Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Electrical signal parameter measurement system
Reexamination Certificate
1999-12-16
2003-06-03
Nghiem, Michael (Department: 2863)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Electrical signal parameter measurement system
C324S076190, C324S076280, C382S170000, C600S509000, C702S077000
Reexamination Certificate
active
06574573
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to a spectrum analysis and a display method of time series data derived from a series of signal issued from a living organism, wherein: in the spectrum analysis method, the series of signals issued from the living organism are sampled in amplitude at predetermined time intervals and converted into digital form through an analog-to-digital conversion processing and the like to obtain a time series data, which data thus obtained is then decomposed into a plurality of frequency components each of which is determined in amplitude; and, in the display method, variations in amplitude of the frequency components of the time series data are transformed into corresponding densities of a predetermined color including a gray color according to a predetermined color scale or a predetermined gray (i.e., white-to-black) scale and, and displayed on a display screen on the basis of a time base of the display screen.
2. Description of the Related Art
Heretofore, in most cases, a conventional spectrum analysis method has been performed through the “fast Fourier transformation (i.e., FTT)” processing. Also used is a so-called MEM (i.e., Maximum Entropy Method), which makes it possible to perform a necessary spectrum analysis of time series data even when the time series data is based on a series of signals issued for a relatively short period of time. These spectrum analyses of the time series data are performed at regular intervals of time to obtain necessary results, which are displayed in a three-dimensional data diagram on a display screen.
However, in case that information of the living organism is processed through the fast Fourier transformation processing: the larger the frequency resolution, the slower the spectrum analysis of the time series data will be performed through the fast Fourier transformation processing. Consequently, in case that the fast Fourier transformation processing is used to deal with the time series data varying in a relatively short period of time, when a period of time required to perform such spectrum analysis is reduced, variations in amplitude of the frequency components of the time series data become hard to detect. On the other hand, when the period of time required to perform the spectrum analysis of the time series data is increased to improve, in frequency resolution, the three-dimensional data diagram showing the frequency components of the time series data, variations in amplitude of the frequency components of the time series data become hard to detect.
Further, due to the dispersion in spectrum of the time series data caused by discontinuity appearing in each of the opposite ends of an analysis region characteristic of the Fourier transformation processing, it is hard for the Fourier transformation processing to detect both of: variations in amplitude of the frequency components of the time series data, which variations appear in a relatively short period of time; and, relatively small variations in frequency of the frequency components of the time series data. This is one of drawbacks of the Fourier transformation processing in dealing with the time series data.
In most cases, the signal issued form the living organism varies in a relatively short period of time. Under such circumstances, it is very important to grasp the entire variations in mode of the signal occurring in such a short period of time. However, heretofore, the Fourier transformation processing has been used to grasp variations in frequency of the signal, and, therefore suffered from its relatively long period of analysis time, which makes it impossible to detect the variations in mode of the signal appearing in such a short period of time.
For example, in the case of an electrocardiogram with a frequency resolution of 1 cpm (i.e., cycle per minute), only a result obtained in spectrum analysis of the time series data is an average value (i.e., mean information) of a series of signals issued from a patient's heart for one minute. However, in general, an abnormal irregular rhythm called “arrhythmia” of the patient's heartbeats has a few seconds' duration. Consequently, variations in amplitude of frequency resulted from the Fourier transformation processing of such “arrhythmia” are extremely small, which makes it impossible to grasp the entire activity mode of the patient's heart.
On the other hand, in the case of the above-mentioned MEM (i.e., Maximum Entropy Method), a value of amplitude in frequency of the time series data obtained through the MEM is not stable and therefore not reliable. Due to this, there is not recognized any relationship between the distribution in spectrum of the frequency components of the time series data and the time series data itself. Consequently, the MEM is not used in preparation of the electrocardiogram. In the case of the electrocardiogram and like diagrams having been already known in waveform in its meaning in detail, it is considered inadequate to employ the spectrum analysis which is not reliable in amplitude of a weak one of the frequency components of the time series data.
Further, in general, the results of the spectrum analyses periodically performed are represented by using a three-dimensional diagram. However, such a three-dimensional diagram suffers from the disadvantage that the amplitude of the weak one of the frequency components of the time series data is often hidden by a larger amplitude of another frequency component disposed in front of the weak frequency component in the diagram, which poses another disadvantage that it is hard to grasp the entire variations in mode of the frequency components of the time series data varying with elapsed time. Further, it is necessary for a desired display method to enable a doctor to grasp the entire variations in mode of the frequency components of the time series data in a relatively short time and also grasp the amplitude of the weak frequency component of the time series data even when a conventional type of spectrum analysis method is performed to display its results on the display screen by using a color map or diagram.
As described above, the fast Fourier transformation (i.e., FFT) processing and the MEM (i.e., Maximum Entropy Method) both having heretofore been used in the spectrum analysis of the time series data suffer from various problems in dealing with the signals issued from the living organism and like signals varying in a relatively short period of time.
SUMMARY OF THE INVENTION
Consequently, it is an object of the present invention to provide a spectrum analysis method and a display method of time series data derived from a series of signals issued from a living organism, which enables a doctor to precisely grasp variations in mode of frequency components of the time series data and also grasp variations in amplitude of even a weak one of the frequency component of the time series data.
In accordance with a first aspect of the present invention, the above object of the present invention is accomplished by providing:
A spectrum analysis method of time series data derived from a series of signals issued from a living organism, the signals being converted into digital form of the time series data, comprising:
a step for decomposing the time series data into a plurality of frequency components by means of a set of band pass filters, wherein the band pass filters have their cut-off frequencies shifted or offset from each other by their passing band widths, respectively, so that amplitude information of the frequency components of the time series data is obtained.
In accordance with a second aspect of the present invention, the above object of the present invention is accomplished by providing:
A display method of time series data derived from a series of signals issued from a living organism, the signals being converted into digital form of the time series data, comprising the steps of:
decomposing the time series data into a plurality of frequency components by means of a set of ban
Gram Corporation
Le John
Nghiem Michael
Schmeiser Olsen & Watts
LandOfFree
Spectrum analysis and display method time series does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Spectrum analysis and display method time series, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Spectrum analysis and display method time series will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3096963