Abstract: 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.

Abstract: The present disclosure provides a system for analyzing electrical activities of at least one brain. The system comprises a visual output module for rendering a visual output space according to analyzed data sets generated by an analysis module, and displaying a visual output. The visual output comprises a first axis representing FM, a second axis representing AM, and a plurality of visual elements defined by the first axis and the second axis. Each of the visual elements comprises an accumulated signal strength and the analyzed data sets. Each of the analyzed data sets comprises a plurality of analyzed data units collected over a time period.

Abstract: A functional network analysis system for complex network provided by the present application, includea multi-signal-source measurement unit, a signal decomposition unitand a cross-frequency coupling analysis unit, wherein the multi-signal-source measurement unit, the signal decomposition unitand the cross-frequency coupling analysis unit are connected successively. A method using the functional network analysis system is more suitable for the decomposition of non-linear and non-stationary data in complex networks, and can reflect the dynamic properties of functional network links between different frequency bands in a complex system.

Abstract: The present disclosure provides a system for analyzing physiological signals. The system comprises a visual output module for rendering a visual output space according to a plurality of analyzed data sets generated by a analysis module, and displaying a visual output, wherein the visual output comprises a first axis representing frequency modulation (FM), a second axis representing amplitude modulation (AM), and a plurality of visual element defined by the first axis and the second axis, and each of the visual elements comprises an accumulated signal strength and the analyzed data sets.

Abstract: 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.

Abstract: The present disclosure provides a non-transitory computer program product embodied in a computer-readable medium, and when executed by one or more analysis module, 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 (IMFs); 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.

Abstract: 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.

Abstract: The invention provides a method for data analyzing. The method comprises receiving a data, and decomposing the data into a plurality of intrinsic mode functions (IMFs) by utilizing an empirical mode decomposition (EMD) method, wherein the IMFs are a value changes over time of the data in different frequencies. The method further comprises obtaining a plurality of probability density functions based on accumulating the distribution of each IMF according to a longest mean time scale, and generating an intrinsic probability distribution function (iPDF) component spectrum, wherein the iPDF component spectrum comprises the distribution of probability density functions between a frequency dimensional and a standard deviation dimensional. This invention result of the method can be used as a diagnosis tool implementing in a system.

Abstract: The invention provides a method for electric brain stimulator. In the beginning, obtaining a brain functional amplitude modulation spectrum, wherein the brain functional amplitude modulation spectrum is a relationship between carrier frequency and amplitude-frequency on different brain sites. Then selecting a first alternating current frequency, wherein the first alternating current frequency is determined by the amplitude-frequency in which the brain functional amplitude modulation spectrum display a maximum power relation value, or a maximum correlation value with any behavior index of behavior and cognitive functions. And selecting a second alternating current frequency, wherein the second alternating current frequency is determined by the carrier frequency in which the brain functional amplitude modulation spectrum display a maximum power relation value, or a maximum correlation value with any behavior index of behavior and cognitive functions.

Abstract: The invention discloses a method and a system for quickly and directly processing an original signal into a plurality of mode functions, the steps comprises: decomposing the original signal by Empirical Mode Decomposition (EMD) method to choose a first intrinsic mode functions (IMFs). Then, adding the plurality of level n conjugate masking functions, which are selected from a group of sinusoidal functions comprising the mean amplitude and the mean frequency of the first IMF, to the original signal individually to obtain level n mode functions, until the level n mode function is a monotonic function, wherein the plurality of mode functions are the IMFs between each frequency regions of the original signal. The invention not only includes the advantage of EMD analyzing, but also excludes the problem of mode mixing phenomenon which is caused by the intermittent disturbance.

Abstract: The present invention provides a method for identifying images of brain function. In the beginning, choosing one of the brain data collected by multichannel scalp EEG/MEG, and using a mode decomposition method to obtain a plurality of intrinsic mode functions for each brain data, transforming the intrinsic mode functions (IMFs) in the same frequency scale into a plurality of source IMFs across the cerebral cortex by a source reconstruction algorithm, and classifying each source IMF in the same frequency scale into a plurality of frequency regions corresponding to the different brain sites. Then, repeatedly choosing a source IMF, and obtaining an amplitude envelope line through each absolution value of the source IMF. Further to obtain a plurality of source first-layer amplitude IMFs decomposed from the function of the amplitude envelope line by the mode decomposition method.

Abstract: 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.

Abstract: The present invention provides a method of implementing the high dimensional Holo-Hilbert spectral analysis which transforms a data from time domain to frequency domain. At the first of the steps, obtaining an amplitude intrinsic mode component and an instantaneous frequency component of the data by a mode decomposition, such as using Empirical Mode Decomposition (EMD), adaptive filtering, or optimal basis pursue, etc to show a plurality of amplitude intrinsic mode functions (amplitude IMFs) and a plurality of frequency intrinsic mode functions (frequency IMFs). Then, analyzing each of the amplitude IMFs and the frequency IMFs to obtain a plurality value in different high order components. At the last, to establish a high dimensional Holo-Hilbert spectrum by combining the high order component with the original component to show the interaction between frequency and amplitude.

Abstract: This invention discloses a multi-scales intrinsic entropy analysis method that can quantify the entropies on difference time scales for a complex time series. The implementation of the method decomposes a complex time series into a plurality of intrinsic mode functions by a nonlinear signal processing algorithm, such as the method of empirical mode decomposition. Then, the entropy increments can be calculated on multiple coarse-graining scales when an intrinsic mode functions is added into the reconstructed time series analyzed by the method of multi-scale entropy. The entropy increment is significant on a specific coarse-graining scale, which corresponds to the averaged period of the intrinsic mode functions. The entropy increment on the specific coarse-graining scale is defined as the intrinsic entropy for an intrinsic mode functions. Multiple intrinsic entropies represent the entropy properties for a complex time series on their corresponding time scales.

Abstract: The present invention provides an analysis system adapted for processing a signal with a time period. The analysis system comprises a segmenting unit, an analyzing unit, processing unit and an outputting unit. The segmenting unit divides the signal into a plurality of scale windows according to one of interval scales. The analyzing unit processes the scale windows via HHT algorithm to make each scale window generate a plurality of quantized windows according to different components. The processing unit reorganizes the quantized windows make each scale window generate a plurality of quantized windows according to different components. The outputting unit accumulates a plurality of specific frequency values in difference interval scales and combines the specific frequency values to form a three-dimensional variation visual diagram.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.