and apparatus for recording and 
analyzing uterine electrical activity from the surface of the abdomen or 
vagina for the purpose of diagnosing contractile patterns of the uterus in 
pregnant and nonpregnant patients. The present invention provides data 
analysis techniques for analyzing electromyographic data measured from the 
surface of a patient to characterize uterine and abdominal muscle 
activity. 
A feature of the present invention is the measurement in vivo of the 
electrical and therefore the mechanical activity of uterine and abdominal 
muscle tissue, to produce a more quantitative, comprehensive and 
analytical framework of the tissue by transferring information from the 
tissue to a computer memory for automatic analysis and for display on a 
monitor for assessment by an attending physician or other party interested 
in monitoring the tissue. 
The present invention is applicable to a wide range of obstetrical, 
gynecological and other conditions. One such application is defining the 
state of the uterus during term and preterm labor. Another application is 
monitoring the nonpregnant uterus for indication of conditions such as 
infertility and uterine pathology. The method and apparatus are also 
valuable for use in connection with other tissues other than the uterus 
such as tests of bladder function during urination or similarly, 
evaluation of the bowels during defecation. 
The method and apparatus of the present invention includes algorithms for 
the systematic analysis of electrical signals recorded from the abdominal 
surface. Such processing comprises integration of signals, frequency 
analysis, 3-dimensional mesh plots, vector analysis, fast wavelet 
transform (continuous and discrete) and wavelet packet analysis, and joint 
time-frequency characteristics. These techniques and apparatus are 
appropriate for use in the a clinic or through communication lines for use 
as a remote or "home" uterine monitoring unit. As such, uterine electrical 
activity may be measured at a remote location and stored in compressed 
form. Data from these measurements may be recorded for later processing at 
a central or remote facility, or it could be processed on-line over 
communications lines, such as telephone lines or radio frequencies. 
Further, the present invention can be used to predict successful treatment 
for cases where either the uterus fails to develop forceful contractions 
at term or begins to contract pre-term. 
In accordance with an embodiment of the invention, recording electrodes 
capable of measuring action potentials are placed at various points on the 
abdominal surface of a pregnant patient. An amplifier is electrically 
coupled to receive an analog input from the electrodes, and to amplify it. 
An analog to digital converter (ADC) may also be electrically coupled to 
receive an amplified analog input from the amplifier indicative of 
bioelectrical potentials measured by the electrodes. 
Electromyographic signals are transmitted through the electrodes at a 
sampling frequency of between 0.5 Hz to 1 kHz for a duration of time 
sufficient to record at least one (1) burst of action potentials. These 
signals are amplified by an amplifier. Typically, the sampling frequency 
is about 100 Hz and a minimum of five (5) bursts are measured. The signals 
may be further processed using filters before or after storage to isolate 
signal components. A memory (or storage device) comprising sufficient 
storage capacity to store data resulting from a sampling of 
electromyographic signals at a sampling frequency of at least 100 Hz. for 
a duration of time sufficient to record at least five (5) bursts of action 
potentials is coupled to receive an input from the amplifier, indicative 
of electrical signals received by the amplifier. Said electromyographic 
signals are stored in the memory. The signals may then be filtered in 
order to identify signals in one or more frequency ranges of interest. The 
values of these ranges are dependent upon the species of patient under 
evaluation. For instance, in human beings, the primary frequency range of 
interest for the identification of uterine contractility from surface 
abdominal signals is .01 Hz-10 Hz. The computer contains software to 
facilitate this analysis of the signals. 
The above described electrodes may alternatively be placed on the vaginal 
wall or pericervical surfaces. Probe electrodes or needle electrodes are 
particularly suited for this application. Probe electrodes are available 
from Millar Instruments of Houston, Tex. This may be particularly useful 
for monitoring electrical activity in early pregnancy and in nonpregnant 
women where the uterus is small and not likely to produce strong EMG 
signals that propagate to the abdominal surface, but are transferred down 
the reproductive tract to the vagina. 
In particular, the present invention contemplates a method of analyzing 
surface electromyograplic data to characterize uterine or abdominal muscle 
activity, comprising applying action potential measuring electrodes to an 
abdominal surface of a patient; measuring electromyographic signals 
produced by the electrodes; analyzing frequency components of the 
electromyographic signals; and characterizing uterine or abdominal muscle 
activity of the patient based on the analysis of frequency components. 
Ideally, the analysis of uterine activity indicating parameters is 
performed for data from at least five (5) bursts of action potentials 
within the stored electromyographic signals. The burst analysis may 
include determining the frequency, duration, amplitude, number of action 
potentials per burst, activity per unit time of interest, and power 
density spectrum of at least five (5) bursts of action potentials and the 
frequency, duration, and amplitude of a plurality of action potentials in 
one or more of these bursts. As a further embodiment, the method also 
includes predicting treatment for the patient based on the 
characterization of uterine activity, in particular this treatment may be 
pharmacologically inducing or inhibiting labor in the patient. 
The burst of action potentials may be analyzed using wavelet or Cepstrum 
Analysis, as described in Akay, Chapter 6, "Cepstrum Analysis," Biomedical 
Signal Processing, Academic Press (1994). The electromyographic signals 
may also be analyzed using non-linear dynamics, or chaotic analysis, as 
described in Molnar, et al., "Correlation Dimension of Changes 
Accompanying the Occurrence of the Mismatch Negativity and the P3 
Event--Related Potential Component," Electroencephalography and Clinical 
Neurophysiology, 95 (1995), pp. 118-26; Elbert, et al., "Chaos and 
Physiology: Deterministic Chaos in Excitable Cell Assemblies," Physiology 
Reviews, Vol. 74, No. 1, Jan. 19, 1994; and Skinner, et al. "The Point 
Correlation Dimension Performance with Non-Stationary Surrogate Data and 
Noise," Integrative Physiological and Behavior Science, Vol. 28, No. 3, 
pp. 217-34 (Sept. 1994). The electromyographic signals may also be 
analyzed using a myometrial index, comprising power density spectrum and 
frequency data. The distribution of intervals between successive action 
potentials may be characterized as an indicator of aberrant activity. 
The invention also contemplates the stimulation of the vagina of the 
patient while the electromyographic signals are being stored. This 
stimulation permits the assessment from the stored electromyographic 
signals for the phenomenon of conduction, and permits the diagnosis of 
labor as a function of the signals. The stimulation of the vagina may 
either be electrical, mechanical or pharmacological, for example through 
the infusion of oxytocin to the patient. 
Other further embodiments contemplate isolating high frequency components 
(F2) within the electromyographic signals; isolating a fast wave component 
(FW) within the high frequency components (F2); determining a 
low-frequency (FW.sub.L) domain, including low-frequency components within 
the fast wave component (FW), and a high-frequency (FW.sub.H) domain, 
including high-frequency components within the fast wave component (FW); 
and determining a relationship between the low-frequency (FW.sub.L) domain 
and the high-frequency (FW.sub.H) domain indicative of an obstetrical 
diagnosis. This relationship can be indicative of pre-term or term uterine 
activity. 
Other embodiments of the present invention contemplate analyzing the 
frequency ranges of interest using wavelet analysis methods to decorrelate 
the signals, displaying the signal components by subband, and comparing 
the energy levels contained in particular subbands versus time of 
pregnancy. The wavelet transform or wavelet packet analysis may be used to 
generate various measures (such as amplitudes and ratios) of the wavelet 
maxima, skeleton, or energy content within particular subbands. The 
resulting decomposition(s) of the signal may be used in denoising by 
thresholding, wavelet shrinkage, and comparable approaches. The signal may 
be compressed with high efficiency before storage by discarding the 
smallest wavelet coefficients. 
An alternative embodiment of the present invention, contemplates a method 
of analyzing surface electromyographic data to characterize uterine 
activity, comprising applying multiple pairs of action potential measuring 
electrodes to a surface of a patient; measuring electromyographic signals 
produced by the electrodes; analyzing the electromyographic signals; 
determining potential vector characteristics of the electromyographic 
signals to identify direction and rate of propagation of uterine 
electrical activity; and characterizing uterine activity of the patient 
based on the potential vector characteristics. This potential vector can 
be indicative of a obstetrical diagnosis, including pre-term or abnormal 
term uterine activity. 
The apparatus of the present invention includes at least one electrode 
(unipolar, bipolar, etc) that is applicable to the abdominal, cervical or 
vaginal surface of the patient under analysis, an amplifier electrically 
coupled to the electrode to receive and amplify a signal indicative of 
action potentials measured by the electrode; an analog-to-digital 
converter, that is electrically coupled to receive an analog input from 
the amplifier indicative of action potentials measured by the electrode, 
and that converts electromyographic signals produced by the electrodes 
into digitized data which are indicative of electromyographic signals, a 
memory for storing the digitized signals, and comprising sufficient 
storage capacity to store data resulting from a sampling of 
electromyographic signals at a sampling frequency of at least 100 Hz. for 
a duration of time sufficient to record at least five (5) bursts of action 
potentials, a filtering device capable of segregating and identifying 
electromyographic signals, including action potentials, in preselected 
frequency ranges, and a programmed computer for analyzing the stored 
digitized signals and for providing a characterization of uterine 
activity. The computer comprises an expert system programmed to analyze 
the frequency, duration, amplitude, and power density spectrum o connected 
to the f action potential bursts and individual action potentials. The 
expert system is further capable of characterizing uterine activity and of 
identifying abdominal muscle contractions, based upon this analysis. 
The expert system may comprise algorithms needed to perform a Cepstrum 
analysis, wavelet analysis, chaotic analysis, or myometrial analysis of 
the action potentials. The expert system may also be capable of 
identifying abdominal muscle contraction. The expert system may also be 
capable of evaluating the trend of uterine activity over the course of 
labor, encompassing many hours, to determine whether labor is progressing, 
and alerting the physician to the possible diagnosis of 
failure-to-progress and the need to treat by pharmacological, surgical, or 
electrical means. The expert system may be capable of assessing other 
clinical data in combination with the EMG data. 
An alternative embodiment contemplated by the present invention is an 
apparatus for recording and analyzing uterine electrical activity from the 
abdominal surface, comprising at least one action potential measuring 
electrode applicable to an abdominal surface of a patient under analysis; 
an analog-to-digital converter, connected to the at least one electrode, 
for converting electromyographic signals produced by the electrode into 
digitized data indicative of the electromyographic signals; a memory for 
storing the digitized signals; and a programmed computer for analyzing 
frequency components of the stored digitized electromyographic signals, 
and for providing an indication of uterine electrical activity of the 
patient under analysis as a function of the stored digitized signals. A 
still further embodiment contemplates an apparatus wherein the programmed 
computer is used further for determining power density spectral 
characteristics of the frequency components of the electromyographic 
signals. 
The present invention further contemplates an apparatus in the form of a 
remote uterine monitoring system for analyzing surface electromyographic 
data to characterize uterine activity, comprising a remote uterine monitor 
and a central programmed computer in communication with the remote uterine 
monitor for analyzing stored digitized electromyographic signals, and for 
providing an indication of uterine electrical activity of the patient 
under analysis as a function of the stored digitized signals. The remote 
uterine monitor includes at least one action potential measuring electrode 
applicable to an abdominal surface of a patient under analysis; and a 
remote analog-to-digital converter, connected to the at least one 
electrode, for converting electromyographic signals produced by the 
electrode into digitized data indicative of the electromyographic signals. 
In a further embodiment, the remote uterine monitor and the central 
programmed computer communicate on-line through a telephone line. In a 
still further embodiment, the remote uterine monitoring system also 
includes a remote storage device for recording the digitized 
electromyographic signal data, and wherein the central programmed computer 
communicates with the remote uterine monitor off-line through the remote 
storage device. 
These and other features and advantages of the present invention will 
become apparent to those of ordinary skill in this technology with 
reference to the following detailed description and appended drawings.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
Referring now to FIG. 1A, there is shown schematically a pregnant patient 
11 with a fetus 12 retained within the uterus 13. The uterine wall 14 is 
primarily configured of muscle tissue and is disposed proximate to the 
abdominal wall 16 of the patient 11. In accordance with the principles of 
the present invention, electrodes 17 are placed on the exterior of the 
patient 11 on the abdominal wall 16. In another embodiment, electrode 16 
may be placed on the vaginal surface of the patient. The electrodes 17 
have leads 18 that are connected to a recording apparatus 19 including an 
amplifier 20, analog-to-digital converter (ADC) 21, computer 22 and 
monitor 23. In a preferred embodiment, amplifier 20 is used to amplify the 
signal received from electrode 17. The ADC, computer and monitor may be 
replaced or augmented by other output indicators, such as chart recorders 
or indicator lamps or audio monitors. 
In accordance with the principles of the present invention, the uterus 13 
of the pregnant patient 11 is monitored for electrical activity from 
signals detected on the surface of the abdomen. In a preferred embodiment, 
the signals (EMG) are amplified by amplifier 20, digitized by ADC 21, and 
displayed on a monitor 23. The signals are also stored in the memory 24 of 
computer 22 for analysis of the frequency duration and other 
characteristics of the action potentials. As shown in FIG. 1B, memory 24 
comprises filtering device 26 capable of segregating and identifying 
electromyographic signals including action potentials in predetermined 
frequency ranges, a multiplicity of bins 25 for storing 
elecctromyographics signals in discrete predetermined frequency ranges, 
and expert system 27 programmed to analyze the frequency, duration, 
amplitude and power density spectrum of action potential bursts and 
individual action potentials and further capable of characterizing uterine 
activity and identifying muscle contractions, based upon such analysis. In 
an alternative embodiment, functions performed by filtering device 26 are 
carried out by software. 
In a preferred embodiment, expert system 27 is also capable of determining 
the mean frequency, starting frequency, and ending frequency of a 
plurality of action potentials. In another preferred embodiment, the 
expert system 27 is also capable of identifying abdominal muscle 
contractions. In another preferred embodiment, the expert system 27 is 
also capable of evaluating long-term trends in uterine activity as 
indicating the progression of labor. In another embodiment, the expert 
system may compare records from the same patient taken at different times 
during her pregnancy and predict the onset of labor at term. In another 
preferred embodiment, the expert system 27 is capable of using clinical 
information in combination with electromyographic data to suggest possible 
diagnoses. 
In accordance with one embodiment of the present invention, ADC 21 may be, 
for example, a Data-Pac II A/D board, available from Run Technologies, or 
a MacLab A/D board, available from MacLab Division of AD Instruments. 
Amplifier 20 may be, for example, a Grass polygraph recorder, Mode #7D 
with DC amplifiers, available from Grass Instruments, or a Gould amplifier 
and recorder Model TA240, available from Gould Instruments, or a MacLab 
amplifier for Macintosh computers, available from the MacLab Division of 
AD instruments. Computer 22 with monitor 23 may be, for example, any IBM 
PC compatible computer, preferably with a 486-type (or better) 
microprocessor, twelve (12) megabytes of RAM, and a 500 megabyte hard 
drive and a VGA (or better) display, or a Macintosh IIci computer with 
display, or a Macintosh Powerbook lap-top computer, or an IBM lap-top 
computer, or any other equivalent computer and monitor. Computer 22 may 
also include several types of long-term storage devices, including 
recordable CD-ROM, tape, or high-capacity disks or removable cartridges. 
Electrodes 17 may be, for example, stainless steel clips or cups, for 
example, various models available from Hewlett-Packard, silver or platinum 
clips or cups, or they may be a Bard catheter with electrodes for vaginal 
recording, available from Bard Reproductive Sciences. 
Although specific examples have been given for the various hardware 
components shown in FIG. 1A-1B, it will be understood that different 
hardware components may be used, without departing from the spirit and 
scope of the present invention. For example, some embodiments of the 
present invention comprising the more complex data analysis schemes may 
require a Pentium.RTM. or comparable microprocessor and at least 64 Mbytes 
of RAM. 
Referring also to FIG. 2, an enlarged side view of the electrodes 17 is 
shown, that are used in contact with a patient's abdominal wall. The 
electrodes 17 are bipolar (or tripolar) comprised of silver or platinum, 
and are spaced about 1 cm apart. Leads 18 from the electrodes 17 are 
connected to amplifier 20. 
The amplifier 20 includes controls for amplifying or attenuating the 
signals and also filters for elimination of some of the high or low 
frequency noise. The amplifier is, for example, a battery powered ac/dc 
differential amplifier with the following approximate specifications: 
______________________________________ 
Gain, AC and DC .times.100, .times.1,000 & .times.10,000 
Input resistance 10.sup.12 ohms typical 
Leakage current 50 pA typical 
Common Mode Rejection 
100,000:1 min @ 60 Hz 
Noise, input shorted 
10 .mu.V p--p, 1 Hz-10 kHz 
Low Freq filter settings 
0.01, 0.1, 1.0, 10, 300 Hz 
High Freq filter settings 
0.1, 1.0, 3.0, 10 kHz 
Output resistance 220 ohms 
______________________________________ 
In another embodiment, amplifier 20 may carry out several stages of signal 
processing and analysis, including action potential detection and power 
spectral analysis, by analog hardware implementations of algorithms. 
The computer 22 and monitor 23 may be of conventional PC design with 
software and hardware to digitize the signals. The computer 22 is 
programmed with software to enable computer 22 to acquire, store, display 
and analyze the signals. This software may comprise an integrated 
general-purpose or customized software suite such as DataPac or MacLab or 
LabView (National Instruments). Additional software with extended 
signal-processing or statistical analysis capabilities may also be 
utilized, such as MatLab (The Math Works, Inc.) or S-Plus with S+ Wavelets 
(MathSoft). The operation of computer 22, in accordance with the present 
invention, is discussed below in detail with reference to the flow charts 
of FIGS. 4A-4E. 
Referring also to FIG. 3, shown are typical bursts 31 that are comprised of 
multiple action potentials 32 recorded from the surface of a patient's 
abdomen 16 from electrical activity of the uterus 13 and that correspond 
to the overlying contractility of the uterus 13 (see also, FIG. 1). The 
initial identification of action potential and burst events may be 
partially or fully automated, where action potentials are identified by 
techniques such as peak detection or wavelet analysis implemented in 
either software or hardware, and the putative identification indicated on 
a monitor display where it may be optionally reviewed by an operator for 
acceptability prior to further analysis. Thresholds and other selection 
criteria may be adjusted manually. A need for further preprocessing or 
optimization of the recording configuration may also be identified at this 
stage either by the operator or by automatic quality-control routines. 
Various parameters are measured from the bursts and from the action 
potentials, and are used for diagnostic purposes in accordance with the 
present invention. These parameters include: Frequency of 
bursts(1/T.sub.B), number of bursts per unit time (N.sub.B), duration of 
bursts (D.sub.B), quiescent periods between bursts (Q.sub.P), number of 
action potentials in each burst (N.sub.P), and characteristics of the 
action potentials including, frequency of action potentials (1/T.sub.P), 
duration of action potentials (D.sub.P), magnitude of action potentials 
(M.sub.P), rate of rise of action potentials (R.sub.P, i.e. slope of the 
depolarization of action potentials, dv/dt). The parameters measured 
during the burst may compared with their counterparts during the interval 
between bursts as a further indicator of contractility and relaxation. 
The analysis of the present invention is both real-time and predictive. In 
the real-time analysis, the action potential indicating parameters are 
analyzed in order to assess the present or real-time status of the 
patient's condition. In the predictive analysis, a series of action 
potential indicating parameters are analyzed as a function of time in 
order to predict uterine contractility, based upon one or more identified 
trends of examined uterine activity indicating parameters. 
Referring now to FIGS. 4A-4G, presented are flow charts depicting the 
operation of the apparatus of FIG. 1, in accordance with the present 
invention. In practice, the flow charts of FIGS. 4A-4G are embodied in a 
computer program or expert system used to control the operation of 
computer 22 of FIG. 1. Beginning in step 41, computer 22 acquires EMG 
signals produced by electrodes 17, which have been amplified by amplifier 
20 and digitized by ADC 21. In step 42, digitized versions of the EMG 
signals are stored in the memory of computer 22. 
Control then passes to step 43 where the stored EMG data is analyzed to 
assess parameters reflecting groups or bursts of action potentials present 
in the stored EMG signal. These analysis steps are shown in more detail 
with reference to FIG. 4B. Control then passes to block 44 wherein the 
stored EMG signal is analyzed to determine parameters characterizing the 
individual action potentials within the stored EMG signal. The details of 
the action potential analysis is shown in FIG. 4C. 
Control then passes to step 46 where probability analysis is conducted on 
the EMG signal characteristics determined in steps 43 and 44. The details 
of this probability analysis are shown with reference to FIG. 4D. 
Control then passes to decision block 47 where, based upon the probability 
analysis performed in step 46, it is determined whether the stored EMG 
signal reflects normal or abnormal uterine progression. The details of 
this diagnostic decision are shown below with reference to FIG. 4E. If 
normal progression is concluded by decision block 47, control passes to 
block 48 wherein the normal progression is characterized as either 
non-labor, pre-labor or labor based upon characteristics of the bursts and 
action potentials. If abnormality is concluded by decision block 47, 
control passes to block 49 where the abnormality is characterized as 
preterm labor, dystocia or other abnormalities based upon characteristics 
of abnormal bursts and action potentials. 
Referring now to FIGS. 3 and 4B, the details of the analysis of burst 
activity conducted within block 43 of FIG. 4A are presented. Beginning in 
step 51, the frequency of each burst (1/T.sub.B) is determined by 
estimating the number of bursts per unit time, control then passes to 
block 52 where the duration of each burst (D.sub.B) is determined by 
measuring the time from the first action potential of the burst until the 
final action potential of the burst. Then, in block 53, the quiescent 
periods (Q.sub.P) between the bursts are determined from measurements of 
the last action potential in a burst to the first action potentials in 
another burst. Then, in block 54, the number of action potentials in each 
burst (N.sub.P) are determined Control is then returned to the flow chart 
of FIG. 4A. The analysis of burst activity is conducted on 
electromyographic signals stored from at least five (5) bursts of action 
potentials. 
FIG. 4C presents the details of the analysis of action potential performed 
by block 44 of FIG. 4A. Beginning in block 61, the frequency of the action 
potential (1/T.sub.P) is determined by estimating the number of action 
potentials per unit time within each burst. Then, in block 62, the 
duration of the action potentials (D.sub.P) is determined by measuring the 
time from depolarization to repolarization. Control then passes to block 
63 where the magnitude of the action potentials (M.sub.P) is determined 
from measurements of the peak voltage of the depolarization. Control then 
passes to block 64 where the rate of rise of the action potentials 
(R.sub.P) is determined by determination of the slope dv/dt of 
depolarization. Conduction is estimated in a known manner from the rate of 
rise of action potentials (R.sub.P). In general, the greater the rate of 
rise, R.sub.P, the higher the conduction. Conduction may also be estimated 
from analysis of data when more than one surface electrode is used and 
time between bursts from separate electrodes is estimated or after vaginal 
stimulation (see below). In another preferred embodiment, the rate of all 
of the action potentials (F.sub.P) is also determined. The rate of fall is 
also useful in estimating conduction. Control then returns to the flow 
chart of FIG. 4A. 
FIG. 4D shows details of the probability analysis performed by block 46 of 
FIG. 4A. Beginning in block 71, the mean of each of the measured 
parameters is determined (see also, FIGS. 4B and 4C), and the standard 
deviation of each of the parameters is calculated. In another embodiment, 
other properties of the distributions of these parameters are also 
considered. 
FIG. 4E presents the details of the diagnosis step (block 47). Data from 
burst and action potential probability analysis (block 46) pass to blocks 
81 and 82 respectively and recorded burst and action potentials are 
compared to known normal ranges of values. Estimates for normal values for 
the measured parameters for action potentials and bursts of action 
potentials for human labor patients are presented in the following tables. 
Expected values of action potential parameters and burst parameters vary 
as a function of the classification of the patient. 
FIG. 4F presents the details of the burst analysis depicted in block 81 of 
FIG. 4E. The examination of burst characteristics, as depicted in block 83 
of FIG. 4F, comprise an examination of one or more of the following 
characteristics: frequency, duration, number of action potentials per 
burst, activity per unit time, power density spectrum (PDS), chaotic 
analysis, myometrial index, and wavelet or Cepstrum analysis. After the 
burst characteristics are examined, the patient's conditionmay be 
diagnosed. This diagnosis may result in a determination that the patient 
is in non-labor, pre-term labor, or labor, as shown in blocks 84-86 of 
FIG. 4F. 
The action potential analysis of block 82 in FIG. 4E is depicted in FIG. 
4G. The examination of action potential characteristics, referred to in 
block 93, comprises an examination of one or more of the following 
characteristics: frequency, duration, amplitude, rate of rise, rate of 
fall, and an analysis of changes in any of the above characteristics. 
After the action potential characteristics are examined, the patient's 
condition may be diagnosed. This diagnosis may result in a determination 
that the patient is in non-labor, pre-term labor, or labor as shown in 
blocks 94-96 of FIG. 4G. 
______________________________________ 
ACTION POTENTIALS 
Frequency: 0.5-1.2/second 
Duration: 50-200 milliseconds 
Amplitude: 0.2-1.2 millivolts 
BURSTS 
Frequency: 0.3-0.4/minute 
Duration: 10-30 seconds 
Action Potentials/Burst: 
10-30 
Power Density: 10-50 microvolts/second 
______________________________________ 
For non-labor patients, values for the measured parameters for action 
potentials and bursts of action potentials are considerably lower than the 
values presented in the above tables, with the exception of burst duration 
which may actually be larger. For values either higher or lower than 
normal for burst or action potential data, the computer recognizes these 
as abnormal and passes control to block 49. If burst or action potential 
parameters are within normal limits, the information passes to block 48. 
The calculated standard deviations for the measured parameters are used to 
determine whether the calculated parameter means for statistically 
different or the same as normal values. 
While utilization of the apparatus and method has been described above as 
particularly useful for monitoring the uterine wall during pregnancy, the 
instrument can also be used to measure electrical activity from the vagina 
that propagates or conducts from the uterus. This is particularly useful 
in early pregnancy or in nonpregnant patients where the uterus is small 
and not in contact with the abdominal wall. In addition, it is within the 
scope of this invention to utilize the apparatus and method thereof for 
medical and biological procedures other than uterine wall monitoring, such 
as, for example bladder or bowel function. 
From the foregoing description, one skilled in the art may easily ascertain 
the essential characteristics of this invention, and without departing 
from the spirit and scope thereof, may make various changes and 
modifications of the invention to adapt it to various usages and 
conditions. 
FIGS. 5-14 illustrate use of the present for monitoring uterine electrical 
activity. To produce the graphs of FIGS. 5-14, bipolar electrodes were 
placed on the abdominal surface of pregnant rats to monitor EMG activity 
in accordance with the present invention. In addition, in order to 
demonstrate the efficacy of the present invention relative to prior, more 
invasive, procedures, stainless steel electrodes were implanted directly 
on the uterus and/or vagina wall surface, and, a pressure transducer 
(specifically, a Model SPR-524 transducer available from Millar 
Instruments of Houston, Tex.) was placed in the uterus. The apparatus for 
recording was identical to that described above. The above described 
invention is designed for use mainly in humans or domestic animals whereas 
the following FIGS. 5-14 represent data obtained from rats. The 
instrumentation is essentially the same for both species. 
FIGS. 5-14 illustrate the correlation between the EMG signals recorded by 
the abdominal surface electrodes of the present invention, and signals 
recorded from uterus electrodes surgically implanted in the uterus. 
FIGS. 5, 6 and 7 show EMG activity (electrical activity of the uterus) 
(Channels 1 and 2) and pressure (Channel 3) recorded simultaneously 
directly from the uterine wall (Channel 1) and from the abdominal surface 
(Channel 2) of pregnant rats. Pressure (Channel 3) was measured from an 
intrauterine pressure device. Note that on days 18 and 21 of gestation 
(FIGS. 5 and 6) bursts of electrical activity are small and do not always 
correspond on the surface and uterus (FIG. 5, Channels 1 and 2), but do 
coincide with small uterine contractions (FIGS. 5 to 7, Channel 3). On the 
other hand, at term during delivery (FIG. 7) the EMG bursts signals from 
both the uterus and abdominal surface are of high amplitude and correlate 
with large pressure changes. Additional information may be derived by 
simultaneous recording of EMG and of contraction or of EMG and of Doppler 
or ultrasonic images. Contraction may be recorded by a device, such as an 
intrauterine catheter. 
These studies indicate that uterine EMG activity is low prior to term and 
that it increases dramatically during labor and delivery. Furthermore, 
these data show that uterine electrical activity may be recorded from the 
abdominal surface (Channel 2) to give an adequate representation of either 
the uterine electrical or mechanical activity. 
FIG. 8 shows EMG and pressure recordings from an animal during labor at 
term before and after treatment with oxytocin. Note that the bursts 
(Channels 1 and 2) coincide to low pressure changes (Channel 3) prior to 
oxytocin. Following IV infusion of oxytocin the EMG activity as recorded 
on the uterus (Channel 1), and abdominal surface (Channel 2) increase 
substantially and correspond to the large pressure changes in the uterus. 
These results indicate that electrical activity recorded from the surface 
of the abdomen (Channel 2) accurately mirrors changes in uterine EMG 
activity (Channel 1) and uterine pressure (Channel 3). 
FIG. 9 shows an expanded portion of an EMG burst recorded from the uterus 
(Channel 1) and abdominal surface (Channel 2). Note that the individual 
action potentials within the bursts correspond between those recorded from 
the uterus and surface. 
FIG. 10 illustrates EMG activity recorded from the uterus (Channel 1), 
abdominal surface (Channel 2) and activity of the heart (recorded with 
external electrodes placed on the chest) (Channel 3). Note that cardiac 
action potentials occur regularly with a frequency which matched the heart 
rate (300 to 400 beats per minute). In contrast bursts of action 
potentials from the uterus recorded with both uterine and abdominal 
surface electrodes occur periodically. Note that a minor signal from the 
cardiac potentials appears in the EMG signals from the uterus and uterine 
signals overlap with some signals from the heart. This information shows 
that one can record action potential bursts from the uterus with surface 
electrodes on the abdomen with little interference from the heart. 
FIG. 11 demonstrates EMG recordings from the uterus and abdominal surface 
in conscious rats (FIGS. 5 to 10 and FIG. 12 show data from anesthetized 
animals). Shown are corresponding bursts of EMG activity demonstrating 
that it is possible to record uterine electrical signals from the 
abdominal surface from conscious animals. 
FIG. 12 shows EMG recordings from the uterus (Channel 1) and vagina surface 
(Channel 2) and intrauterine pressure (Channel 3). Note the correspondence 
between uterine and vaginal EMG activity with accompanying changes in 
intrauterine pressure. These studies indicate that it is possible to 
record uterine EMG activity from the vaginal wall. 
FIGS. 13 and 14 depict a portion of EMG signals recorded from the uterus 
(Channel 1) and from the vaginal surface (Channel 2), indicating that when 
the vagina is stimulated mechanically during labor, signals are propagated 
(conducted) to the uterus (FIG. 13), whereas when the vagina is 
mechanically stimulated prior to term, signals are not conducted to the 
uterus (FIG. 14). This assessment of conduction may be used to indicate or 
diagnose a state of preparation for labor. 
The present method and apparatus may also be used to measure normal and 
abnormal function of other smooth muscle tissue, such as that of the 
bladder and lower gastrointestinal tract. Both organs depend upon smooth 
muscle contractility to perform their respective functions. Thus, 
electrical activity of the bladder and bowel may be registered from the 
abdominal surface during respective urination or defecation, in order to 
estimate appropriate and abnormal electrical activity of these organs. 
The analysis of EMG activity by abdominal surface recording to determine 
uterine contractility is further discussed in Buhimschi and Garfield, 
Uterine Contractility as Assessed by Abdominal Surface Recording of EMG 
Activity, published in AM. J. OB/GYN, 1996; 174:744-53 (February 1996). 
The present invention may also be used as a remote or "home" uterine 
monitoring system. FIG. 26 shows a diagram for an embodiment of a remote 
monitoring system. Uterine electrical activity may be measured at a remote 
location, such as in the privacy of one's home, utilizing remote monitor 
2602, comprising at least one electrode 17 capable of measuring action 
potentials emitted from the abdominal or vaginal surface of a patient 
under analysis and further capable of emitting an analog signal indicative 
of action potentials measured by said electrode. The remote monitor 
further comprises an amplifier 20 electrically coupled to the electrode to 
receive and amplify a signal indicative of action potentials measured by 
the electrode. The remote monitor also comprises a data transmission 
system 2601 capable of receiving an amplified signal from said amplifier 
and transmitting the signal to a remote location from the electrode, as 
shown in FIG. 26. 
In a preferred embodiment, the data transmission system comprises a 
telecommunication system. In another preferred embodiment, the data 
transmission system comprises a wireless communication system utilizing 
electromagnetic energy such as radio waves or microwaves. Such a system 
may employ analog or digital signals. 
The data from these measurements may be sent to a remote data storage and 
processing device 2604 so that the data may be recorded and/or processed 
for later use. Alternatively, data from measurements of uterine electrical 
activity may be processed on-line in real-time by a central data processor 
or a remote data processor. 
In a preferred embodiment, the remote data storage and processing device 
2604 comprises a remote analog to digital converter 2605 coupled to the 
data transmission system to receive an analog input from the amplifier 
indicative of action potentials measured by an electrode, and a memory 
2606 comprising sufficient storage capacity to store data resulting from a 
sampling of electromyographic signals at a sampling frequency of at least 
100 Hz for a duration of time sufficient to record at least five bursts of 
action potentials. The memory is coupled to receive a digital signal from 
the analog to the digital converter indicative of action potentials 
received by the analog to digital converter. The storage and processing 
device further comprises a filtering device capable 2607 of segregating 
and identifying electromyographic signals, including action potentials, in 
a frequency range of .03 to 50 Hz from electromyographic signals outside 
that range. The remote data storage and processing device further 
comprises a computer 2608 comprising an expert system programmed to 
analyze the frequency, duration, amplitude and power density spectrum of 
action potential bursts and individual action potentials. This expert 
system is also capable of characterizing uterine activity based upon this 
analysis. 
The present invention may also be used to predict treatment for a pregnant 
woman. The data signals collected can be analyzed by the present system 
and compared to existing norms to indicate the appropriate pharmacological 
treatment depending upon the uterine activity. For example, when signal 
levels are low and indicative of non-labor, a term patient can be treated 
in such a fashion pharmacologically to induce labor (i.e., oxytocin, 
prostaglandins, etc.). When signal levels are high in a patient prior to 
term (i.e., pre-term labor) treatment can consist of use of uterine 
inhibitors to suppress labor (e.g., tocolytic agents, .beta.-agonists, 
calcium channel blockers, etc.). As one of skill would understand, other 
obstetric diagnosis treatments could be predicted using the present 
invention. 
From the techniques described above, digital analysis techniques have now 
been developed and further improved to analyze further the surface EMG 
activity for obstetrical diagnosis and characterization of uterine 
activity. FIG. 15 shows a flow-chart for an embodiment of a method 
according to the present invention. Method 1500 for analyzing surface 
electromyographic data to characterize uterine activity includes process 
steps 1502, 1504, 1506 and 1508. In step 1502, EMG signals are first 
acquired. After this data is obtained, the EMG signals are analyzed in 
step 1504. Once processed and analyzed, parameters are determined from the 
EMG signals that are indicative of a obstetrical diagnosis in step 1506. 
Finally, a diagnosis is made or predicted in step 1508. Within this 
general framework, a wide variety of data analysis techniques may be 
employed to analyze EMG signal for obstetrical diagnosis. 
These analysis techniques may include: (1) power-density spectral analysis 
based upon 3-dimension mesh plots (e.g., energy level vs. frequency vs. 
time of pregnancy), (2) potential vector analysis, and (3) other 
analytical techniques, such as integration of the EMG signals to provide 
approximate total energy within a burst of action potentials, fast wavelet 
transform analysis, and joint time-frequency analysis. 
These analytical systems provide important information on EMG that can be 
used for diagnosis. These systems are based upon the recording of uterine 
electrical activity from the abdominal surface as described above. The 
abdominal electromyogram (EMG), or electrohysterogram (EHG), may be 
analyzed and the resulting analysis used to facilitate the clinical 
evaluation of uterine activity during pregnancy. The present invention may 
also be utilized for the early diagnosis of abnormal uterine contractility 
by analyzing uterine EMG signals. Such diagnoses take advantage of the 
characteristic that uterine electrical activity gradually changes during 
the last month of pregnancy until parturition. Weak and localized at the 
beginning of labor, this electrical activity becomes stronger, rhythmical 
and well propagated during labor. EMG, therefore, offers much information 
about both excitation and propagation of uterine activity. 
These data analysis techniques also rely upon an unique approach to 
characterizing EMG frequency components of a burst of action potentials. 
FIG. 16 shows the EMG frequency components of a burst of action potentials 
recorded from a human uterus. A burst of action potentials is shown in 
FIG. 17, which provides an amplitude versus time graph of a burst of 
action potentials recorded from the abdominal surface of a pregnant 
patient. 
In a preferred embodiment, the uterine activity may be analyzed by 
determining (a) the mean frequency of a plurality of action potentials in 
at least one measured burst, (b) the starting frequency of action 
potentials in at least one measured burst, and (c) the ending frequency of 
action potentials in at least measured burst. In another preferred 
embodiment, the uterine activity may be analyzed by determining the rate 
of rise of amplitude in at least one action potential in at least one 
burst, and the rate of fall of amplitude in at least one action potential 
in at least one measured burst. 
Referring back to FIG. 16, F1 (less than 0.005 Hz) is representative of low 
frequency components in the EMG data, such as the periodic occurrence of a 
burst. During human parturition, the burst frequency corresponds to a 
maximum of four contractions per 10 minutes (i.e., maximum F1=0.005 Hz). 
F2 (approximately 0.005 to 3.0 Hz) is representative of high frequency 
components, such as the intrinsic spike frequency within each burst. F2 
frequencies are believed to be more significant than F1 frequencies 
because they are related to the intensity of the uterine contraction. 
F2 can be divided into two distinct activities: a slow wave component and a 
fast wave component (FW). The slow wave component, ranging from 
approximately 0.005 to 0.03 Hz, is mainly obtained with abdominal 
recordings and is likely caused by mechanical artifacts. The fast wave 
component is of more importance and is the frequency band representative 
of uterine activity (about 1 Hz for a human). This fast wave frequency 
spectrum can be recorded in virtually all situations (myometrial or 
abdominal recordings, parturition of pregnancy). 
In turn, this fast wave component contains two specific domains: a 
low-frequency (FW.sub.L) domain present in any uterine electrical 
recording and a high-frequency (FW.sub.H) domain. The FW.sub.L domain 
relates to lower frequency components (e.g., FW.sub.L is in an approximate 
range from 0.01 to 0.06 Hz) having a longer duration (the duration mean 
was computed as 74.6 sec). In contrast, labor EMG is related to the 
presence of "high" frequency components (e.g., FW.sub.H is in an 
approximate range from 0.06 to 3.0 Hz) having a shorter duration (the 
duration mean was computed as 59.3 sec). 
The relationship between FW.sub.L and FW.sub.H has been found to indicate 
preterm and term uterine activity. It may also be useful in making other 
obstetrical diagnosis. This relationship can be expressed by various 
parameters, including parameters that are not dependent on the individual 
patient. Thus, these parameters may be used as differentiation parameters 
in indicating diagnoses and characterizing uterine activity. 
The following data analysis techniques are based upon analyses of EMG 
signals using various approaches and represent different definitions and 
calculations of these parameters. 
1. Power-Density Spectral Analysis 
a. Spectral Analysis Technique 
A spectral analysis method may be used to obtain important spectral 
characteristics of uterine EMG and defme some key parameters useful in the 
differentiation of normal and abnormal uterine activity. 
To specify the frequency range that only concerns the previously defmed 
"fast" electrical activity, spectral analysis processing is performed on 
the phases of EMG corresponding to contractions of the pressure signal. 
The power density spectrum (PSD) curve which is computed from the filtered 
"fast" (0.2-3.0 Hz) electrical burst relates to one contraction (see FIG. 
17). The pregnancy contraction mainly corresponds to the FW.sub.L 
frequency band and the labor contraction to the FW.sub.H band. The energy 
on each of FW.sub.L and FW.sub.H band can be calculated. Defining 
E.sub.fwL as the energy of FW.sub.L band and E.sub.fwH as the energy of 
FW.sub.H, the total energy E.sub.fw on the FW band is E.sub.fwL 
+E.sub.fwH. The formula is as follows: 
##EQU1## 
Referring to FIG. 18, which depicts an EMG power spectral density (PSD) 
curve, the geometrical significance of E.sub.fwL and E.sub.fwH are the 
areas under the PSD curve. The physical significance of E.sub.fwL and 
E.sub.fwH is the energy of contraction in pregnancy phases. The energies 
in different pregnancy phases and labor stages are expected to be 
different. The EMG signals that are processed are discrete signals. Their 
PSD are also discrete functions. So, E.sub.fwL and E.sub.fwH can be 
calculated as follows: 
##EQU2## 
The energy may be dependent on the individual patient. Supposing that the 
ratio (Re) of E.sub.fwL and E.sub.fwH is independent of individuals, 
standard ratios (possibly invariable) may be developed at each different 
stage of pregnancy, pre-labor and labor. Thus, the following ratio may be 
particularly useful as a parameter indicative of an obstetrical diagnosis: 
EQU Re=E.sub.fwH /E.sub.fwL. 
Based on this supposition, the ratio in the early pregnancy phase is 
expected to be smaller than the ratio in later pregnancy and the labor 
phases. This ratio will increase with the days of pregnancy. This means 
the E.sub.fwL decreased but the E.sub.fwH increased. In the normal state, 
the ratio in a certain phase of pregnancy or labor is expected to be a 
standard ratio. If the ratio in certain phases of pregnancy is larger than 
the standard ratio in this phase, the woman may be considered to be in an 
abnormal pregnancy state (the commencement of premature labor or 
miscarriage). If the ratio is smaller than the normal ratio, the woman may 
be in danger and may suffer post-labor or dystopia problems. 
FIGS. 19A-19B and 20A-20B show power density spectra for FW.sub.H 
components during labor. FIG. 19A is a burst of action potentials recorded 
from a rat's abdominal surface. FIG. 19B is a power density spectra 
obtained for the burst shown in FIG. 19A. As evident from FIG. 19B, labor 
in a rat is indicated by a large frequency component between 2-3 Hz. FIG. 
20A is a burst of action potentials recorded from a patient's abdominal 
surface. FIG. 20B is a power density spectra obtained for the burst shown 
in FIG. 20A. As evident from FIG. 20B, labor in a woman is indicated by a 
large frequency component at approximately 1 Hz. 
b. Application of Spectral Analysis 
The development of spontaneous uterine contractility exists throughout 
pregnancy, labor, and the early postpartum. Changes in magnitude of 
uterine activity in relation to duration of pregnancy and to the 
successive stages of labor and postpartum increases dramatically during 
labor. These correspond to the tremendous increase in electrical activity 
(PSD) during labor. 
In the examples described below, the whole EMG data was classified as 
normal or abnormal (pre-labor, miscarriage, post-labor, labor difficulty, 
etc.) according to the end-result. If a woman finished normal delivery, 
the whole EMG data was classified as normal, otherwise it was classified 
as abnormal. After acquiring EMG data, the temporal and spectral 
characteristics of this data was analyzed for important statistical 
parameters. The parameters include Re, E.sub.FWL, E.sub.FWH, duration of 
contraction, and statistical data, such as correlation coefficient and 
covariance. 
In the study, two Ag--AgCl Beckman electrodes (8 mm in diameter, 25 mm 
spaced centers) were placed on the abdominal wall after careful 
preparation of the skin. They were located on the median vertical axis, 
halfway between uterine fundus and the symphysis, thus parallel to the 
more superficial uterine fibers. The ground electrode was located 
laterally on the hip. These electrode locations, based on common anatomic 
references, have been found to provide good EMG results. 
The MacLab digital signal acquiring system was used to obtain EMG signals. 
The mechanical effect of uterine contractions were recorded by the 
existing method of pressure recording with an intrauterine catheter or 
surface pressure with a tocodynamometer. The pressure recording detects 
weak contractions in pregnancy. The pressure signal may be simultaneously 
recorded with the EMG, providing a time reference for the appearance of 
contractile activity. 
c. Filtering to Eliminate Noise 
The raw EMG signals can be contaminated by noise. The noise includes 
respiratory artifact, background noise due to motion, and ECG (material 
and fetal). Adaptive Noise Canceling (ANC) and Adaptive Line Enhancer 
(ALE) methods with the LMS (least mean squares) adaptation algorithms were 
used to cut off the noise. The noises were first classified into reference 
noises and background noise that contaminate the EMG signal according to 
their frequency range. The frequency range of the EMG fast wave component 
lies mostly between 0.2 Hz and 3 Hz. The frequency of respiratory 
artifacts is around 0.3 Hz. Some overlap, therefore, exists between 
frequencies caused by the respiratory artifacts and the EMG signals. An 
adaptive noise canceler is used to eliminate the respiratory artifact from 
the desired uterine EMG measurements. 
The ECG signal (from heart activity) frequency range is greater than 2 Hz. 
Thus, there may also be some overlap between ECG and EMG signals. However, 
this overlapping part of the ECG signal has very low energy. The 
nonoverlapping part of the ECG can be processed as background noise. Thus, 
the noises that contaminate the EMG signal are mainly respiratory signals 
(mostly overlap with EMG) and background noise (including ECG signals). 
Referring to FIG. 21, an adaptive system for reducing the noise in the EMG 
signal is shown. In particular, an adaptive filter system is shown, which 
consists of the Adaptive Line Enhancer (ANE), shown on side (a) of FIG. 
21, and the Adaptive Noise Canceler (ANC), shown on side (b) of FIG. 21. 
The components for these systems are available from Newark Electronics, 
Chicago, Ill. Both filters use a LMS (least mean square) algorithm. 
In operation, the main signal d.sub.j, which includes the EMG signal or 
primary signal (s.sub.j), is bandpass filtered with bandwidth 0.2-3.0 Hz 
before adaptive filtering. An adaptive noise canceler (ANC) is used to 
filter out respiratory noise (r.sub.j). Here, a respiratory signal is also 
a reference signal (r.sub.j) of the ANC. A respiratory reference signal is 
recorded by positioning a pressure transducer on the chest above the 
diaphragm. The Adaptive Line Enhancer (ANE) is to eliminate the background 
noises n.sub.j from the primary signal s.sub.j. The delay, .DELTA., is 
chosen as one constant according to practical experiments to decorrelate 
the periodic and correlated signals s.sub.j and r.sub.j from n.sub.j. 
Thus the Adaptive Line Enhancer (ANE) part of the adaptive system functions 
as a preprocessing unit to eliminate the background noise n.sub.j. The 
adaptive noise canceler is used to eliminate respiratory artifacts 
(r.sub.j). After the processing, an enhanced main EMG signal (s.sub.j) is 
acquired and may be analyzed. 
d. Time and Shape Analysis of Burst Power 
The power density spectral analysis does not define differences in power 
density at different time points within the burst. This is likely 
important during pregnancy when electrical coupling is poor prior to term 
as compared with well coupling during labor. In this type of analysis, the 
power of the burst over time is expressed. Prior to term, this analysis is 
expected to show a weak initial signal with a peak during the mid-phase of 
the burst. Because electrical coupling is improved during normal labor, 
the power per unit time is expected to be maximal with little change from 
beginning to the end of a burst. 
e. Application of Spectral Analysis Technique 
To specify the frequency range representative of the previously defmed 
"fast" electrical activity, the spectral analysis is performed on the EMG 
signal phases corresponding to contractions. The contractions are detected 
by recording pressure at the same time when the EMG signals are recorded. 
The power spectral density is computed in the frequency range 0.2-3.0 Hz 
with a Kaiser Window Filter. An estimation technique of parametric 
modeling methods was used to calculate the PSD. Parametric modeling 
methods include AR (Autoregressive Methods) and ARMA (Autoregressing 
Moving Average Method). 
According to the PSD, the energy on discriminating frequency bands FW.sub.L 
and FW.sub.H and the energy ratio Re can be calculated. After acquiring a 
group of Re's, statistical values of Re and its standard deviation (SD) 
can be calculated. The power spectral analysis (PSD) can be carried 
further along into coherence analysis if necessary, such as that described 
in Sica, et al., Respiration--Related Features of Sympathetic Discharges 
in the Developing Kitten, Journal Automatic Nervous System, 44:77-84, 
1993; and Sica, et al., Evidence for Central Patterning of Sympathetic 
Discharge in Kittens, Brain Research, 530:349-352, 1990. 
Focusing on frequency analysis of the amplitude or energy levels in bursts 
of action potentials at various times in pregnancy, mesh plots of the 
"power density" of the bursts of action potentials can be created. FIGS. 
22A-22F represent graphical depictions of power density analyses and 
3-dimensional mesh plots for EMG data measured from the uterine activity 
at a rat's abdomen surface. 
FIG. 22A shows a burst of action potentials from the EMG signal, as well as 
the corresponding intrauterine pressure (IUP) changes, recorded from a 
pregnant rat at day 22, where the rat was not in labor. FIG. 22C shows a 
burst of action potentials form the EMG signal, as well as the 
corresponding intrauterine pressure (IUP) changes, recorded from a 
pregnant rat at day 22, where the rat was in labor. Only frequencies 
between 0.3-50 Hz in the EMG signal are shown in FIG. 22A and FIG. 22C. 
FIG. 22B shows a power density spectral analysis of the burst shown in FIG. 
22A. For a rat not in labor, no large frequency components are present, 
except for a component below 0.5 Hz. This component may attributable to 
respiratory noise or breathing. FIG. 22D shows a power density spectral 
analysis of the burst shown in FIG. 22C. In stark contrast to the 
frequency components present in FIG. 22B, large frequency components are 
present in FIG. 22D. Comparing FIG. 22B and FIG. 22D, it is significant to 
note that the peak frequency of the action potentials occurs between 
approximately 2-3 Hz in a delivering rat and that very high energy levels 
occur during labor as compared to a non-delivering rat. It should also be 
noted that a large frequency component is still present in FIG. 22D below 
0.5 Hz, which may again be attributed respiratory noise or breathing. 
Referring to FIG. 22E, a mesh plot (energy levels vs. frequency vs. time of 
pregnancy) is shown that was prepared from many rats at different times in 
pregnancy. FIG. 22E shows that the energy level was very low (flat) prior 
to labor at days 18 to 22 NL (NL representing "non-labor"). Energy levels 
then rose sharply during labor at day 22 L (L representing "labor"). The 
energy levels then declined rapidly during postpartum days 1-2 pp (pp 
representing "postpartum"). Such data can be used as a basis for 
performing a predictive analysis of future uterine activity. 
Referring to FIG. 22F, a combined mesh plot is shown. This mesh plot 
includes a mesh plot from EMG measurements from a rat for a normal 
delivery overlapped with a mesh plot from EMG measurements from a rat for 
a preterm delivery. Preterm delivery was achieved by treating a rat with 
ZK299 (onapristone) to induce preterm birth. As evident from FIG. 22F, 
energy levels for a forced preterm delivery were also greatly increased 
over a normal pregnancy as shown in FIG. 22E. 
The data in FIGS. 22A-22F, therefore, show that power density spectral 
analysis of the EMG signals from bursts of action potentials may be used 
to predict when an animal is in term or preterm labor. The changes in the 
power density spectra from low to high levels reflect increased excitation 
and propagation of action potentials in the myometrium during labor and 
delivery. Thus, the spectral analysis provides a technique for indicating 
or predicting treatment during pregnancy. Further, these analyses may 
provide indications of other obstetrical diagnoses. 
The techniques described above were also used to measure EMG signals of 
bursts of action potentials in pregnant women. FIGS. 23A-23G represent 
graphical depictions of power density analyses and a 3-dimensional mesh 
plot for EMG data measured from the uterine activity at a woman's abdomen 
surface. 
FIGS. 23A-23B show electrical activity recorded from two sites on the 
abdominal surface in a patient at 27 weeks of gestation (FIG. 23A) and in 
a term laboring patient (FIG. 23B). The patient at 27 weeks was 
progressing normally and appeared for a weekly perinatal visit. She had 
worked all day and felt minor contractions that corresponded to the EMG 
activity (arrows in FIG. 23A). Eventually, this patient delivery a healthy 
boy at term. In contrast, the patient at term labor in FIG. 23B shows high 
amplitude and frequent (every 1/2 min.) EMG bursts. This patient delivered 
a normal baby girl about 30 minutes after the above record was made. 
Comparing 23A and 23B, it is evident that the frequency and amplitude of 
EMG bursts are quite different between the term and preterm patients. 
FIGS. 23A-23B also provide duplicate readings. The first set was made using 
large electrodes. The second set was made using small electrodes. Both the 
small and large electrodes were found to be effective. 
FIG. 23C shows a burst of action potentials from the EMG signal recorded 
from a pregnant woman at 27 weeks, where the woman was not in labor. FIG. 
23E shows a burst of action potentials form the EMG signal recorded from a 
pregnant woman at week 42, where the woman was in labor. Only frequencies 
between 0.3-50 Hz in the EMG signal are shown in FIG. 23C and FIG. 23E. 
FIG. 23D shows a power density spectral analysis of the burst shown in FIG. 
23C. For a woman not in labor, no large frequency components are present. 
FIG. 23F shows a power density spectral analysis of the burst shown in 
FIG. 22E. In stark contrast to the frequency components present in FIG. 
22D, large frequency components are present in FIG. 22F. Comparing the 
power spectrum analyses of the EMG bursts from the above patients (FIG. 
23D and FIG. 23F), the bursts from the patient at 27 weeks gestation 
showed low energy levels (FIG. 23D), while the term labor patient 
demonstrated very high energy with a peak frequency about 1 Hz (i.e., 1 
action potential per second) (FIG. 23F). It should also be noted that a 
large frequency component is present in FIG. 22F below 0.25 Hz, which may 
be attributed to respiratory noise or breathing. 
FIG. 23G shows a power density spectral mesh plot (energy levels vs. 
frequency vs. weeks of gestation) for EMG measurements of bursts of action 
potentials from a pregnant patient during gestation. Analysis of such data 
for many patients showed very similar results to the rat studies above 
(compare to FIG. 22E). In particular, it was found that the power density 
spectrum is relatively low (flat) prior to term labor, but rises 
dramatically during labor. 
The above data indicates that spectral analysis of EMG bursts in women is 
of considerable value in evaluating the contractile state of the uterus 
during pregnancy. Information from these analyses can be used to dictate 
treatment. Patients at term with low power density spectrum score would 
not be expected to have passed through the series of steps necessary for 
excitation of the myometrium and, therefore, may require adjuvant 
treatment to augment labor. On the other hand, patients with a high score, 
normal for labor, would be predicted to progress without any treatment. 
The score may also be used to predict the likelihood of successful 
induction of labor with oxytocin at term. 
Similarly, spectral analysis may be useful to foretell treatment and 
outcome of patients thought to be in preterm labor. Patients thought to be 
in preterm labor that have a low spectrum score might be expected to 
proceed normally to term. High spectral scores preterm should be 
coincident with true labor contractions and this information may lead to 
effective treatment to prevent preterm birth. 
Spectral analysis may also be used to estimate the transition from low to 
high energy levels. Thus this technology may be helpful to monitor 
patients continuously in order to assess normal or abnormal progress. 
These methods will also be particularly useful for remote or "home" 
uterine recordings and monitoring. In this way, patients can easily be 
instrumented and monitored from a distance via a communications link, such 
as telephone lines. 
Spectral data for many patients may be collected to provide a knowledge 
base from which to predict future uterine activity based upon one or more 
identified trends in a patient's examined uterine activity indicating 
parameters. In performing a predictive analysis an identified trend in the 
patient's examined uterine activity indicating parameters is compared to 
other trends in the knowledge base of an expert system. When matching 
trends are found between the patient's trends and trends in the expert 
system for an identified time period, T.sub.I, a prediction is made 
regarding future uterine activity based upon how the matching trends in 
the knowledge base have behaved beyond time period T.sub.I. In a preferred 
embodiment, a chaotic model of electromyographic activity may be used to 
predict future uterine activity. 
2. Potential Vector Analysis Using Vectorhysterograms 
A vector analysis of action potentials may also be used to determine useful 
parameters for obstetrical diagnosis. The uterine electromyogram is the 
result of electrical activity generated at the cellular level. The 
potential at any arbitrary point on the abdominal surface, back and sides 
from a pregnant woman may be measured and recorded, and the whole uterus 
can be modeled as a dipole vector. If the vector represents the spread of 
uterine myometrium excitation, the orthogonal component of the vector can 
be recorded. The orthogonal vector component P.sub.x (t), P.sub.y (t) and 
P.sub.z (t) of the vector P and its direction can be determined and 
analyzed. 
a. General Principles 
Body surface potential vector analysis is based on Frank's torso experiment 
model and research results. In the 1950's, Frank shaped a plaster cast of 
a subject's body, waterproofed it and filled it with saltwater. He then 
placed a dipole source composed of two electrodes on a rod within the 
torso model. 
From measurements in such experiments, Frank found that the geometrical 
transfer coefficients that relate the dipole source to each point of the 
body surface potential V.sub.n (t). Thus for a set of k body surface 
potentials, there is a set of k equations that can be expressed in matrix 
from: 
EQU V=T*P. 
Here, V={v.sub.1 . . . , V.sub.K }.sup.T ;T={T.sub.1, T.sub.2, T.sub.3 
};T.sub.1 ={t.sub.11, . . . , t.sub.1K }.sup.T ; T2={t.sub.21, . . . , 
t.sub.2K }.sup.T ; T.sub.3 ={t.sub.31, . . . ,t.sub.3K }.sup.T ; P=55 
P.sub.x, P.sub.y, P.sub.z }. V is a K*1 vector, T is a K*3 transfer 
coefficient matrix. P is the 3*1 time-varying dipole source vector. 
Based upon this dipole analysis, and making the dipole source a uterus of a 
pregnant women, the potential at any point and at the same time can be 
measured to obtain the orthogonal vector component of the action 
potentials on an XYZ axis. FIG. 24 illustrates the placement of electrodes 
on a patient, and a 3-dimensional position of electrodes located on an XYZ 
axis. 
When acquiring the six point potential at any time, the vector component on 
X, Y axis at this time is also obtained. That is: 
##EQU3## 
It is noted that the direction is that the vector points toward the 
electrode with higher potential. For example, if P.sub.x &gt;0, then the 
direction is in X positive direction. 
b. EMG Signal Recording and Noise Canceling po Six bipolar pairs of 
Ag--AgCl beckman electrodes may be used. Other electrodes may be used, 
such as unipolar, tripolar, etc. They may be arranged on the abdominal 
surface, sides and back of a patient, as shown in FIG. 24. It is also 
possible to use a fewer or a greater number of electrodes, as shown in 
FIG. A. An abdominal sheath (A1) shown in FIG. A is embedded with an array 
(A2) of electrodes (A3) from which EMG signals are collected for further 
processing. In other embodiments the coverage of the sheath may be less 
than or greater than shown here, so that sheath may reach around the back 
of the patient and higher or lower on the pelvis and torso, and the number 
of electrodes may be greater than or less than illustrated here and the 
electrodes may be selectable. An ECG electrode may also be included for 
purposes of noise reduction. 
At each point the system records the EMG signal corresponding to the 
uterine contraction. A Maclab digital signal processing system is used to 
acquire the EMG signals at each point. The EMG signal at each point will 
likely be contaminated by noises. The Adaptive Noise Canceler (ANC) and 
Adaptive Line Enhancer (ANE) may be used to cut off the noises, as 
discussed above. 
C. Analysis 
Six channel EMG signal segments are selected to correspond to the 
mechanical contact segments of the uterus. Data is then saved into one 
data file. The data file is then analyzed. 
To analyze the data, the orthogonal vector components P.sub.x, P.sub.y and 
P.sub.z are acquired. According to the orthogonal vector component 
direction, the tracing of uterine potential vector P(t) can be divided 
into 8 areas in a 3-dimension space. The rules are as follows: 
______________________________________ 
P.sub.x (t) 
P.sub.y (t) P.sub.x (t) 
Area No. 
______________________________________ 
+ + + 1 
- + + 2 
- - + 3 
+ - + 4 
+ + - 5 
- + - 6 
- - - 7 
+ - - 8 
______________________________________ 
At each sample time, the system will calculate the direction vector 
{.phi..sub.x,.phi..sub.y,.phi..sub.z } of uterine potential vector P(t) as 
shown graphically in FIG. 25. .phi..sub.x is the angle of vector P(t) with 
X axis in 3-dimension. .phi..sub.y is the angle of vector P(t) with Y axis 
in 3-dimension. .phi..sub.z is the angle of vector P(t) with Z axis in 
3-dimension. The formula of calculating the angle is as follow: 
##EQU4## 
The present system may display the vector P(t) tracing in 3-dimension on a 
computer screen. The user can select the demonstration speed that controls 
the tracing on screen. When the lower speed is selected, the details of 
the tracing changes at each sample time can be easily observed. The system 
supports another method to assist the user to analyze the changes in 
progression of P(t) at each sample time. At each sample time t.sub.i, the 
system will determine which area vector P(t.sub.i) should be in according 
to the rules of area division. The system will draw out the change in 
progression in order of P(t) as follow: 
______________________________________ 
Time: t.sub.0 t.sub.0 + .DELTA.t 
t.sub.0 + 2.DELTA.t 
t.sub.0 + 3.DELTA.t . . . 
Order: 
1 to 3 to 5 to 6 to . . . 
Angle: 
{12,24,53} to 
{23,54,12} to 
{3,23,15} to 
{13,34,60} 
to . . . 
______________________________________ 
The results can be printed on a printer. Using this method the vector of 
activity can be defined, and the origin and spread of activity may also be 
defined. Using this technique, pacemaker regions and direction of 
propagation of uterine electrical activity may be identified. These 
parameters may then be used to predict treatment of pregnant women or to 
make other obstetrical diagnoses. 
3. Other Data Analysis Techniques 
As would be obvious to one of skill in the art, other analytical techniques 
may be utilized to analyze the uterine electrical activity data described 
above. 
Using multiple electrodes as shown in FIGS. 24A-24C, a field of vectors 
representing activity at various points on the uterine surface can be 
constructed. This information can be used to characterize the behavior of 
the electrical activity locally by mapping the vector at each point onto 
an ovoid surface. The appearance of the map may be useful in staging labor 
or prelabor and in identifying conduction anomalies. In addition to 
providing information about overall conduction, this data will also 
evaluate the homogeneity of the electrical behavior at a number of 
locations on the surface of the uterus. These data can help to localize 
excitation spots and dead zones. In a preferred embodiment, electrodes 
e1-e6 can be affixed to a belt that is worn by the patient, as shown in 
FIG. 27. 
One alternative technique is to integrate the energy measured for a burst 
of action potentials. Using this technique, the electrical signals in a 
burst of action potentials recorded from the uterus are first squared, 
then summed and the total area under the curve is then integrated. This 
analysis gives a rough estimate of the energy within a burst of action 
potentials. It does not, however, account for the length or time component 
of the data measured. Thus, this analysis could be extended by further 
dividing the approximate total energy for the burst of action potentials 
by the total time of the burst to determine the integrated function/time 
value or energy per unit time for the burst. 
Phase information may also be applicable to the analysis. In FIGS. 30A-30B, 
both phase and amplitude information are presented. In each panel, the two 
components of the FFT, real and imaginary, have been plotted for each 
frequency. Each point represents the FFT at a given frequency. Consecutive 
points, which are joined by lines, represent values at, for example, 1 Hz, 
2 Hz, 3 Hz, and so on, up to the maximum frequency. Although it is not 
apparent in such a plot which point corresponds to which frequency, one 
can identify sequential points. The amplitude information is given by the 
distance of each point from the center of the graph; the phase information 
is given by the angle with respect to the real axis. These plots show that 
the active term data is characterized by having frequency components of 
large amplitude at a plurality of different frequencies, equivalent to 
saying that the power spectrum is broad. The points jump around rapidly 
from quadrant to quadrant, so there is no simple way to describe the phase 
pattern for successive frequencies. (The top and bottom half of each graph 
are mirror images because of the symmetry of the FFT.) The net result is 
to produce a star-like pattern during active labor. The data before labor 
has much smaller power (or amplitude) at each frequency, and fewer 
frequencies have significant power. Therefore, the pattern is necessarily 
much simpler. This analysis was constructed using the fft and plot 
commands in Matlab. For each top panel, the pattern produced by one muscle 
action potential (256 points at 200 Hz) in rat uterus is shown (raw data 
in bottom panels). 
Another technique that may be utilized is a fast wavelet transform 
technique. This technique could be adapted from that described in Cody, 
The Fast Wavelet Transform, Dr. Dobb's Journal (April 1992); Cody, A 
Wavelet Analyzer, Dr. Dobb's Journal (April 1993); and Cody, The Wavelet 
Packet Transform, Dr. Dobb's Journal (April 1994); for ECG, see A. Djohan, 
T. Q. Nguyen and W. Tompkins, "ECG Compression using Discrete Symmetric 
Wavelet Transform," International Conf. EMBS, Sept. 1995; 
Sri-KrishnaAditya, Chee-Hung H. Chu, and Harold H. Szu, "Application of 
adaptive subband coding for noisy bandlimited ECG signal processing", SPIE 
Proceedings Vol. 2762, pp. 376-387). 
One objective of wavelet analysis, like Fourier analysis, is to re-express 
data in terms of frequency content (or the equivalent). Wavelet analysis 
belongs to a general set of approaches called time-frequencyanalysis. 
Unlike Fourier frequency analysis, time-frequency analysis determines the 
"instantaneous" frequency content at each time point and is more 
appropriate for signals whose frequency characteristics clearly change 
with time. The continuous wavelet transform essentially describes the 
signal in terms of all possible frequencies, or scales. 
In FIGS. 31A-31D, action potentials from active term labor or nonlabor are 
analyzed by this method. Analysis of rat uterine EMG data was carried out 
using the continuous wavelet transform (CWT) and the wavelet function 
"db10". The Wavelet Toolbox of the Matlab software suite was run on a 66 
MHZ computer comprising a Pentium.RTM. microprocessor. The CWT yields a 
three-dimensional decomposition of the electrical activity. One axis in 
FIGS. 31A-31D is "time". The second axis, referred to as "scale", provides 
information analogous to "frequency" in the discrete Fourier transform 
(DFT). Smaller values of the "scale" translate into high frequencies, 
larger into the slower frequencies. The z-axis values are the wavelet 
coefficients, analogous to the amplitude of the DFT, being proportional to 
the frequency content detected. 
Like other "spectrogram-type" methods, such as joint time-frequency 
analysis, a primary advantage of this type of analysis is the localization 
in time of nonstationary frequency behaviors. FIGS. 31A-31D show the 
"instantaneous" frequency content. The key difference between the labor 
and nonlabor is seen at low values of scale, equivalent to high values of 
frequency. During labor, many peaks are seen at low values of scale but 
during nonlabor, the surface is essentially flat at the same values of 
scale. Based on the multiple large peaks at small scale values, it is 
apparent that the "active labor" sample (left) contains much more high 
frequency activity, occurring during the muscle action potentials, than is 
present in "late pregnancy" (right). In FIGS. 31B and 31D the voltage 
scale differs between the two bottom panels and the data was acquired at 
200 Hz . 
The discrete wavelet transform (DWT) may also be used to analyze the data. 
The DWT takes advantage of the striking mathematical properties of 
wavelets to greatly reduce the number of scales needed. The discrete 
wavelet transform, splits the signal, roughly speaking, into two parts, 
equivalent to processing with a high-pass filter and a low-pass filter, 
and the process is iterated, although only on the output from the low-pass 
filter. 
FIG. 32A shows data from women during nonlabor, preterm labor, and term 
labor. In the top half of the figure, the original data along with the 
decorrelated output for an eight-level discrete wavelettransform using the 
"s8" wavelet. The bottom scale corresponds to number of points, where data 
was acquired at 200 Hz. The term and preterm labor differ from nonlabor in 
the proportion of activity in different subbands. S8 shows the "smooth" 
part of the signal in each case. D1 to D8 are the details at each level. 
The bottom half of the figure shows the fraction of the total energy in 
the signal in each subband. During nonlabor, approximately 80% of the 
energy is contained in S8. During labor, this energy is split roughly 
equally between S8 and D8, which contains the next highest frequency 
subband. Before labor the S8:D8 ratio is approximately 8; during preterm 
or term labor it is close to one. In the examples shown, little high 
frequency noise may be observed in the other channels. This data was 
previously denoised by the wavelet shrinkage technique. All processing was 
carried out using the S+ wavelets program. 
A generalization of this process, which iterates the filtering on either or 
both of the high-pass and low-pass outputs at each stage, is called 
wavelet packet analysis. Wavelet packet analysis does not limit subband 
processing to the low-pass signals, but lets you focus, by selecting the 
right "tree", on any frequency band you wish. An example is shown in FIG. 
32B using rat data. In this case data from rat myometrium at either day 20 
(left top panel) or delivery (right) was decomposed using a more elaborate 
tree (center). The bottom panels show the difference between the channel 
marked "2,1" for preterm and term uterus. The spikes on either end of the 
trace should be ignored; these are "edge effects." The action potential 
activity at delivery is clearly detected on this channel, whereas the same 
channel is essentially blank. Thus either the DWT or wavelet packet 
analysis may be used for detecting the new high-frequency components which 
characterize labor. Wavelet packets may help to breakdown activity into 
finer subbands. In addition, each of these approaches can be used to 
compress the EMG signal with high efficiency before storage. 
A still further technique that may be adapted and utilized is a joint 
time-frequency analysis according to Gabor or windowed Fourier analysis. 
These techniques could be implemented utilizing the LabVIEW and the 
Joint-Time Frequency Analysis (JFTA) Toolkit available from National 
Instruments, Austin, Tex. A still further technique is cosine packet 
analysis. This technique may also be implemented using the S+ Wavelets 
program. 
FIG. 33 shows an example of wavelet-based compression of the EMG. Using the 
"wavemenu" program of the Matlab Wavelet Toolbox (and the wavelet function 
"db10"), data was compressed by thresholding so that out of 100% (raw 
data), only 5% (20:1) of the wavelet coefficients, or only 2.5% (40:1) of 
the wavelet coefficients, were nonzero. With only the largest 2.5% of the 
wavelet coefficients retained, the signal is little distorted, especially 
for the largest action potentials which occur during a burst. A 
corresponding procedure can be used for denoising of signals. The x axis 
refers to the number of points in the original signal (16K). The 
acquisition frequency was 200 Hz. As the number of retained coefficients 
becomes smaller, a limiting situation occurs where one coefficient remains 
for each large action potential. In thise case, the largest coefficients 
are located at large action potentials and these coefficients therefore 
identify the large action potentials. Changes in the shape of action 
potentials can be reflected in the "scale" of the wavelets whose 
coefficients are the largest. 
FIG. 34 illustrates the use of the uterine EMG signal analysis during 
pregnancy. Because some isolated contractions occur throughout pregnancy, 
these methods could be used to analyze the mild patterns of contraction 
(i.e. Braxton-Hicks contractions) that presage normal labor. Mild 
contraction patterns during pregnancy may also provide the opportunity to 
study patterns associated with incipient preterm labor and enhance the 
ability to diagnose preterm labor at an early stage in high-risk 
populations. This methodology could also be used to assess the uterine 
response to "a challenge," a test dose of an excitatory agent such as 
oxytocin, as a measure of the overall readiness to labor. 
Once labor has begun, the EMG signal analysis method taught by the present 
invention will be useful in diagnosing, assessing treatment options, and 
in predicting the course of labor. This may also be combined with 
conventional clinical methods, such as intrauterine pressure measurement. 
The methods of EMG signal analysis taught by the present invention may 
also be useful postpartum in assessing the regression of uterine activity. 
FIG. 35 indicates the uses of the present invention for non-pregnant women 
and men. There are some disorders of uterine contractility or motility 
that occur outside pregnancy. One of these is dysmenorrhea. It is caused 
by cramping during parts of the menstrual cycle. Another important 
disorder is due to smooth muscle tumors ("fibroids" or leiomyomata) in the 
myometrium. This disorder tends to be hyperexcitable compared to normal 
myometerium and can be very painful. Often, women are treated with 
hormones to produce regression of these tumors. Functional regression of 
such tumors may be montbred and analyzed with the present invention. Other 
smooth muscle organs in both men and women such as bladder and bowel, also 
show spontaneous activity which could be monitored and analyzed with the 
present invention. 
While the present invention has been presented with reference to particular 
embodiments, it will be understood that additions, deletions and changes 
to these embodiments may be made without departing from the spirit and 
scope of the present invention.