TWI467184B - Spectrum analysis method and disease examination method - Google Patents

Description

Spectrum analysis method and disease screening method

The present invention relates to a spectrum analysis method and a disease screening method.

Energy Medicine (EM) is a scientific instrument that uses energy to detect non-invasive diseases in humans, thereby providing patients with a drug or even non-pharmaceutical treatment.

The organism itself emits a unique micro-energy signal, and the micro-energy signal can contain the internal information of the organism, such as whether the human body has a certain disease message. However, intuitively, this micro-energy signal is a kind of messy signal that is difficult to analyze, and there is no spectrum analysis method of energy signal at present, which can analyze the spectrum of the micro-energy, and then apply the analysis to pathological information. Quick screening on the top. In addition, there is no screening method for diseases, which can be based on the microenergy signal of the human body to explore and analyze the possibility and potential of various diseases.

Therefore, how to provide a spectrum analysis method and a disease screening method can analyze the spectrum of the (micro) energy signal, and can apply this spectrum analysis method to the rapid screening of diseases, which has become one of the important topics.

In view of the above problems, an object of the present invention is to provide a spectrum analysis method and a disease screening method capable of analyzing a spectrum of a (micro) energy signal and applying the spectrum analysis method to a rapid screening operation of a disease.

To achieve the above objective, a spectrum analysis method according to the present invention is for analyzing an energy signal, the energy signal having a plurality of first spectrum signals and a plurality of second spectrum signals, and the spectrum analysis method includes: a first step: separately finding each a spectrum of a larger energy of the first spectrum signal and each of the second spectrum signals, and correspondingly establishing a first bandwidth matrix and a second bandwidth matrix thereof according to the spectra of the larger energy, wherein each of the first bandwidth matrix And each of the second bandwidth matrices respectively has a plurality of primary energy frequencies of the first spectral signal and each of the second spectral signals; and a second step: calculating each of the first bandwidth matrix and each of the second bandwidth matrix The energy of the main energy frequencies of a spectrum signal and each of the second spectrum signals; a third step: integrating the first bandwidth matrix and the second bandwidth matrix to respectively establish the first spectrum signals a first integrated bandwidth matrix and a second integrated bandwidth matrix of the second spectral signals; a fourth step: according to the first integrated bandwidth matrix and the second integrated bandwidth matrix, respectively The energy of the same main energy frequency of each of the first spectrum signal and each of the second spectrum signals is accumulated, thereby respectively establishing a first energy spectrum and a second energy spectrum; and a fifth step: according to the first energy spectrum and the second The energy spectrum establishes the main energy spectrum of one of the main energy frequencies of the energy signal.

In an embodiment, in the first step, each of the first bandwidth matrix and each of the second bandwidth matrices respectively have the main energy frequencies and the bandwidths of the first spectrum signals and the second spectrum signals.

In an embodiment, in a third step, the first integrated bandwidth matrix and the second integrated bandwidth matrix respectively integrate the primary energy signals of the first spectral signals and the second spectral signals, and record the The maximum left bandwidth and the maximum right bandwidth of each of the primary energy signals of the first spectral signal and the second spectral signals.

In an embodiment, in the fourth step, the first bandwidth matrix and the second bandwidth matrix are separately integrated according to the first integrated bandwidth matrix and the second integrated bandwidth matrix, respectively, to respectively The first bandwidth matrix and the energy of the same primary energy frequency in the second bandwidth matrix are accumulated.

In an embodiment, in the fifth step, the energy spectrum of the first energy spectrum having the same frequency as the second energy spectrum is removed to establish a main energy spectrum of the main energy frequency of the energy signal.

In an embodiment, in the fifth step, when the main energy spectrum of the main energy frequency of the energy signal is established, the main energy spectrum needs to be corrected by a fixed database.

In an embodiment, when the main energy spectrum of the main energy frequency of the energy signal is established, if a certain main energy frequency in the first energy spectrum cannot find a corresponding frequency on the second energy spectrum, the fixed data is fixed. The bandwidth of the main energy frequency of the library is introduced to replace and correct the bandwidth of the main energy spectrum of the main energy frequency.

In an embodiment, the spectrum analysis method further includes a sixth step of: establishing a first accumulated bandwidth spectrum and a second accumulated bandwidth spectrum according to the first integrated bandwidth matrix and the second integrated bandwidth matrix, respectively.

In an embodiment, in the sixth step, the square of the left bandwidth and the square of the right bandwidth of each main energy frequency of each of the first spectrum signal and each of the second spectrum signals are respectively squared to form a new bandwidth index. And adding the new bandwidth indices of the primary energy signals and the spectrums of the second spectral signals to the first spectrum signals and the second spectrum signals respectively An accumulated bandwidth spectrum and a second accumulated bandwidth spectrum, wherein the first accumulated bandwidth spectrum and the second accumulated bandwidth spectrum respectively have a bandwidth value added by the new bandwidth indicator.

In an embodiment, the spectrum analysis method further comprises a seventh step of: finding a main energy of the second accumulated bandwidth spectrum whose bandwidth value added by the new bandwidth index of the first accumulated bandwidth spectrum is greater than or equal to 2 times The new bandwidth of the frequency is added to the bandwidth value to establish a main energy bandwidth proportional spectrum.

To achieve the above object, a disease screening method according to the present invention is for screening a disease, and the disease screening method includes the spectrum analysis method as described above, and the disease screening method includes: a plurality of diseases and plurals having diseases The normal persons are separately tested to obtain the energy signals of the patients and the normal persons respectively; the energy signals of the patients and the normal persons are respectively analyzed by the above-mentioned spectrum analysis methods to obtain respectively The main energy spectrum of the patients and the normal persons and the spectrum of the main energy bandwidth ratios; respectively, comparing the main energy spectrum of each patient and each normal person with the spectrum of the main energy bandwidth ratio, Finding the main energy spectrum and the main energy bandwidth ratio spectrum separately, only the complex frequencies of the diseases of the patients exist; and only the spectrum exists according to the main energy spectrum and the ratio of the main energy bandwidths The frequencies of the diseases of the patients are calculated as the repetition ratios of the frequencies of the diseases, respectively.

In one embodiment, in the detecting step, one or more days are detected and detected at least once a day.

In an embodiment, the disease screening method further comprises: when a test subject performs the test and analyzes by the above-mentioned spectrum analysis method, the test subject has at least one of the frequencies of the disease, and the frequency is heavy. The higher the proportion, the higher the probability that the subject will get the disease.

In an embodiment, the disease screening method further comprises: when a test subject performs the test and analyzes by the above-mentioned spectrum analysis method, the test subject has at least one of the frequencies of the disease, and the frequency is heavy. The lower the ratio, the lower the probability that the subject will get the disease.

In one embodiment, the disease screening method further comprises: when a subject is detected and analyzed by the above-described spectrum analysis method, and the subject does not have one of the frequencies of the disease, the subject obtains The lower the chance of disease.

In view of the above, the present invention provides a spectrum analysis method for analyzing the spectrum of a (micro) energy signal that is difficult to analyze and disorder, and can eliminate background noise as much as possible (ie, remove the energy spectrum of the background noise). To get the main energy frequency and its energy that can truly represent the energy signal. By the analysis method, the main energy spectrum and the main energy bandwidth ratio spectrum of the main energy frequency of the energy signal can be obtained. In addition, the main energy spectrum and the main energy bandwidth ratio spectrum obtained by the spectrum analysis method of the present invention can be applied to disease screening for rapid screening of human pathological information, and the possibility and potential of various diseases can be performed. Analysis.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a spectrum analysis method and a disease screening method according to a preferred embodiment of the present invention will be described with reference to the accompanying drawings, wherein the same elements will be described with the same reference numerals.

Please refer to FIG. 1, which is a schematic flowchart of a spectrum analysis method according to a preferred embodiment of the present invention. First, the spectrum analysis method of the present invention is used to analyze an energy signal, which can be a weak energy signal, and can be, for example, a micro energy emitted by a living body or a non-living micro energy field. . The energy signal of the embodiment is exemplified by a weak energy signal emitted by a human body (a micro energy signal emitted by a normal person or a person suffering from a certain disease). When the human body's weak energy signal is measured by the instrument, due to environmental noise, an energy signal and a background noise are obtained during the measurement. Here, the hair (which may also be other tissues of the human body) is measured using a photon code subtle energy analyzer (PCSEA). Among them, when the hair is placed on the photon fluctuation energy analyzer without adjusting its resonance knob, the obtained signal is background noise. In addition, when the hair is placed and the resonance knob of the photon fluctuation energy analyzer is adjusted to reach resonance, the measured signal is the energy signal.

In addition, after obtaining the energy signal and the background noise, the measured energy signal and the background noise are separately cut first, for example, one stroke is cut in one second, and 100 times are cut, and 100 pens are obtained. The main energy signal, shown in Figure 2A, is one of them, where the ordinate of Figure 2A is the amplitude (i.e., energy) and the abscissa is time (seconds). Similarly, 100 background noises are also available. Of course, you can take 2 times in 1 second, take 200 times or other cutting methods. Users can take different cutting times and quantities according to their needs. Furthermore, before performing the analysis by the spectrum analysis method of the present invention, the main energy signals are respectively subjected to Fourier Transform to obtain a plurality of first spectrum signals (100 words in total), and FIG. 2B shows one of them. First spectrum signal. Wherein, the ordinate of FIG. 2B is the amplitude (ie, energy), the abscissa is the frequency (Hz), and the frequency range is 1 Hz to 1025 Hz. Of course, in other implementations, the frequency range may be different using different instrument measurements, which is not limited herein. In addition, the background noises are respectively converted by fast Fourier transform to obtain a plurality of second spectrum signals (100 in total, not shown). Then, the Fourier-converted complex first spectrum signal and the complex second spectrum signal are subjected to the analysis work of the present invention.

The spectrum analysis method of the present invention includes the following first step S01 to fifth step S05.

The first step S01 is: separately finding a spectrum of a larger energy of each of the first spectrum signal and each of the second spectrum signals, and respectively establishing a first bandwidth matrix according to the spectrum of the larger energy. BM) and its second bandwidth matrix. In this embodiment, the spectrum of the top 100 energy of each of the first spectrum signals (100 lines in total) and the second spectrum signals (100 lines in total) is respectively found, and a first spectrum signal is used to establish a spectrum. A first bandwidth matrix (a total of 100 first bandwidth matrices) and a second spectral signal establishes a second bandwidth matrix (a total of 100 second bandwidth matrices). Of course, other quantities of larger energy spectrum may be taken, such as the top 50 or the first 200 or other quantities. Each of the first bandwidth matrix and each of the second bandwidth matrix respectively has a complex primary energy frequency of each of the first spectral signal and each of the second spectral signals, that is, the energy of the primary energy frequencies is greater than High (each main energy frequency has its corresponding energy).

Please refer to FIG. 3A, which is a schematic diagram of a spectrum. In order to calculate the bandwidth of a certain frequency, in the maximum amplitude (energy) of the frequency, the difference between the other frequency of the amplitude less than 3 db and the frequency is the bandwidth. For example, in FIG. 3A, the maximum energy of the frequency f is X2, and the frequency of the energy X1 which is 3 db less than the energy X2 is f1, and the frequency difference is f-f1 is the left bandwidth, and the energy X3 is 3 db less than the energy X2. The frequency is f2, and the frequency difference is f2-f, which is the right bandwidth. Here, the spectrum of the first 100 energy is analyzed by the concept of phase noise, and the main energy frequencies of the first spectrum signals and the second spectrum signals are found out. In other words, distinguishing between the first spectrum signal and each of the second spectrum signals, what is the main energy frequency (ie, the greater energy), and what is the noise (ie, the less energy), and establishes the first a bandwidth matrix and each of the second bandwidth matrix, wherein each of the first bandwidth matrix and each of the second bandwidth matrix respectively have the primary energy frequencies of the first spectrum signal and each of the second spectrum signals and their left and right bandwidth.

As shown in Table 1, it is the first bandwidth matrix of a certain first spectrum signal. For example, the frequency of the primary energy of Table 803 is 803 Hz, the left bandwidth is 2 (Hz), and the right bandwidth is 3 (Hz). In addition, a bandwidth value of 0 means that the frequency has no bandwidth, and also represents the energy of the first spectrum signal, and the frequency and its energy do not exist.

Furthermore, the second step S02 is: calculating the energy of the main energy frequencies of the first spectrum signal and each of the second spectrum signals according to each of the first bandwidth matrix and each of the second bandwidth matrix. Here, the energy calculation can be proportional to the sum of the squares of the energies X1, X2, and X3, that is, the energy of the main energy frequencies can be proportional to (X1 ² + X2 ² + X3 ² ). Therefore, according to the formula, the energy of each main energy frequency of each of the first 100 spectral signals and the second spectral signals of each of the 100 pens (not the absolute value of the energy, but a proportional relative value) is calculated, and therefore, the first The spectrum signal can obtain 100 energy spectrum diagrams, as shown in FIG. 3B, which is a schematic diagram of the energy spectrum of a certain first spectrum signal, and the second spectrum signal also obtains 100 energy spectrum diagrams (not shown), wherein The abscissa is the frequency and the ordinate is the intensity of the energy.

In addition, the third step S03 is: separately integrating the first bandwidth matrix and the second bandwidth matrix to respectively establish a first integrated bandwidth matrix of the first spectrum signals and the second spectrum. One of the signals is a second integrated bandwidth matrix. The third step S03 is to integrate the first bandwidth matrix and the second bandwidth matrix respectively, and determine which main energy frequency systems in different pen spectra are determined by using a larger energy as a criterion. Belong to the same set of frequencies. Therefore, the first integrated bandwidth matrix and the second integrated bandwidth matrix can respectively integrate the first spectrum signals and the main energy frequencies of the second spectrum signals, and record the first spectrum signals and the second The maximum left bandwidth and the maximum right bandwidth of each of the main energy frequencies of the spectrum signal.

The fourth step S04 is: accumulating the energy of the same main energy frequency of each of the first spectrum signal and each of the second spectrum signals according to the first integrated bandwidth matrix and the second integrated bandwidth matrix of step S03, respectively A first energy spectrum and a second energy spectrum are respectively established. In this case, the first bandwidth matrix and the second bandwidth matrix are respectively integrated according to the first integrated bandwidth matrix and the second integrated bandwidth matrix, respectively, to respectively use the first bandwidth matrix and the first The energy of the same main energy frequency in the two-bandwidth matrix is accumulated. The first integrated bandwidth matrix and the second integrated bandwidth matrix respectively integrate the first spectrum signals and all the spectrums of the second spectrum signals, and determine which frequencies in the different pen spectrum belong to the same group. Therefore, in step S04, the energy of the same group of frequencies in the first spectrum signals and the second spectrum signals are respectively accumulated to establish the first spectrum signals and the second spectrum signals respectively. The first energy spectrum (shown in Figure 4A) and the second energy spectrum (shown in Figure 4B). Here, the first energy spectrum and the second energy spectrum are respectively referred to as a first spectrum signal and a top 100 energy spectrum (TES) before the second spectrum signal.

In addition, the fifth step S05 is: establishing one main energy of the main energy frequency of the energy signal according to the first energy spectrum (ie, the TES of the first spectrum signal) and the second energy spectrum (ie, the TES of the second spectrum signal) Spectrum. In this case, the energy spectrum of the first energy spectrum having the same frequency as the second energy spectrum is removed (ie, the energy spectrum of the background noise is removed) to establish a main energy spectrum of the main energy frequency of the energy signal. As shown in FIG. 5, it is a schematic diagram of the main energy spectrum of the main energy frequency of the energy signal. Among them, Figure 5 has background noise (excluding the second spectrum signal), that is, only the main energy spectrum of the main energy frequency of the energy signal. In other words, from the Top 100 energy spectrum of the first spectrum signal, the frequency of the main energy frequency simultaneously appearing at the second spectrum signal is picked and removed (ie, the main energy frequency of the second spectrum signal in the TES of the first spectrum signal) The frequency and its energy removal) to establish the main energy spectrum of the main energy frequency of the energy signal.

It is worth noting that in order to make the invention more accurate, another correction step is needed to correct the main energy spectrum of the main energy frequency of the energy signal of the fifth step S05.

Therefore, in the fifth step S05, when the main energy spectrum of the main energy frequency of the energy signal is established, the main energy spectrum needs to be corrected by a fixing database. The reason for the correction is that the error of the measurement may cause the frequency of the main energy to be greater than the bandwidth of the main energy frequency itself (variation exceeds the left and right bandwidths), so that the correct master cannot be found by the concept of phase noise. The energy frequency, so a fixed database is constructed to solve the frequency variation (ie, error) caused by the measurement. This error is due to the frequency at which the frequency of the original noise drifts and becomes the main energy during human operation.

In this embodiment, the method for constructing the fixed database is to obtain a plurality of first spectrum signals of the complex array energy signals by the above method (for example, five groups are used as examples, and of course, other quantities may be used, and each group of energy is used. The signals may have 100 first spectrum signals and 100 second spectrum signals respectively, which are referred to herein as groups R1 to R5, and the respective energy spectra of the groups R1 to R5 are obtained according to the above analysis steps S01 to S05 ( This is referred to herein as TES1 to TES5). Two different energy spectra (TES) are taken from the groups R1 to R5 for comparison, so that 10 pairs of energy spectrum (TES) comparison groups can be formed. For example, TESR1 is compared with TESR2 (or TESR2 is compared with TESR3, etc.), and the frequencies that are not associated with each other in TESR1 and TESR2 are found, that is, only TESR1 or TESR2 exist alone, not the frequencies shared by the two, and the frequencies of these frequencies. The width is due to measurement errors, making it impossible to find the associated frequencies on other energy spectra (TES), observing and recording the bandwidth variation of these frequencies. Here, the bandwidth variation obtained by comparing 10 groups is made into a fixed database at intervals of 100 Hz (that is, the largest one of the left and right bandwidths with the largest error is found), that is, the fixed database has 100 Hz each. The maximum left and right bandwidth of the frequency interval. Therefore, the fixed database can be obtained as shown in Table 2 below.

Then, when the main energy spectrum of the main energy frequency of the energy signal is established, if a certain main energy frequency in the first energy spectrum cannot find a corresponding frequency on the second energy spectrum, the owner of the fixed database will be fixed. The left and right bandwidths of the energy frequency are introduced to replace and correct the bandwidth value of the main energy spectrum of the main energy frequency of the energy signal. In this way, the frequency of the second spectrum signal can be corrected to be shifted into the first spectrum signal due to the measurement error, so that the analysis method of the present invention can be more accurate.

In addition, please refer to FIG. 6, which is another schematic flowchart of the spectrum analysis method of the present invention.

In addition to the first step S01 to the fifth step S05 described above, the spectrum analysis method of the present invention may further include the sixth step S06 and the seventh step S07.

The sixth step S06 is: according to the first integrated bandwidth matrix and the second integrated bandwidth matrix of the third step S03, respectively establishing a first accumulated bandwidth spectrum and a second accumulated bandwidth spectrum (here, accumulating frequency Broadband is called Top100 cumulative bandwidth spectrum, TCBS). In this embodiment, the square of the left bandwidth of each main energy frequency of each of the first spectrum signal and each of the second spectrum signals is respectively added to the square of the right bandwidth to form a new bandwidth index, and respectively In the spectrum of the first spectrum signal and the second spectrum signals, the new bandwidth indices of the main energy frequencies are further added to respectively establish the first spectrum signal and the first accumulated frequency broadband of the second spectrum signals. The spectrum (shown in Figure 7A) and the second accumulated bandwidth spectrum (as shown in Figure 7B). The first accumulated bandwidth spectrum and the second accumulated bandwidth spectrum respectively have bandwidth values added by the new bandwidth indicator.

In addition, the seventh step S07 is: finding the main energy frequency of the second accumulated bandwidth spectrum whose bandwidth value of the first added bandwidth spectrum of the first accumulated bandwidth spectrum of step S06 is greater than or equal to 2 times. The bandwidth value of the new bandwidth index is added to establish a bandwidth ratio spectrum. In other words, the bandwidth value of the new bandwidth index added in the first spectrum signal is found to be more than 2 times (inclusive) the bandwidth value of the new bandwidth index of the second accumulated bandwidth spectrum. After the bandwidth value, the main energy bandwidth ratio spectrum of the energy signal is established, and the result is shown in FIG. 8.

The spectrum analysis method of the present invention is for analyzing an energy signal (especially a micro energy signal) and excluding its background noise as much as possible (that is, removing the energy spectrum of the background noise) to obtain a truly representative The main energy frequency of the energy signal and its energy. After the analysis method, the main energy spectrum of the fifth step S05 is obtained (as shown in FIG. 5, which may be more accurate after being corrected by the fixed database described above) and the main energy bandwidth ratio of the seventh step S07. The spectrum (as shown in Fig. 8), and the above spectrum analysis method and the obtained main energy spectrum and main energy bandwidth ratio spectrum are applied to the following disease screening methods for rapid screening of pathological information.

Please refer to FIG. 9, which is a schematic flow chart of a disease screening method of the present invention.

The disease screening method of the present invention provides a rapid screening of a disease in a non-invasive manner, providing a possible proportion of whether or not the subject is getting the disease. First, the disease screening method of the present invention can analyze the energy signal (micro energy signal) possessed by the living body by using the above spectrum analysis method.

The disease screening method of the present invention includes the following steps P01 to P04.

Step P01 is: detecting a plurality of patients having a certain disease and a plurality of normal persons to obtain the energy signals of the patients and the normal persons respectively. Among them, one or more days can be detected separately, and at least once a day to obtain a micro energy signal of a plurality of patients and a plurality of normal persons in one day or a plurality of days. In the present embodiment, the disease to be screened is exemplified by diabetes (here is merely an example, and of course, other diseases such as liver disease, hypertension, etc.) can be screened by the method. In addition, 10 micro-energy signals (20 people) were detected in 10 diabetic patients and 10 normal people, respectively, and tested once a day for a total of three days, so a total of 60 micro-energy signals (30 normal human micro-energy) were obtained. Signal, 30 micro-energy signals of diabetic patients, and each micro-energy signal can have a plurality of first spectrum signals and a plurality of second spectrum signals (background noise). The number of people, the number of days, and the number of tests per day are all examples. Of course, different numbers of people, different days, and different number of tests per day can be used. Among them, the more the number of people, the number of days and the number of tests (ie, the more the number of samples), the more accurate the subsequent disease screening results will be.

In addition, the step P02 is to analyze the energy signals of the patients and the normal persons by using the above-mentioned spectrum analysis method to obtain the main energy spectrums of the patients and the normal persons, respectively. The main energy bandwidth ratio spectrum. Here, the spectrum analysis method described above is used, and 60 main energy spectra and 60 main energy bandwidth ratio spectra are obtained.

In addition, the step P03 is: respectively comparing the main energy spectrum and the main energy bandwidth ratio spectrum of each patient and each normal person to respectively find the main energy spectrum and the main energy bandwidth ratio spectrum. There are only multiple frequencies of the disease in these patients. In this embodiment, the frequency of one day of detection (ie, the main energy spectrum and the main energy bandwidth ratio spectrum) is used to find out the frequency of a diabetic patient and a normal person only present in the diabetic patient, and repeat In this three-day step, the frequency of occurrence of diabetes patients for more than two days is found out from these three days, and a table showing the frequency of occurrence of the main energy spectrum of diabetic patients is made. Here, the frequency tables shown in Table 3 below and Table 4 below can be obtained. Therefore, 20 people can be divided into 10 groups, and a total of 10 tables in Table 3 and 10 tables in Table 4 can be obtained. Among them, the figures appearing in Tables 3 and 4 represent the frequency of the main energy spectrum of more than two days in the (micro) energy signal of patients with diabetes in the three-day test data (indicating that it may be specific to diabetic patients) Frequency of).

In addition, step P04 is: calculating the repetition ratio of each frequency of the disease according to the main energy spectrum and the frequencies of the diseases in which only the patients are present in the main energy bandwidth spectrum. In this embodiment, the percentage of the repetition rate of the main energy frequency (the main energy frequency of the diabetes) is calculated from the table of the above 10 Table 3 and the table 10 of the 10 Table 4, and Make the following table five. Among them, the percentage of repeated proportions in Table 5 represents the proportion of this frequency appearing in all the data in Tables 3 and 4 above. For example, the repetition rate of 8Hz appears to be 20%, which represents the frequency of all the three tables and four, the ratio of occurrence in 100 times is 20, and so on.

In addition, the disease screening method may further include: when a test subject performs the test and analyzes by the above-mentioned spectrum analysis method, the test subject has at least one of the frequencies of diabetes (ie, the frequencies listed in Table 5). One, and the higher the repetition ratio of the frequency, the higher the probability that the subject gets diabetes.

For example, if a subject is subjected to the above-described spectrum analysis method and the frequency of the main energy spectrum and the main energy bandwidth ratio spectrum is 1073 Hz (the repetition ratio is 60%), the subject receives diabetes. The chances are quite high (possibly 60%). Therefore, the testee needs to further carry out other traditional detection methods to further clarify whether or not there is diabetes.

Conversely, the disease screening method may further include: when a test subject performs the test and analyzes by the above-mentioned spectrum analysis method, the test subject has at least one of the frequencies of diabetes, and the repetition ratio of the frequency The lower the rate, the lower the chance that the subject will get the disease. For example, the frequency of 125 Hz in Table 5 is only 10%, indicating that the probability of getting diabetes in the testee is low (lower means no diabetes).

In addition, the disease screening method may further include: when a test subject performs the test and analyzes by the above-mentioned spectrum analysis method, and the test subject does not have one of the frequencies of diabetes, the probability of the testee getting diabetes It is also quite low (previously it can be judged that there is no diabetes).

In summary, the present invention provides a spectrum analysis method for analyzing the spectrum of a (micro) energy signal that is difficult to analyze and disorder, and can eliminate background noise as much as possible (ie, removing the energy spectrum of the background noise). To get the main energy frequency and its energy that can truly represent the energy signal. By the analysis method, the main energy spectrum and the main energy bandwidth ratio spectrum of the main energy frequency of the energy signal can be obtained. In addition, the main energy spectrum and the main energy bandwidth ratio spectrum obtained by the spectrum analysis method of the present invention can be applied to disease screening for rapid screening of human pathological information, and the possibility and potential of various diseases can be performed. Analysis.

The above is intended to be illustrative only and not limiting. Any equivalent modifications or alterations to the spirit and scope of the invention are intended to be included in the scope of the appended claims.

S01~S07, P01~P04‧‧‧ steps

1 is a schematic flowchart of a spectrum analysis method according to a preferred embodiment of the present invention; FIG. 2A is a schematic diagram of a main energy signal; FIG. 2B is a schematic diagram of a first spectrum signal; FIG. 3A is a schematic diagram of a spectrum; Schematic diagram of the energy spectrum of the spectrum signal; FIG. 4A and FIG. 4B are respectively a schematic diagram of the first energy spectrum and a second energy spectrum; FIG. 5 is a schematic diagram of the main energy spectrum of the main energy frequency of the energy signal; FIG. 6 is a spectrum analysis method of the present invention; FIG. 7A and FIG. 7B are respectively a schematic diagram of a first accumulated bandwidth spectrum and a second accumulated bandwidth spectrum; FIG. 8 is a schematic diagram of a main energy bandwidth proportional spectrum of an energy signal; and FIG. 9 is a schematic diagram of the present invention A schematic diagram of a process for a disease screening method.

S01～S05. . . step

Claims (15)

A spectrum analysis method for analyzing an energy signal having a plurality of first spectrum signals and a plurality of second spectrum signals, the spectrum analysis method comprising: a first step: separately finding each of the first spectrum signals and a spectrum of a larger energy of each of the second spectral signals, and correspondingly establishing a first bandwidth matrix and a second bandwidth matrix thereof according to the spectra of the larger energy, wherein each of the first bandwidth matrix and each of the first bandwidth matrix The second bandwidth matrix has a complex primary energy frequency of each of the first spectral signal and each of the second spectral signals; and a second step: calculating respectively according to each of the first bandwidth matrix and each of the second bandwidth matrix The energy of the primary energy signals of the first spectral signal and each of the second spectral signals, wherein the energy of the primary energy frequencies is proportional to the sum of the squares of the energy X1, X2, and X3, respectively, and X2 is the first frequency spectrum. The maximum energy value of the signal or each of the second spectral signals, and X1 and X3 are respectively smaller than X2 by a specific value; a third step: respectively integrating the first bandwidth matrix and the second bandwidth matrix, Establishing a first integrated bandwidth matrix of the first spectrum signals and a second integrated bandwidth matrix of the second spectrum signals respectively; a fourth step: according to the first integrated bandwidth matrix and the second integration a bandwidth matrix for accumulating the energy of the same main energy frequency of each of the first spectrum signal and each of the second spectrum signals, and further dividing Do not establish a first energy spectrum and a second energy spectrum; and a fifth step: establishing a primary energy spectrum of the primary energy frequency of the energy signal according to the first energy spectrum and the second energy spectrum. The spectrum analysis method of claim 1, wherein in the first step, each of the first bandwidth matrix and each of the second bandwidth matrix has each of the first spectrum signals and each of the second The main energy frequencies of the spectrum signal and their bandwidth. The spectrum analysis method of claim 1, wherein in the third step, the first integrated bandwidth matrix and the second integrated bandwidth matrix respectively integrate the first spectrum signals and the first The main energy frequencies of the two spectral signals, and recording the maximum left bandwidth and the maximum right bandwidth of the primary energy signals and the primary energy frequencies of the second spectral signals. The spectrum analysis method of claim 1, wherein in the fourth step, the first bandwidth matrix is integrated according to the first integrated bandwidth matrix and the second integrated bandwidth matrix, respectively. And a second bandwidth matrix to accumulate the energy of the same primary energy frequency in the first bandwidth matrix and the second bandwidth matrix, respectively. The spectrum analysis method of claim 1, wherein in the fifth step, the energy spectrum of the first energy spectrum having the same frequency as the second energy spectrum is removed to establish the energy signal. The main energy spectrum of the main energy frequency. The spectrum analysis method according to claim 1, wherein in the fifth step, when the main energy frequency of the energy signal is established In the main energy spectrum, the main energy spectrum needs to be corrected with a fixed database. The spectrum analysis method according to claim 6, wherein when the main energy spectrum of the main energy frequency of the energy signal is established, if a main energy frequency in the first energy spectrum is in the second energy spectrum When the corresponding frequency is not found, the bandwidth of the main energy frequency of the fixed database is introduced to replace and correct the bandwidth of the main energy spectrum of the main energy frequency. The method for spectrum analysis according to claim 1, further comprising: a sixth step: respectively establishing a first accumulated bandwidth spectrum and a first integrated bandwidth matrix and the second integrated bandwidth matrix The second accumulated bandwidth spectrum. The method of spectrum analysis according to claim 8, wherein in the sixth step, the square of the left bandwidth of each of the first spectrum signal and each of the second spectrum signals is respectively added to the right The bandwidth is squared to form a new bandwidth index, and the new bandwidth indices of the primary energy signals and the spectrums of the second spectral signals are respectively added to each other to establish the first a first accumulated bandwidth spectrum of the spectral signal and the second spectral signals and the second accumulated bandwidth spectrum, wherein the first accumulated bandwidth spectrum and the second accumulated bandwidth spectrum respectively have the new bandwidth The bandwidth value to which the metrics are added. The method for spectrum analysis according to claim 8 of the patent application, further comprising: a seventh step: finding that the added bandwidth value of the new bandwidth indicator of the first accumulated bandwidth spectrum is greater than or equal to 2 times Second accumulated bandwidth The bandwidth value of the new bandwidth index of the main energy frequency of the spectrum is added to establish a main energy bandwidth proportional spectrum. A disease screening method for screening a disease, the disease screening method comprising the method of spectrum analysis according to any one of claims 1 to 10, wherein the disease screening method comprises: The multiple patients and the normal persons of the disease are separately tested to obtain the energy signals of the patients and the normal persons respectively; the spectrum analysis method is used to analyze the patients and the normal persons respectively. Equal energy signals to obtain the main energy spectrum of the patients and the normal persons and the spectrum of the main energy bandwidth ratios; respectively, respectively comparing the main energy spectrum of each of the patients and each of the normal persons And each of the main energy bandwidth ratio spectrums to separately find the main energy spectrum and the main energy bandwidth ratio spectrum, only the complex frequencies of the diseases of the patients are present; and according to the main energy spectrum And the frequencies of the diseases in the main energy bandwidth ratio spectrum are only present in the patients, and the repetition ratios of the frequencies of the diseases are respectively calculated. The disease screening method according to claim 11, wherein in the detecting step, detecting one day or multiple days, and detecting at least once a day. The method for screening diseases according to claim 11 of the patent application further includes: when a test subject performs the test, and analyzes by the spectrum analysis method Thereafter, the subject has at least one of the frequencies of the disease, and the higher the repetition ratio of the frequency, the higher the probability that the subject gets the disease. The method for screening a disease according to claim 11, further comprising: when a subject is tested and analyzed by the spectrum analysis method, the subject has at least one of the frequencies of the disease. First, the lower the repetition ratio of the frequency, the lower the probability that the subject will get the disease. The method for screening a disease according to claim 11 further includes: when a test subject performs the test and analyzes by the spectrum analysis method, the test subject does not have one of the frequencies of the disease. The lower the chance that the subject will get the disease.