Analyzing uterine EMG: tracking instantaneous burst frequency

The uterine electromyogram (EMG) has been proven to be an efficient tool for pregnancy and parturition monitoring. Temporal and spectral characteristics of this signal, recorded by means of external electrodes, make it possible to discriminate between efficient and inefficient contractions in terms of electrical command capability. As it is possible to record the uterine electrical activity as early as 19 weeks gestational age, abdominal EMG can be of value for pregnancy monitoring especially if this noninvasive recording can be associated with ambulatory instrumentation. Much work has been achieved related to the characteristics of this signal as well as to its ability to explain, to a certain extent, uterine contractility. In previous studies on monkeys, the authors demonstrated the relationship between internal and external recordings, by simultaneously using internal wire electrodes on the uterine muscle itself and external abdominal bipolar Ag/AgCl electrodes. In the same preliminary studies, the authors defined the most relevant frequency bands in terms of significant modifications between pregnancy and parturition. Furthermore, they have proposed a criterion which is representative of contraction efficiency. However, the most important point has been to relate the observations made on the external recordings to internal cellular activity. In fact, uterine smooth muscle cells continuously exhibit resting potentials with small and slow spontaneous fluctuations. When these fluctuations reach a threshold, bursts of action potentials are induced. The frequency of the spikes within a burst appear to control the mechanical contraction. If it could be possible to extract such an information of within-burst frequency by means of external recordings, this should lead to a relevant characterization in terms of contraction efficiency. In addition, the EMG undergoes important modifications from pregnancy to parturition, primarily due to the appearance of gap junctions that facilitate electrical coupling between cells, and increase in the within-burst spike frequency. >

[1]  Robert J. Marks,et al.  The use of cone-shaped kernels for generalized time-frequency representations of nonstationary signals , 1990, IEEE Trans. Acoust. Speech Signal Process..

[2]  P. Bertrand,et al.  Optimum smoothing of the Wigner-Ville distribution , 1987, IEEE Trans. Acoust. Speech Signal Process..

[3]  Daniel Graupe,et al.  Time series analysis: Identification and adaptive filtering , 1984 .

[4]  S. Pearce,et al.  Time-frequency analysis of infant cry: measures that identify individuals. , 1993, Physiological measurement.

[5]  William J. Williams,et al.  Improved time-frequency representation of multicomponent signals using exponential kernels , 1989, IEEE Trans. Acoust. Speech Signal Process..

[6]  N. Martin,et al.  An AR spectral analysis of non-stationary signals , 1986 .

[7]  W. J. Williams,et al.  Cross Time-frequency Representation Of Electrocorticograms In Temporal Lobe Epilepsy , 1991 .

[8]  W. Koenig,et al.  The Sound Spectrograph , 1946 .

[10]  Douglas L. Jones,et al.  A signal-dependent time-frequency representation: optimal kernel design , 1993, IEEE Trans. Signal Process..

[11]  J. Makhoul,et al.  Linear prediction: A tutorial review , 1975, Proceedings of the IEEE.

[12]  W. J. Williams,et al.  New time-frequency analyses of EMG and TMJ sound signals , 1989, Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society,.

[13]  Patrick Flandrin,et al.  Wigner-Ville spectral analysis of nonstationary processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[14]  William J. Williams,et al.  Time -frequency Analysis Of EEG Recordings With The Reduced Interference Distribution , 1990, [1990] Proceedings of the Twelfth Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  Ajem Guido Janssen,et al.  On the locus and spread of pseudo-density functions in the time-frequency plane , 1982 .

[16]  Patrick Flandrin,et al.  Maximum signal energy concentration in a time-frequency domain , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[17]  Boualem Boashash,et al.  Patterns in Hilbert transforms and Wigner-Ville distributions of electrocardiogram data , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[18]  Robert J. Marks,et al.  Some properties of the generalized time frequency representation with cone-shaped kernel , 1992, IEEE Trans. Signal Process..

[19]  Douglas L. Jones,et al.  Signal-dependent time-frequency analysis using a radially Gaussian kernel , 1993, Signal Process..

[20]  Douglas L. Jones,et al.  A high resolution data-adaptive time-frequency representation , 1990, IEEE Trans. Acoust. Speech Signal Process..

[21]  N. Thakor,et al.  Multiresolution wavelet analysis of evoked potentials , 1993, IEEE Transactions on Biomedical Engineering.

[22]  L. Cohen,et al.  Time-frequency distributions-a review , 1989, Proc. IEEE.

[23]  C Marque,et al.  Human abdominal EHG processing for uterine contraction monitoring. , 1989, Biotechnology.

[24]  C. Y. Kao.  Electrophysiological Properties of the Uterine Smooth Muscle , 1977 .

[25]  Catherine Marque,et al.  Uterine EHG Processing for Obstetrical Monitorng , 1986, IEEE Transactions on Biomedical Engineering.

[26]  Catherine Marque,et al.  Uterine EMG spectral characteristics and instantaneous frequency measurement , 1992, 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[27]  Berkman Sahiner,et al.  Application of time-frequency distributions to magnetic resonance imaging of non-constant flow , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[28]  C Marque,et al.  Uterine electromyography: a critical review. , 1993, American journal of obstetrics and gynecology.

[29]  L. Griffiths.  Rapid measurement of digital instantaneous frequency , 1975 .

[30]  G.F. Boudreaux-Bartels.  An introduction to time-frequency signal representations in bio-engineering signal processing , 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.

[31]  Douglas L. Jones,et al.  A resolution comparison of several time-frequency representations , 1992, IEEE Trans. Signal Process..

[32]  Boualem Boashash,et al.  On estimating the instantaneous frequency of a Gaussian random signal by use of the Wigner-Ville distribution , 1988, IEEE Trans. Acoust. Speech Signal Process..

[33]  H. Akaike.  A new look at the statistical model identification , 1974 .

[34]  S.M. Kay,et al.  Spectrum analysis—A modern perspective , 1981, Proceedings of the IEEE.

[35]  Detection of stenotic plaque in arteries , 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.

[36]  Mingui Sun,et al.  Discrete-time instantaneous frequency and its computation , 1993, IEEE Trans. Signal Process..

[37]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .