Statistical Signal Characterization Research Articles

Discrimination between parkinsonian tremor and essential tremor using artificial neural network with hybrid features.

Essential tremor (ET) and the tremor in Parkinson's disease (PD) are the two most common pathological tremors with a certain overlap in the clinical presentation. The main purpose of this work is to use an artificial neural network to select the best features and to discriminate between the two types of tremors. The features used are of hybrid type obtained from two different algorithms: the statistical signal characterization (SSC) of the signal describing its morphology, and the soft-decision wavelet-decomposition (SDWD) features extracted from the accelerometer and surface EMG signals. The SSC method is used to obtain morphology-based features of the spectrum of the accelerometer and two surface EMG signals. The SDWD technique is used in this work to obtain the approximate spectral representation of both accelerometer and the two surface EMG signals. Two sets of data (training and test) are used in this paper. The training set consists of 21 ET subjects and 19 PD subjects, while the test set consists of 20 ET and 20 PD subjects. A neural network of the type feed forward back propagation has been used to combine best SSC features and best SDWD features of the accelerometer and EMG signals. Efficiency result of 92.5% was obtained using best hybrid features. The artificial neural network has been used successfully to combine two types of features in an automatic discrimination system between PD and ET.

Investigation of heart rate variability of patients undergoing coronary artery bypass grafting using the statistical signal characterization method.

Autonomic function can be estimated non-invasively using heart rate variability (HRV). HRV of patients undergoing coronary artery bypass grafting (CABG) is investigated in time-domain and frequency-domain before and after CABG to study the effect of operation on the status of patients. The main purpose of this work is to evaluate the effect of CABG surgery on patients with ischemic heart disease (IHD) before operation, and to monitor the status of patients on day 6 and day 30 after the CABG operation. The statistical signal characterization (SSC) technique is used in this work in order to derive different morphology-based parameters to indirectly describe time-domain and frequency-domain HRV parameters in 24 patients undergoing CABG operation, before the operation (Group 1: G1), 6days after operation (Group 2: G2) and 30days after operation (Group 3: G3). The data is obtained from the Sultan Qaboos University Hospital in Oman. The SSC parameters Mean(mt) and Mean(dt) are reduced in all 24 patients and in 23 out of 24 patients in G2 compared to G1 (6-days after operation compared with before operation), respectively. Comparing G3 to G1 the reduction in Mean(mt) and Mean(dt) is noted in 18 of the 24 patients. The parameters Mean(mt) and Mean(dt) are successful parameters to follow the HRV for patients undergoing CABG surgery. A relation between those SSC parameters and the HRV time-domain and frequency-domain parameters is investigated in this paper to understand the physiological behavior of the patients.

Denoising Underwater Acoustic Signals for Applications in Acoustical Oceanography

A method for denoising underwater acoustic signals used in applications of acoustical oceanography is presented. The method has been introduced for imaging denoising and has been modified to be applied with acoustic signals. The method keeps the energy significant part of the raw signal and reduces the effects of noise by comparing overlapping signal windows and keeping components which resemble true signal energy. It is shown by means of characteristic experiments in connection with a statistical signal characterization scheme based on wavelet transform, that using the statistical features of the wavelet sub-band coefficients of the denoised signal, tomography or geoacoustic inversions lead to a reliable estimation of the parameters of a marine environment.

Deblurring acoustic signals for statistical characterization in applications of ocean acoustic tomography

The signal characterization method suggested by Taroudakis et al. (J.Acoust. Soc. Am. 119, 1396-1405 (2006)) based on the statistics of its 1-D wavelet transform coefficients and successfully applied for inverting acoustic signals in applications of acoustical oceanography has been proven to be sensitive to noise contamination of the signal, but still, it provides good inversion results if an appropriate denoising strategy is applied. In this work, the statistical signal characterization is applied to signals which are both blurred and noise contaminated. Deblurring of the signal is achieved by means of a technique introduced by Taroudaki and O’ Leary (SIAM J. Sci. Comput. 37(6), A2947-A2968 (2015)) for image deblurring, and it is based on a statistical near optimal spectral filtering technique that takes advantage of the singular values of the approximated blurring matrix and the Picard Parameter of the signal that allows for estimation of the additive noise properties and estimation of the error. The study is extended to cases when no accurate knowledge of the blurring mechanism is available. It is shown by typical simulated experimental data that the combination of deblurring and simple denoising strategies provide good results with respect to both signal characterization and subsequent inversions.

A hybrid approach for estimating range-dependent properties of shallow water environments

H hybrid approach based on statistical signal characterization and a linear inversion scheme for the estimation of range dependent sound speed profiles of compact support in shallow water is presented. The approach is appropriate for ocean acoustic tomography when there is a single receiver available, as the first stage of the method is based on the statistical characterization of a single reception using wavelet transform to associate the signal with a set of parameters describing the statistical features of its wavelet sub-band coefficients. A non-linear optimization algorithm is then applied to associate these features with range-dependent sound speed profile in the water column. This inversion method is restricted to cases where the range dependency is of compact support. At the second stage a linear inversion scheme based on modal arrivals identification and a first order perturbation formula to associate sound speed differences with modal travel time perturbations is applied to fine tune the results obtained by the optimization scheme. A second restriction of this stage is that mode identification is necessary. If this assumption is fulfilled the whole scheme may be applied in ocean acoustic tomography for the retrieval of three-dimensional features, combining inversion results at various slices.

Inversion of acoustical data from the "Shallow Water 06" experiment by statistical signal characterization.

This paper presents an application to validate an acoustic signal characterization scheme for ocean acoustic tomography and geoacoustic inversions proposed by Taroudakis, Tzagkarakis, and Tsakalides [J. Acoust. Soc. Am. 119, 1396-1405 (2006)] using data from an experiment at sea. The data were collected during the Shallow water '06 (SW06) experiment off the New Jersey Continental Shelf and the inversion results (sea-bed geoacoustic parameters and source range) are compared with those reported from the same data by Bonnel and Chapman [J. Acoust. Soc. Am. 130(2), EL101-EL107 (2011)]. The comparison and the signal reconstruction using estimated values of the model parameters are satisfactory indicating that the new signal characterization method is useful for practical applications of acoustical oceanography.

Discrimination of Parkinsonian Tremor From Essential Tremor by Voting Between Different EMG Signal Processing Techniques

Parkinson's disease (PD) and essential tremor (ET) are the two most common disorders that cause involuntary muscle shaking movements, or what is called "tremor”. PD is a neurodegenerative disease caused by the loss of dopamine receptors which control and adjust the movement of the body. On the other hand, ET is a neurological movement disorder which also causes tremors and shaking, but it is not related to dopamine receptor loss; it is simply a tremor. The differential diagnosis between these two disorders is sometimes difficult to make clinically because of the similarities of their symptoms; additionally, the available tests are complex and expensive. Thus, the objective of this paper is to discriminate between these two disorders with simpler, cheaper and easier ways by using electromyography (EMG) signal processing techniques. EMG and accelerometer records of 39 patients with PD and 41 with ET were acquired from the Hospital of Kiel University in Germany and divided into a trial group and a test group. Three main techniques were applied: the wavelet-based soft-decision technique, statistical signal characterization (SSC) of the spectrum of the signal, and SSC of the amplitude variation of the Hilbert transform. The first technique resulted in a discrimination efficiency of 80% on the trial set and 85% on the test set. The second technique resulted in an efficiency of 90% on the trial set and 82.5% on the test set. The third technique resulted in an 87.5% efficiency on the trial set and 65.5% efficiency on the test set. Lastly, a final vote was done to finalize the discrimination using these three techniques, and as a result of the vote, accuracies of 92.5%, 85.0% and 88.75% were obtained on the trial data, test data and total data, respectively.

NMR-SSC Magnetic Field Profiler Applied to Magnetic Field Shimming

A new methodology aimed to control the spatial dependence of a magnetic field within a volume of interest is described. The designed hardware uses Nuclear Magnetic Resonance (NMR) to sense the magnetic field. A modified version of the Statistical Signal Characterization (SSC) method was used to analyze the NMR signal, providing a dynamical definition of the characterization parameters. The information is used to control the currents driving a set of coils that introduce corrections in the spatial dependence of the main field generated by an independent unit (magnet or electromagnet). As a practical matter, we deal with the particular case of magnetic field shimming.

Discrimination of Parkinsonian tremor from essential tremor using statistical signal characterization of the spectrum of accelerometer signal

A new technique for discrimination of Parkinson tremor from essential tremor is presented in this paper. This technique is based on Statistical Signal Characterization (SSC) of the spectrum of the accelerometer signal. The data has been recorded for diagnostic purposes in the Department of Neurology of the University of Kiel, Germany. Two sets of data are used. The training set, which consists of 21 essential-tremor (ET) subjects and 19 Parkinson-disease (PD) subjects, is used to obtain the threshold value of the classification factor differentiating between the two subjects. The test data set, which consists of 20 ET and 20 PD subjects, is used to test the technique and evaluate its performance. Three of twelve newly derived SSC parameters show good discrimination results. Specific results of those three parameters on training data and test data are shown in detail. A linear combination of the effects of those parameters on the discrimination results is also included. A total discrimination accuracy of 90% is obtained.

Statistical signal characterization in ocean acoustic tomography and geoacoustic inversions.

A performance study of a statistical characterization of an underwater acoustic signal in relation to geoacoustic inversion or tomography problems is presented. The method of characterization has been presented by Taroudakis et al. [JASA 119, 1396–1405 (2006)] and is based on the use of an appropriate distribution law which describes the statistics of the sub‐band coefficients of the signal wavelet transform. The method has been applied with synthetic data simulating tomographic experiments with low‐frequency sources in shallow environments for the estimation of the water column and bottom properties. In this work the inversion procedure incorporating a genetic algorithm is applied in shallow water environments with simulated noisy data to assess the performance of the method and its limitations under realistic conditions.

Statistical Signal Characterization of Spectral Analysis of Heart Rate Variability for Screening of Patients with Obstructive Sleep Apnea

A new screening technique for obstructive sleep apnea (OSA) is implemented. This technique is based on finding the statistical signal characterization (SSC) parameters of the spectrum of pre-processed R-R-interval (RRI) data. A single classification factor (CR) is selected to classify between patients with obstructive sleep apnea and normal controls. Both nonparametric spectral analysis techniques (with Welch method as an example) and parametric techniques (with Burg method as an example) are used to estimate the power spectral density. The data tested in this work are drawn from MIT database. Both trial and test (challenge) groups of data are used. Each of these two groups contains 20 OSA and 10 normal records. The trial data is used to set the threshold value of the classification factor, which is then used in identifying the challenge data. The total accuracy of the screening is about 93% and 90% using Burg and Welch algorithms respectively.

Classification of modulation signals using statistical signal characterization and artificial neural networks

Modulation recognition systems have to be able to correctly classify the incoming signal modulation scheme in the presence of noise. A new method for classification of analogue and digital modulated signals at low signal-to-noise ratio (SNR) is introduced in this paper. This method uses the statistical signal characterization (SSC) to extract parameters to classify the different modulation signals. The SSC technique produces a set of four numerical parameters for a specific modulated signal. Subsequent comparison of these parameters to those of other waveforms provides the basis for our classification system. The results of SSC technique are applied to an artificial neural network (ANN) to have a robust classification system in the presence of noise down to SNR of 3 dB. No a priori information is required by this technique about the set of input waveforms. The input to the classification system can be analogue or digital signals or a combination of both. The proposed technique shows a 100% efficiency of classification of analogue signals or digital signals at SNR of 7 dB. This classification efficiency reduces to 83% and 86% for analogue or digital signals at SNR of 3 dB. The SSC technique shows better classification results in comparison with other techniques with an important advantage over other methods, which is the simplicity of the neural network needed with this technique due to the small number of features used in the classification.

Statistical signal characterization for congestive heart failure patient's classification

This paper aims at investigating a new technique of time-domain analysis of heart variability (R-R interval (RRI)) for the screening of patients with Congestive Heart Failure (CHF). This method is based on the Statistical Signal Characterization (SSC) of the analytical signal that is generated using Hilbert transformation of the RRI data. The four SSC parameters are: amplitude mean, period mean, amplitude deviation and period deviation. These parameters and their maximum and minimum values are determined over sliding segments of 300-samples, 32-samples and 16-samples for both the instantaneous amplitudes and the instantaneous frequencies derived from the analytical signal of the RRI data. Data used in this work are drawn from MIT database. Threshold values used in the identification of CHF patients from normal records are selected using the Receiver Operating Characteristics (ROC) curves on trial data. This new technique correctly classifies 31/33 of trial data and 65/70 of test data.

Screening of obstructive sleep apnea based on statistical signal characterization of Hilbert transform of RRI data

A new technique of time-domain analysis for screening of Obstructive Sleep Apnea (OSA) using R-R interval (RRI) data is investigated. This method is based on the Statistical Signal Characterization (SSC) of the analytical signal that is generated using Hilbert transformation of the RRI data. The four SSC parameters: amplitude mean, period mean, amplitude deviation and period deviation, and their maximum and minimum values are found over a 5-minutes sliding window for both the instantaneous amplitudes and the instantaneous frequencies derived from the analytical signal of the RRI data. Data used in this work are drawn from both MIT database as well as from the Sleep Laboratory at Sultan Qaboos University (SQU) hospital. Threshold values used in the identification of OSA from normal subjects are selected using the Receiver Operating Characteristics (ROC) curves. The new technique classifies correctly 29/30 of MIT Trial data, 27/30 of MIT challenge data, and 30/30 of SQU data.

Statistical Signal Characterization

Statistical Signal Characterization