Method, module and system for analysis of physiological signal

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.

Description

CROSS-REFERENCE TO RELATED APPLICATION[0001]The present disclosure claims the benefit of U.S. provisional patent application No. 62 / 596,912, filed on Dec. 11, 2017, the entirety of which is incorporated herein by reference.FIELD[0002]The present disclosure is generally related to the method, module and system for analysis of physiological signal. More particularly, the present disclosure is directed to a method, module and system for analysis of electrical activities of the cardiovascular system.BACKGROUND[0003]Physiological signals provide valuable information for evaluation, diagnosis, or even prediction of physical conditions of a living organism. Each type of physiological signals obtained from a living organism represents the status of a particular system of the living organism.[0004]Various physiological signals can be obtained from a living organism, including but not limited to: electrocardiography (EKG) signals, electromyography (EMG) signals, electroretinography (ERG) sign...

AI Technical Summary

Benefits of technology

This patent is about creating visual forms for electrocardiography, electromyography, and blood pressure signals. The goal is to help medical professionals better analyze and understand the patterns of these signals. The patent also aims to provide visual representation of abnormal conditions in these signals, as well as comparing different groups of subjects or time intervals.

Problems solved by technology

However, in frequencies or wave characteristics shown in the graph, noise or disturbances are considered as irrelevant information when conducting analysis of acquired metrics.

The non-stationary and non-linear nature of many physiological wave signals pose significant obstacles for signal processing.

Conventional approaches for signal processing of physiological wave signals have failed to provide an effective solution to the obstacles.

For instance, Fourier transformation are often used to interpret linear and stationary wave signals, such as spectrum analysis; however, due to its mathematical nature and probability distribution, Fourier transformation is unable to provide meaningful visualization results from non-stationary and non-linear wave signals.

Conventional probability distribution function is unable to provide insights from non-stationary and non-linear wave signals.

However, the application of HOSA on analysis of EKG signals has never been explored and exploited.

Due to the lack of adequate signal processing tools, data associated with acquired physiological signals often need to be analyzed by trained professionals, in addition to available algorithms or software embedded instruments.

Physiological measurement data could be massive in terms of their quantity and complexity.

The complexity and amount of the acquired 24-hour EKG data are overwhelming even for well-trained professionals, therefore increasing the chances of missed detection or misinterpretation of EKG deviation or abnormal EKG signals.

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