United States of America As Represented by the Administrator of Nasa

National Central University

The United States of America As Represented by the Administrator of the

Dynadx Corporation

Adaptive, Intelligent and Dynamic Brain Corporation (aidbrain)

Norden E Huang

Men-Tzung Lo

Yu-Cheng Lin

Bo-Jau Kuo

Zhao-Hua Wu

Xian-Yao Chen

Yi-Chung Chang

Chen Lin

Chung-Kang Peng

Shih-Ann Chen

Wan-Hsin Hsieh

Liming W Salvino

Yenn-Jiang Lin

Chung-Wei Lee

Hui-Yang Huang

Chung-Yi Yang

Hon-Man Liu

Zhaohua Wu

Zheng Shen

Ke-Hsin Hsu

Hung-Yi Lee

The present disclosure provides a non-transitory computer program product embodied in a computer-readable medium and, when executed by one or more analysis modules, providing a visual output for presenting physiological signals of a cardiovascular system. The non-transitory computer program product comprises a first axis representing, subsets of intrinsic mode functions (IMF); a second axis representing a function of signal strength in a time interval; and a plurality of visual elements, each of the visual elements being defined by the first axis and the second axis, and each of the visual elements comprising a plurality of analyzed data units collected over the time interval. Wherein each of the analyzed data units comprises a first coordinate, a second coordinate, and a probability density value generated from an intrinsic probability density function of one of the subsets of IMFs.

Method, module and system for analysis of physiological signal

The present invention provides a diagram building system adapted for processing a signal with a time period. The diagram building system comprises a inputting device for receiving the signal; a computing device, dividing the signal into a plurality of window scales according to one of time interval scales; decomposing the window scales via HHT algorithm to generate a plurality of quantized windows according to different components; then, calculating the value of quantized windows with the same single-frequency component through a quantifying function to generate a plurality of specific frequency values; an outputting device, sequentially arranging the specific frequency values according to the time interval scales and the single-frequency components to form a visual diagram.

Diagram building system and method for a signal data decomposition and analysis

A noninvasive arterial condition detecting system includes a processing device and an electronic stethoscope configured for auscultating carotid artery to receive sound signals. The processing device is connected to the electronic stethoscope. The processing device records the sound signals, preprocesses the sound signal to reduce noise, measures a trough-peak distance and a trough-peak time, and generates an estimate result of arterial conditions according to the trough-peak distance and the trough-peak time. The trough-peak distance and the trough-peak time are related to the health of the carotid artery walls.

Noninvasive arterial condition detecting method, system, and non-transitory computer readable storage medium

An analysis module is provided in the invention. The analysis module is connected with a database containing several data. The analysis module comprises an analysis unit and a database managing system. The analysis unit is developed by a development software to combine HHT algorithm and an automatic data-loading program. The database managing system is integrated in the analysis unit and connected with the database. The data are transferred to the analysis unit by the database managing system, and the analysis unit is used to load the data by the automatic data-loading program and analyzes the data.

Analysis module, cloud analysis system and method thereof

A computer-assisted method for quantitative characterization and treatment of ventricular fibrillation includes preprocessing a time series of an atrial fibrillation signal obtained from a patient, segmenting the time series of the AF signal into activation segments by the computer system, obtaining local activation waveforms (LAW) from the activation segments, determining degrees of similarity between the LAWs, and identifying one or more critical regions in the patient's atria if the LAWs have degrees of similarity exceeding a first threshold value.

System and method for improved complex fractionated electrogram ablation

For multi-dimensional temporal-spatial data, EEMD is applied to time series of each spatial location to obtain IMF-like components of different time scales. All the ith IMF-like components of all the time series of all spatial locations are arranged to obtain ith temporal-spatial multi-dimensional IMF-like component. For two-dimensional spatial data or images, the two-dimensional spatial data or images are consider as a collection of one-dimensional series in first direction along locations in second direction. The same approach to the one used in temporal-spatial data decomposition is used to obtain the resulting two-dimensional IMF-like components. Each of the resulted IMF-like components are taken as the new two-dimensional data for further decomposition, but the data is considered as a collection of one-dimensional series in second-direction along locations in first-direction.

Data decomposition method and computer system therefrom

A degree of nonlinearity based on intra-wave frequency modulation is proposed here with the value substantially between 0 and 1. The degree of nonlinearity is used for obtaining the state rather than a system. The data required for defining the degree of nonlinearity is the state of the motion or the observed data. For a complicate state with more than one IMF containing prominent energy density, the degree of nonlinearity has also considered the amplitude variations. The combination of the intra-wave frequency modulation and the amplitude variation gives the Combined Degree of nonlinearity. With the definitions of degree of nonlinearity, the nonlinearity characteristic can be quantified, and the discussion of nonlinear effects could be conducted more precisely.

Method for quantifying and modeling degree of nonlinearity, combined nonlinearity, and nonstationarity

An apparatus, computer program product and method of analyzing complex signals. Independent versions are generated for the complex signal, e.g., by adding multiple instances of white noise. Intrinsic mode functions (IMFs) are extracted from each of the independent versions, e.g., using Empirical Mode Decomposition (EMD). Corresponding IMFs from each independent version are combined into Ensemble IMFs (EIMFs), e.g., taking the mean of the corresponding IMFs.

Noise-assisted data analysis method, system and program product therefor

An apparatus, computer program product and method of analyzing non-stationary time varying phenomena. A representation of a non-stationary time varying phenomenon is recursively sifted using Empirical Mode Decomposition (EMD) to extract intrinsic mode functions (IMFs). The representation is filtered to extract intrinsic trends by combining a number of IMFs. The intrinsic trend is inherent in the data and identifies an IMF indicating the variability of the phenomena. The trend also may be used to detrend the data.

Analyzing nonstationary financial time series via hilbert-huang transform (HHT)

An apparatus, computer program product and method of analyzing structures. Intrinsic Mode Functions (IMFs) are extracted from the data and the most energetic IMF is selected. A spline is fit to the envelope for the selected IMF. The spline derivative is determined. A stability spectrum is developed by separating the positive and negative results into two different spectra representing stable (positive) and unstable (negative) damping factors. The stability spectrum and the non-linearity indicator are applied to the data to isolate unstable vibrations.

System and method of analyzing vibrations and identifying failure signatures in the vibrations

This invention presents a method for computing Instantaneous Frequency by applying Empirical Mode Decomposition to a signal and using Generalized Zero-Crossing (GZC) and Extrema Sifting. The GZC approach is the most direct, local, and also the most accurate in the mean. Furthermore, this approach will also give a statistical measure of the scattering of the frequency value. For most practical applications, this mean frequency localized down to quarter of a wave period is already a well-accepted result. As this method physically measures the period, or part of it, the values obtained can serve as the best local mean over the period to which it applies. Through Extrema Sifting, instead of the cubic spline fitting, this invention constructs the upper envelope and the lower envelope by connecting local maxima points and local minima points of the signal with straight lines, respectively, when extracting a collection of Intrinsic Mode Functions (IMFs) from a signal under consideration.

Computing frequency by using generalized zero-crossing applied to intrinsic mode functions

This invention presents Normalized Amplitude Hilbert Transform (NAHT) and Normalized Hilbert Transform(NHT), both of which are new methods for computing Instantaneous Frequency. This method is designed specifically to circumvent the limitation set by the Bedorsian and Nuttal Theorems, and to provide a sharp local measure of error when the quadrature and the Hilbert Transform do not agree. Motivation for this method is that straightforward application of the Hilbert Transform followed by taking the derivative of the phase-angle as the Instantaneous Frequency (IF) leads to a common mistake made up to this date. In order to make the Hilbert Transform method work, the data has to obey certain restrictions.

Computing Instantaneous Frequency by normalizing Hilbert Transform

The present invention discloses a computer implemented signal analysis method through the Hilbert-Huang Transformation (HHT) for analyzing acoustical signals, which are assumed to be nonlinear and nonstationary. The Empirical Decomposition Method (EMD) and the Hilbert Spectral Analysis (HSA) are used to obtain the HHT. Essentially, the acoustical signal will be decomposed into the Intrinsic Mode Function Components (IMFs). Once the invention decomposes the acoustic signal into its constituting components, all operations such as analyzing, identifying, and removing unwanted signals can be performed on these components. Upon transforming the IMFs into Hilbert spectrum, the acoustical signal may be compared with other acoustical signals.

Empirical mode decomposition for analyzing acoustical signals

A computer implemented physical signal analysis method includes four basic steps and the associated presentation techniques of the results. The first step is a computer implemented Empirical Mode Decomposition that extracts a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform which produces a Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum. The third step filters the physical signal by combining a subset of the IMFs. In the fourth step, a curve may be fitted to the filtered signal which may not have been possible with the original, unfiltered signal.

Empirical mode decomposition apparatus, method and article of manufacture for analyzing biological signals and performing curve fitting

A computer implemented physical signal analysis method is includes two essential steps and the associated presentation techniques of the results. All the steps exist only in a computer: there are no analytic expressions resulting from the method. The first step is a computer implemented Empirical Mode Decomposition to extract a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals based on local extrema and curvature extrema. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform. The final result is the Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum.

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Bethesda, MD, United States of America

Norden E Huang