Baseline Characteristics of Dual-Axis Cervical Accelerometry Signals

Dual-axis swallowing accelerometry is a promising noninvasive tool for the assessment of difficulties during deglutition. The resting and anaerobic characteristics of these signals, however, are still unknown. This paper presents a study of baseline characteristics (stationarity, spectral features, and information content) of dual-axis cervical vibrations. In addition, modeling of a data acquisition system was performed to annul any undesired instrumentation effects. Two independent data collection procedures were conducted to fulfil the goals of the study. For baseline characterization, data were acquired from 50 healthy adult subjects. To model the data acquisition (DAQ) system, ten recordings were obtained while the system was exposed to random small vibrations. The inverse filtering approach removed extraneous effects introduced by the DAQ system. Approximately half of the filtered signals were stationary in nature. All signals exhibited a level of statistical dependence between the two axes. Also, there were very low frequency oscillations present in these signals that may be attributable to vasomotion of blood vessels near the thyroid cartilage, blood flow, and respiration. Demographic variables such as age and gender did not statistically influence baseline information-theoretic signal characteristics. However, participant age did affect the baseline spectral characteristics. These findings are important to the further development of diagnostic devices based on dual-axis swallowing vibration signals.

[1]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1971 .

[2]  Giuseppe Baselli,et al.  Measuring regularity by means of a corrected conditional entropy in sympathetic outflow , 1998, Biological Cybernetics.

[3]  C. Schmidt-Lucke,et al.  Low frequency flowmotion/(vasomotion) during patho-physiological conditions. , 2002, Life sciences.

[4]  N. P. Reddy,et al.  Biomechanical measurements to characterize the oral phase of dysphagia , 1990, IEEE Transactions on Biomedical Engineering.

[5]  Richard A. Davis,et al.  Time Series: Theory and Methods , 2013 .

[6]  Tom Chau,et al.  Stationarity distributions of mechanomyogram signals from isometric contractions of extrinsic hand muscles during functional grasping. , 2008, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[7]  Henry Leung,et al.  Classification of audio radar signals using radial basis function neural networks , 2003, IEEE Trans. Instrum. Meas..

[8]  Ryo Ishida,et al.  Hyoid Motion During Swallowing: Factors Affecting Forward and Upward Displacement , 2002, Dysphagia.

[9]  R. Lees Phonoangiography: Qualitative and quantitative , 2006, Annals of Biomedical Engineering.

[10]  Yuhong Yang Can the Strengths of AIC and BIC Be Shared , 2005 .

[11]  J. D. Littler,et al.  The use of the maximum entropy method for the spectral analysis of wind-induced data recorded on buildings , 1997 .

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

[13]  B. Murdoch,et al.  Acoustic Signature of the Normal Swallow: Characterization by Age, Gender, and Bolus Volume , 2002, The Annals of otology, rhinology, and laryngology.

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

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

[16]  N. P. Reddy,et al.  Noninvasive acceleration measurements to characterize the pharyngeal phase of swallowing. , 1991, Journal of biomedical engineering.

[17]  Y. Selen,et al.  Model-order selection: a review of information criterion rules , 2004, IEEE Signal Processing Magazine.

[18]  W. M. Carey,et al.  Digital spectral analysis: with applications , 1986 .

[19]  Alberto Porta,et al.  Assessment of cardiac autonomic modulation during graded head-up tilt by symbolic analysis of heart rate variability. , 2007, American journal of physiology. Heart and circulatory physiology.

[20]  Jorma Rissanen,et al.  The Minimum Description Length Principle in Coding and Modeling , 1998, IEEE Trans. Inf. Theory.

[21]  Lalit Kalra,et al.  Can Pulse Oximetry or a Bedside Swallowing Assessment Be Used to Detect Aspiration After Stroke? , 2006, Stroke.

[22]  Richard A. Davis,et al.  Time Series: Theory and Methods (2nd ed.). , 1992 .

[23]  J. Logemann,et al.  Evaluation and treatment of swallowing disorders , 1983 .

[24]  Stan Z. Li,et al.  Content-based Classification and Retrieval of Audio Using the Nearest Feature Line Method , 2000 .

[25]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[26]  H. Lilliefors On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown , 1967 .

[27]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[28]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[29]  N. Hogan,et al.  Single site electromyograph amplitude estimation , 1994, IEEE Transactions on Biomedical Engineering.

[30]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[31]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[32]  T. Chau,et al.  Investigating the stationarity of paediatric aspiration signals , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[33]  R. Merletti,et al.  Estimation of shape characteristics of surface muscle signal spectra from time domain data , 1995, IEEE Transactions on Biomedical Engineering.

[34]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[35]  T Chau,et al.  Time and time–frequency characterization of dual-axis swallowing accelerometry signals , 2008, Physiological measurement.

[36]  Stan Z. Li,et al.  Content-based audio classification and retrieval using the nearest feature line method , 2000, IEEE Trans. Speech Audio Process..

[37]  C.-C. Jay Kuo,et al.  Content-based classification and retrieval of audio , 1998, Optics & Photonics.

[38]  N. P. Reddy,et al.  Measurements of acceleration during videofluorographic evaluation of dysphagic patients. , 2000, Medical engineering & physics.

[39]  Youngsun Kim,et al.  Maximum Hyoid Displacement in Normal Swallowing , 2008, Dysphagia.

[40]  M Intaglietta,et al.  Quantitation of rhythmic diameter changes in arterial microcirculation. , 1984, The American journal of physiology.

[41]  Sergio Cerutti,et al.  Entropy, entropy rate, and pattern classification as tools to typify complexity in short heart period variability series , 2001, IEEE Transactions on Biomedical Engineering.

[42]  Giuseppe Baselli,et al.  Conditional entropy approach for the evaluation of the coupling strength , 1999, Biological Cybernetics.

[43]  I. O'Brien,et al.  Heart rate variability in healthy subjects: effect of age and the derivation of normal ranges for tests of autonomic function. , 1986, British heart journal.

[44]  Amitava Das,et al.  Hybrid fuzzy logic committee neural networks for recognition of swallow acceleration signals , 2001, Comput. Methods Programs Biomed..

[45]  L. Marple A new autoregressive spectrum analysis algorithm , 1980 .

[46]  J. F. Tracy,et al.  Preliminary observations on the effects of age on oropharyngeal deglutition , 1989, Dysphagia.

[47]  A. Malliani,et al.  Information domain analysis of cardiovascular variability signals: Evaluation of regularity, synchronisation and co-ordination , 2000, Medical and Biological Engineering and Computing.

[48]  Thomas M. Cover,et al.  Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing) , 2006 .

[49]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[50]  Tom Chau,et al.  Segmentation of Dual-Axis Swallowing Accelerometry Signals in Healthy Subjects With Analysis of Anthropometric Effects on Duration of Swallowing Activities , 2009, IEEE Transactions on Biomedical Engineering.

[51]  G. Inbar,et al.  Autoregressive Modeling of Surface EMG and Its Spectrum with Application to Fatigue , 1987, IEEE Transactions on Biomedical Engineering.

[52]  Steven Kay,et al.  Modern Spectral Estimation: Theory and Application , 1988 .

[53]  P. Wang,et al.  First Heart Sound Detection for Phonocardiogram Segmentation , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[54]  Tom Chau,et al.  A radial basis classifier for the automatic detection of aspiration in children with dysphagia , 2006, Journal of NeuroEngineering and Rehabilitation.