Beth Israel Deaconess Medical Center, Inc.

National Central University

Chung-Kang Peng

Ary L Goldberger

Robert Joseph Thomas

Joseph E Mietus

Madalena D Costa

Yu-Cheng Lin

Norden E Huang

Men-Tzung Lo

Bo-Jau Kuo

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

An assessment of sleep quality and sleep disordered breathing is determined from the cardiopulmonary coupling between two physiological data series. In an embodiment, an R-R interval series is derived from an electrocardiogram (ECG) signal. The normal beats from the R-R interval series are extracted to produce a normal-to-normal (NN) interval series. The amplitude variations in the QRS complex are used to extract to a surrogate respiration signal (i.e., ECG-derived respiration (EDR)) that is associated with the NN interval series. The two series are corrected to remove outliers, and resampled. The cross-spectral power and coherence of the two resampled signals are calculated over a plurality of coherence windows. For each coherence window, the product of the coherence and cross-spectral power is used to calculate coherent cross power. Using the appropriate thresholds for the coherent cross power, the proportion of sleep spent in CAP, non-CAP, and wake and/or REM are determined.

Assessment of sleep quality and sleep disordered breathing based on cardiopulmonary coupling

An assessment of sleep quality and sleep disordered breathing is determined from cardiopulmonary coupling between two physiological data series. An R-R interval series is derived from an electrocardiogram (ECG) signal. The normal beats from the R-R interval series are extracted to produce a normal-to-normal interval series. The amplitude variations in the QRS complex are used to extract a surrogate respiration signal (i.e., ECG-derived respiration) associated with the NN interval series. The two series are corrected to remove outliers, and resampled. The cross-spectral power and coherence of the two resampled signals are calculated over a plurality of coherence windows. For each coherence window, the product of the coherence and cross-spectral power is used to calculate coherent cross-power. Using the appropriate thresholds for the coherent cross-power, the proportion of sleep spent in CAP, non-CAP, and wake and/or REM are determined. Coherent cross-power can be applied to differentiate obstructive from non-obstructive disease, and admixtures of the same.

System and method for assessing sleep quality

In a subject undergoing therapeutic intervention, efficacy of the therapeutic intervention is assessed based on a series of physiologic data associated with the subject. The series of physiologic data is analyzed to produce a measure of complexity. The complexity measure is then compared to a control. The efficacy of the therapeutic intervention is assessed based on the comparison of the complexity measure to the control. The control may be, for example, a complexity measure taken prior to initiation of the therapeutic intervention, a complexity measure taken from a different subject, or a predetermined threshold value. The measure of complexity is generated using, for example, a multiscale entropy measurement (MSE), a time asymmetry measurement, and/or an information-based similarity measurement. An increase in complexity indicates a positive effect of the therapeutic intervention, while a decrease in complexity indicates a negative effect of the therapeutic intervention. Stability of a non-biologic network, such as a computer network, communications network or transportation network can also be assessed.

Complexity-based dynamical analysis of a network

In a subject undergoing therapeutic intervention, efficacy of the therapeutic intervention is assessed based on a series of physiologic data associated with the subject. The series of physiologic data is analyzed to produce a measure of complexity. The complexity measure is then compared to a control. The efficacy of the therapeutic intervention is assessed based on the comparison of the complexity measure to the control. The control may be, for example, a complexity measure taken prior to initiation of the therapeutic intervention, a complexity measure taken from a different subject, or a predetermined threshold value. The measure of complexity is generated using, for example, a multiscale entropy measurement (MSE), a time asymmetry measurement, and/or an information-based similarity measurement. An increase in complexity indicates a positive effect of the therapeutic intervention, while a decrease in complexity indicates a negative effect of the therapeutic intervention.

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Sharon, MA, United States of America

Chung-Kang Peng