CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the priority from Korean Patent Application No. 10-2016-0181374, filed on Dec. 28, 2016, in the Korean Intellectual Property Office, the entire disclosure of which is incorporated herein by reference for all purposes.

BACKGROUND

1. Field

Apparatus and method consistent with example embodiments relate to a bio-signal feature scaling apparatus and method.

2. Description of Related Art

Healthcare technology has been gaining attention due to the rapid entry into an aging society and social problems such as increase in medical expenses. Accordingly, not only medical devices that may be utilized by hospitals and inspection agencies but also portable small-sized medical devices such as wearable devices are being developed. In addition, such portable small-sized medical device may be worn by a user in the form of a wearable device capable of directly measuring cardiovascular health status such as blood pressure or the like, to enable the user to directly measure and manage the user's cardiovascular health status.

Therefore, recently, studies on a method and apparatus to estimate a health status such as a blood pressure by analyzing a bio-signal with a portable small-sized device have been conducted.

SUMMARY

One or more example embodiments provide a bio-signal feature scaling apparatus and method.

According to an aspect of an example embodiment, there is provided a bio-signal feature scaling apparatus including a processor configured to execute instructions to: extract a feature from a bio-signal of an object; estimate a cause of change in a bio-state of the object; calibrate a scale factor based on the estimated cause of change in the bio-state; and scale the extracted feature by applying the calibrated scale factor to the extracted feature.

The bio-signal may include at least one from among an electrocardiogram signal, a photoplethysmogram signal, an electromyogram signal, and a ballistocardiogram signal.

The cause of change in the bio-state may include at least one from among breath-holding, anaerobic exercise, aerobic exercise, and stress.

The processor may be further configured to measure a breathing pattern comprising a respiration rate of the object and estimate the cause of change in the bio-state based on the measured breathing pattern.

The processor may be further configured to the breathing pattern of the object using a respiration sensor attached to the object.

The processor may be further configured to measure the breathing pattern of the object by analyzing the bio-signal.

The processor may be further configured to measure the breathing pattern of the object by analyzing a heart rate or inter beat interval (IBI) obtained from the bio-signal, or analyzing at least one from among a low frequency fluctuation component comprising an amplitude component of the bio-signal.

The processor may be further configured to calibrate the scale factor based on a model that defines a relationship between a cause of change in a bio-state and calibration of a scale factor.

According to another aspect of an example embodiment, there is provided a bio-signal feature scaling method including: extracting a feature from a bio-signal of an object; estimating a cause of change in bio-state of the object; calibrating a scale factor based on the estimated cause of change in the bio-state; and scaling the extracted feature by applying the calibrated scale factor to the extracted feature.

The bio-signal feature scaling method, wherein the bio-signal may include at least one from among an electrocardiogram signal, a photoplethysmogram signal, an electromyogram signal, and a ballistocardiogram signal.

The bio-signal feature scaling method, wherein the cause of change in the bio-state may include at least one from among breath-holding, anaerobic exercise, aerobic exercise, and stress.

The bio-signal feature scaling method, wherein the estimating of the cause of change in the bio-state comprises measuring a breathing pattern comprising a respiration rate of the object and estimating the cause of change in the bio-state based on the measured breathing pattern.

The bio-signal feature scaling method, wherein the measuring of the breathing pattern may include measuring the breathing pattern of the object using a respiration sensor attached to the object.

The bio-signal feature scaling method, wherein the measuring of the breathing pattern may include measuring the breathing pattern of the object by analyzing the bio-signal.

The bio-signal feature scaling method, wherein the measuring of the breathing pattern may include measuring the breathing pattern of the object by analyzing a heart rate or inter beat interval (IBI) obtained from the bio-signal, or analyzing at least one from among a low frequency fluctuation component comprising an amplitude component of the bio-signal.

The bio-signal feature scaling method, wherein the calibrating of the scale factor may include calibrating the scale factor based on a model that defines a relationship between a cause of change in a bio-state and calibration of a scale factor.

According to another aspect of an example embodiment, there is provided a bio-signal feature scaling apparatus including: a processor configured to execute instructions to: extract a plurality of features from a bio-signal of an object; estimate a cause of change in a bio-state of the object; calibrate weights to be applied to the features, respectively, based on the estimated cause of change in the bio-state; combine the plurality of features by applying the calibrated weights to the respective features; calibrate a stored scale factor based on the estimated cause of change in the bio-state; and scale the combined plurality of features by applying the calibrated scale factor to the combined feature.

The bio-signal may include at least one from among an electrocardiogram signal, a photoplethysmogram signal, an electromyogram signal, and a ballistocardiogram signal.

The processor may be further configured to measure a breathing pattern comprising a respiration rate of the object and estimate the cause of change in the bio-state based on the measured breathing pattern.

The processor may be further configured to calibrate the weights to be applied to the features, respectively, using a model that defines a relationship between a cause of change in bio-state and calibration of a combining coefficient.

