WO2002045581A1

WO2002045581A1 - Relation between breast cancer and estrogen by body surface real-time signal

Info

Publication number: WO2002045581A1
Application number: PCT/JP2001/010253
Authority: WO
Inventors: Michiko Yagi; Zenro Nihei; Takayuki Osanai; Yukiko Koike
Original assignee: Kabushiki Kaisya Advance
Priority date: 2000-11-22
Filing date: 2001-11-22
Publication date: 2002-06-13
Also published as: JPWO2002045581A1

Abstract

Information on presence of a cancer is obtained by producing time-series signals for a predetermined period of the signals of a specific frequency region in a body surface real-time signal and by determining the area of the time-series signals.

Description

Person

Relationship between breast cancer and estrogen using body surface real-time signals (Technical field)

The present invention relates to the relationship between breast cancer and estrogen using body surface real-time signals.

(Conventional technology)

Breast cancer is a typical hormone-dependent tumor. Therefore, endocrine diagnosis and treatment are performed for diagnosis and treatment of breast cancer. One of them is the method of measuring estrogen (ES) and its receptor (ER), which has attracted attention in recent years. In addition, there are other methods for diagnosing breast tumors such as ultrasound, NMR measurement, and bioelectrical impedance measurement.

However, these methods are diagnostic methods for the response of the signal to the solidified tissue image of breast cancer, whereas the frequency time-series analysis method of the real-time signal waveform of the present method uses the frequency response in response to the biological activity of the living body. This method analyzes the time-varying pattern. This method analyzes the relationship between the physiological activity and the complex network of frequency. In order to apply this study to diagnosis, many cases (100 or more patients) and time and Basic knowledge of frequency analysis is required as an absolute condition. Based on these conditions, the relationship between bioactivity and frequency must be clarified. For that purpose, the relationship with the physiological activity must be examined for each frequency of 1 Hz. Therefore, applying this method to diagnosis requires time and detailed basic research.

(Summary of the Invention)

As shown below, the results of basic research have shown that the bioactivity of breast cancer and estrogen (ES) and intracellular calcium (C a ^{2 +} ) i Because the relationship was found, apply for this law.

In the low frequency range of 1-100Hz, a frequency range showing the relationship between estrogen (ES) and intracellular calcium (Ca2 ⁺ ) i in breast cancer was obtained. The time series pattern of the frequency in this region showed three types of response: vibration response including burst vibration (B0S), spike response, and persistence in the high frequency region. These response patterns indicate that intracellular calcium (Ca ²⁺ ) i metabolic activity by stimulation of various receptors (hormones, immunity, etc.) including the phosphoinositol cascade of the cell membrane in relation to physiological activities Kinetic changes. This fluctuation of (Ca ^{2 +} ) i induces a signal wave. This indicates that the frequency components constituting the biological signal wave reflect the biological activity of the living body. In other words, the results were consistent with the results of many studies on intracellular calcium (Ca ²⁺ ) i using fluorescent dyes using digital imaging microscopy. Research on lucidum (Ca ²⁺ ) i metabolism has been successful only under in vitro (extracellular) microscopic research, and research on real-time measurement methods including the metabolic circuit (cascade) of whole organisms has not been successful. I was waiting. In response to this expectation, the time series analysis method of frequencies obtained from real-time biological signal waveform analysis in this study.

This time series analysis revealed that estrogen (ES) and intracellular calcium (Ca ²⁺ ) i metabolism between breast cancer patients and normal subjects were characteristic in the low frequency range of 1-100 Hz. In other words, the characteristics of the two are different depending on the age and the location of the cancer, but in the low frequency range of 115 Hz in each age group of 20-30, 30-40, 40-50, 50-60, and 60-76 years. The difference in the area of the burst oscillation (B0S) was shown in the sequence pattern (Fig. 4), (Table 4).

In addition, the vibrational structure on the perspective is almost the same in breast cancer than in normal cases. Weakness is a feature of the difference between the two. This may be due to differences in (Ca ²⁺ ) i responses to ES and ES-receptor (ER) at each age (see discussion). In the 10-35 Hz region, both normal and breast cancers shift in frequency (entraimen occurs and burst oscillation (B0S) shifts to a spike type (transient and twin spikes) (Fig. 4). The frequency is different between normal and breast cancer depending on the age.This entrainment region is broader and broader in breast cancer than in normal, and the range is wider. Turns into a pike (Transient) or a twin spike (Bisp.) (Fig. 4, bottom).

Normally, there are more transient spikes than twin spikes (Bisp.), But there are more Bisps in breast cancer. However, when the age is over 60 years old, Bisp. Does not appear in both normal and breast cancer. However, Bisp. Appears (Fig. 4. GH, Fig. 5). According to the text (conventional theory) that in the fluctuation of estrogen (ES) during menstrual cycle, ES shows the lowest value (Emin) on the first day ± 1 day, and shows the highest value (Emax) after 12 days. When comparing the time series patterns of Emin and Emax for a normal person who only shows a transient spike at 55 years of age, a transient spike appears on the first day of Emin, but the same person's Em ax (12 days later) Then, a double spike (Bisp.) Appears (Fig. 6). Furthermore, as a third example, the effect of administration of tamoxifen (Tx), an estrogen receptor (ER) antagonist, was examined. Τχ. Partial or complete disappearance of the bispike (Bisp.) Due to administration. (Figure 7). From these three cases, the bispike pattern (Bisp.) In the time series was interpreted as a mechanism of estrogen (ES) → membrane estrogen receptor (ER) stimulation → activation of (Ca ^{2 +} ) i release. Was. As described above, the frequency time series analysis of the body surface rim signal of breast (normal) and breast cancer in this study showed that the above-mentioned estrogen-cascade in normal and breast cancer was reflected. Therefore, in the future, this method, that is, the time-series analysis method of frequency using real-time signals, is expected to be applied to the study of various physiological activities and the diagnosis of disease states. Therefore, I believe that this law will be widely used in the world and apply here.

(Brief description of drawings)

Figure 1 is a Draf showing the frequency (FFT) spectra of normal individuals and patients with papillary ductal carcinoma.

FIG. 2 is a diagram showing a relationship between upper left and left outer breast cancer and frequency. FIG. 3 is a diagram showing a relationship between upper right, right inner and right outer breast cancer and frequency.

FIG. 4 is a graph showing a time-series pattern in a low frequency region of breast cancer and a normal person,

Figures 4A-1 and 4A-2 show the time series patterns obtained from the upper breasts of 25-35 year old breast cancer patients and normals,

Figures 4B-1 and 4B-2 show the time-series patterns obtained from the outer breasts of 25-35 year old breast cancer patients and normals,

Figures 4C-1 and 4C-2 show the time series patterns obtained from the upper breasts of breast cancer patients and normals between the ages of 40 and 50,

FIGS.4D-1 to 4D-4 show time series patterns obtained from the outer breasts of breast cancer patients aged 40 to 50 years and normals,

Figures 4E-1 and 4E-2 show the time series patterns obtained from the upper breasts of 50- to 60-year-old breast cancer patients and normal persons,

Figures 4F-1 and 4F-2 show 50 to 60 year old breast cancer patients and Shows a time series pattern obtained from the outer breast of a normal person,

Figures 4G-1 and 4G-2 show the time series patterns obtained from the upper breasts of breast cancer patients and normals between the ages of 60 and 76.

Figures 4H-1 and 4H-2 show the time series patterns obtained from the outer breasts of breast cancer patients and normals between the ages of 60 and 76.

Figure 5A is a Draf showing the 5.7.373 Hz time series of the lower left breast of a normal person (66 years old).

FIG. 5B is a graph showing a 58.594 Hz time series of estrogen-treated lower left lower breast cancer patient (66 years old).

FIG. 6A is a graph showing a transient spike time series (E mn) of 68.359 Hz on the first day of the menstrual cycle (ES minimum value) of a normal person (28 years old).

FIG. 6B is a graph showing a 68.359 Hz bispike time series (Emax) of a normal person (28 years old) at 12 days after the menstrual cycle (maximum ES).

FIG. 7A is a Draf showing the 50.49 Hz time series of a lower right breast cancer patient (48 years old).

FIG. 7B shows the time-course of 50.49 Hz of Tamoxifen administration (20 mg / day) lower right breast cancer patient (48 years old: same person as 7A).

FIG. 7C is a graph showing the 64.679 Hz time series of a right posterior breast cancer patient (30 years old).

FIG. 7D shows the 6.4.697 Hz time series of Tamoxifen administration (20 mg / day) right posterior breast cancer patient (30 years old).

FIG. 8 is a diagram schematically showing the relationship between non-voltage-dependent calcium channels and breast cancer.

FIG. 9 is a diagram showing the positions of the electrodes.

FIG. 10 is a schematic configuration of a signal processing device according to one embodiment of the present invention. FIG.

(Example)

Note: In order for this method to be used and applied to the study of bioactivity and the diagnosis of disease, an electrode is inserted into the bioactivity measurement target in vitro, the frequency of the signal wave is analyzed simultaneously, and the relationship between the two is determined. He advises on what needs to be clarified. Purpose

In living organisms, chemical changes due to metabolism are converted into electrical signals, and the signals are emitted from the body surface. EEG, ECG, and EMG capture that signal. In this study, we performed frequency analysis (time-series analysis) of body surface real-time signal waves on the relationship between hormones, estrogen (ES), and intracellular calcium (Ca ²⁺ ) i in breast cancer. The purpose is to find differences between the two. Method

Using a biological signal waveform analysis software (BIMUTASII) from Kitssei Comtech, a surface electrode was placed on the body surface of a breast cancer patient and a normal human at the same age and position as the patient (see Fig. 9; , B is approximately 3 to 5 cm). The signals responding to the electrodes were input to an oscilloscope for biological signal analysis (Nihon Kohden Memory Oscilloscope VC-11) and A / D converted.

FFT analysis of the signal wave was performed, and time series analysis was performed for each frequency of the FFT peak value for normal and breast cancer up to 170 Hz. In this case, if the normal person is the same as breast cancer, analyze the same FFT peak value or the peak value near it and compare the normal and cancer did.

Since the peak value of the FFT increases when the frequency exceeds 100 Hz, a total of 4 normal channels and 2 cancer channels are displayed in parallel, and the spectrum of a normal person (healthy healthy person with no medical history) at the same position as the cancer part is displayed. In comparison, the frequencies that do not appear at the symmetrical position (non-cancerous part) of the normal and cancerous parts (non-cancerous parts) and that appear only at the cancerous part were extracted over 1 to 5000 Hz (Fig. 1). The extracted frequencies are shown for each position (top left, right, inside, and outside breast) and for each histological image (papillary duct carcinoma, solid duct carcinoma, and hard carcinoma), and the relationship between the number of cases and the frequency is shown. Further, frequencies common to the cases are extracted. This is the same age of each cancer location and each tissue image, and the common frequency (common frequency) at the same location. To investigate the nature of the common frequency, time series analysis of the frequency and autocorrelation of the signal wave were performed, and normal and breast cancer were compared. Result

I. Frequency characteristics of normal and breast cancer

As described in the method section above, real-time surface potential waveforms obtained from normal persons and breast cancer patients were subjected to FFT analysis to obtain their frequency characteristics. The frequency characteristic spectrum (FFT spectrum) of the normal and cancer patients was displayed in parallel to compare the normal and cancer patients (FIG. 1). See method section.

I I. Probability distribution of common frequency for location and histology of breast cancer

Group A, 70-80% A 'group, 50-70% B group, 20-40% B 'Group, only one case was classified into Group C.

The frequency range of each group is extracted as a group with one feature in the frequency range of 10 to 17 Hz, and this is connected to a line to form a group. .. '688 · 016 899 · 606 06-900 · 906

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eS30I / l0df / X3d Figures 2 and 3 show the relationship between the above breast cancer position and frequency.

Next, Table 3 shows the relationship between each tissue image and the common frequency for each tissue image of ductal carcinoma.

B. Tissue image and common frequency

Ductal carcinoma is invasive cancer in which cancer cells have invaded the stroma.

Histological features of ductal carcinoma were classified into papillary ductal carcinoma, solid ductal carcinoma and hard carcinoma

1. Papillary duct carcinoma (Papi 1 lotubular carcinoma)

Includes cancers characterized by papillary growth and luminal formation, and squamous cell carcinoma (CO medo car-cinoma). Occasionally, solid growth may occur in some cancer tissues.

2. Solid-tubular carcinoma

Solid cancerous nests are those that show exclusion or swelling growth to surrounding tissues.

Cancer foci consist of solid growth of medullary or obscure small ducts in the glandular cavities. Demonstrates relatively clear boundaries to surrounding tissues almost all around the cancerous lesion

3. Hard cancer (Scirrhous carcinoma)

A type of cancer cells in which the cancer cells are dispersed individually or in small chunks or cords and infiltrate the interstitium, accompanied by the proliferation of interstitial connective tissue. Hard cancer includes two from its origin. One is a narrowly defined hard carcinoma with extremely few intraductal carcinoma foci and advanced interstitial invasion, and the other is derived from papillary duct carcinoma or solid ductal carcinoma and has diffuse interstitial invasion. Predominate II

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° ¾ ¾ ω <^ eS20l / I0df / X3J The common frequency in each histological image in the table is the frequency (A group) that appears in common with a probability of 80% or more in cases. The number shown at the right end of the table is the common frequency probability (%) with respect to the number of cases.

Hard cancer has a high probability of commonality due to few cases, and has many common frequencies. However, the probability of commonality decreases as the number of cases increases.

As described above, time series analysis and autocorrelation analysis of signal waveforms were performed for each extracted common frequency to further prove the reliability of the common frequency of each breast cancer site and each tissue image shown in the table and figure. In comparison, we searched for differences from normal.

I I I. Time series analysis of FFT peak frequency of normal and breast cancer As described in the method section, up to 117 Hz, time series analysis was performed on peak values of FFT for both normal and breast cancer. At frequencies above 100 Hz, analysis was performed for common frequencies in breast cancer, and for normal subjects, time-series analysis was performed for frequencies near the common frequency for breast cancer. 1 Normal and breast cancer features were found in the low frequency range of 1-70 Hz, and time series analysis was performed for this frequency range.

1 The time series pattern in the 15 Hz region varies with age and position, but as shown in Fig. 4, in the case of 25-35 year-old upper (up) and outer (out) breast cancer, it corresponds to the frequency at which organisms occur. In contrast to the burst type vibration (B0S), there are many vibration types under normal conditions.

If you are over the age of 40, it is almost burst type. This burst type oscillation (B0S) showed a difference in area between normal and breast cancer (Fig. 4, Table 4). As explained in the discussion, the area of the oscillating type (B0S) is determined by the amount of Ca ²⁺ -released (releas) from the endoplasmic reticulum (ER) in response to estrogen (ES). ^{2 +} ] i). Figure 4 shows B0S We chose two frequencies, 3.662 and 9.766Hz, which are representative of 1-9.7 Up to 66Hz, the frequency generated by the living organism and the frequency of riding on the burst match. At frequencies above 9.766 Hz, a frequency pull-in phenomenon occurs. Therefore, the area of the burst oscillation (B0S) between normal and breast cancer is expressed as the total power of the 9.76 6 Hz time series divided by the corresponding time (power, sec ~ ¹ = ∑ po er / Σ t (ms)). , This value (area, seer ¹ ), that is, the average power per unit time (power), was compared between normal and breast cancer

Table 4. Left (right), right (R) —surface of 9.766 Hz time series of burst signals in upper and outer breast and breast cancer.

Fig.4 Area of burst signals in the 9.766Hz time series of left (L) and ri ht () up and out breasts in B-cancer and women

u p.

Area indicates power, sec " ¹ (∑po er / ∑time (ms))

up up breast and breast cancer,

out out breast and breast cancer.

* indicates exceptional igh value. Table 4 shows the area (power, sec " ¹ ) of the 9.766Hz time series burst type oscillation (B0S) for the upper (up) and outer (out) breasts (normal) and the age of breast cancer.

Because there were few cases of breast cancer at each age, there was no standard deviation of the area value. However, as shown in Table 4, both the upper (up) cancer and the breast (normal) left (L) Indicates a value about 10 times higher than the right side (R). Comparing the left side (L) and the right side (R) for breast cancer and normal, both show higher values for breast cancer than for normal. On the left side (L), breast cancer is about 10 times higher than normal and on the right side. (R) shows about 4 times higher value. There is no difference in these area values with age, but the value is low in breast cancers over 70 years old, but rather high in normal. On the other hand, in outer (out) breast cancer, the right side (R) is about 10 times larger than the left side (L), contrary to the upper (up) cancer. Also, both left side of breast cancer and normal

Comparing (L), there is no significant difference between the two, with breast cancer being slightly larger. This allows the left side to consider the effects of the myocardium, but does not know what it is. However, on both right sides (R), breast cancer shows a value about 70 times larger than normal.

In FIG. 4, the horizontal axis of each graph starts from 0 and shows the time-dependent numerical value at each 17.5.200 msec interval. (1-b) L and R,

(1—c) L and R, (2-b) L and R, (2-c) L and: R, (3-b) L and R, (4-a) L, (4-c) L And R, (5-c) R, (5-d) L and R, (6-d) L and R ゝ (7-d) L and R, (8-d) then (9—c) L R, (10-c) L and R, (11-c) L and R, (12-c) R, (16-d) R, (20-d) R, (27-c) L and R , (26-c) and (27-b) L and IL, (28-c) L and R, (31-b) L and R, and (32-b) L and R. It is (8-d) scale and (12-c) L that the horizontal axis of each graph indicates the time-dependent numerical values at intervals of 20.400 msec starting from 0. The values shown on the abscissa of the other graphs start from 0 and indicate the values over time at intervals of 1460.000 msec.

The burst type vibration in Fig. 4 is, for example, one of the time-series patterns, and is mainly used for data of patients with breast cancer (Breeast Caneer) of 3.662 Hz and 9.766 Hz. It shows a pattern where the power periodically drops to zero or near zero, as can be seen. A spike is a spike-like undulating waveform.

From the above, there are few cases, but there is a clear difference in the area (power, sec—) of the 9.766 Hz time series between normal and breast cancer. The case (thre-sh-old) for both cases will be calculated in future. As a result, the possibility of diagnosing breast cancer using the area of the 9.766 Hz time-series burst signal (power, sec- ¹ ) was shown.

The signal processing device of the present invention includes a pair of electrodes 1 for detecting an electric signal from a living body, an amplifier 2 for amplifying the electric signal obtained at the electrode 1, a Fourier transform of the amplifier output to a specific frequency, A specific frequency component detection circuit 3 for detecting a signal of a component (mainly a frequency of '10 Hz or less), and integrates the output of the detection circuit up to a predetermined time (for example, FIG. 4 (2-a) L In the case of, the value of POWER on the vertical axis is integrated with respect to the time from 0.000 msec on the left end of the horizontal axis to 466 7 2.000 msec on the right end.) A dividing circuit 5 for dividing the obtained integrated value by the value indicating the predetermined time, data obtained by the dividing circuit 5 being recorded in advance in a recording circuit 6 or the like, and data indicating a value recognized as normal. The combination configuration of the comparison circuit 7 for comparing and outputting the data of the comparison result is an example. Wear. Needless to say, these circuits may be configured in the form of software that can realize the functions by a computer.

The above configuration is only an example, and a specific frequency The time-series change of the turn is displayed on the monitor, and it can be used as an auxiliary means for the doctor's diagnosis.

IV. Entraiment of freguency

In the time series between 1 and 10 Hz, the FFT frequency of the biological signal waveform and the frequency on the burst vibration (B0S) match, but when the frequency exceeds 10 Hz, η

1 mm of retraction occurs.

The starting frequency of this retraction is between 10 and 32 Hz for both normal and breast cancer as shown in Fig. 4, but it is often between 10 and 26 Hz for normal and for breast cancer.

There is a tendency for the frequency to be pulled faster when the frequency is normal between 25-30 Hz.

This retraction is repeated between 0 and 500 Hz in addition to between 1 and 130 Hz for both normal and breast cancer.

Frequency attraction is a self-regulatory mechanism required for cell stabilization.

V. Spike Signal

When frequency pull-in starts from burst oscillation (B0S), it continues

Spike (twin-spike) vibration appears between 30 and 7 OHz. (Fig. 4) Spike vibration includes transient spike (Transient) and twin-spike (Bisp.). Except for transient spikes at 60 to 70 years of age, bilateral spikes appear in both left (L) and right (R) cancers in most age groups.

On the other hand, in the normal case, both transient ones and bi-one spikes appear in each age group. However, in the age group of 40 to 50 and 60 to 70 years old, transient spikes without Bisp. Only appears. In this case, a 60-70 year old transient spike is the same as for breast cancer described above. Figure 5 shows this twin spike signal. As described in pp. 6, 6 and 7, it was almost clear that the kinetics of intracellular calcium (Ca2 ⁺ ) i in response to the estrogen (ES) -estrogen receptor (ER) response.

Based on the results of previous studies, the physiological mechanisms of transient and twin spikes and their control are signals for canceration, and we will await further research on this.

Since breast cancer is a non-excitable cell, the calcium channel on the plasma membrane (Ca ^{2 +} ch.) Is a non-voltage-dependent Ca ^{2 +} c (voltage insensitive Ca ^{2 +} channel). The voltage-dependent Ca ^{2 +} ch. Is activated when cli. (G-protein coupled receptor), G ² -protein and receptor-operated Ca ² + cli. (ROCC) and ER die Three species of Ca ^{2 +} ch. (D0CC: a state in which all Ca ^{2 +} in the endoplasmic reticulum is released) are known (FIG. 8). Figure 8 is a schematic diagram estimated from the literature and the results of this study.

Estrogen (ES) -ES receptor (ER) binds to R0CC and D0CC in these Ca "ch. And mobilizes Ca2 ⁺ from the endoplasmic reticulum, and the (Ca2 ⁺ ) i Are reported to activate MAPK to form a cascade of —transcriptional activity—nuclear gene activation.From these literature reports and the results of this study, burst oscillations in the 9.766 Hz time series described above (item III) are reported. Explaining the difference between the normal (B0S) area and breast cancer, ES-binding protein decreases and blood free estrogen (f-ES) increases after menopause (after age 45) in breast cancer and normal subjects. Since f-ES has a small molecular weight, it can freely pass through cells, enter the cytoplasm, and bind to the tpsi. Receptor (Tg. R) on the endoplasmic reticulum as a kind of mitogen. tg. to release the Ca ^{2 +} from the endoplasmic reticulum to the same extent. Therefore Ca ^{2 +)} i from the endoplasmic reticulum with increasing i-ES release is increased. As a result, Influences the area of time-series burst oscillation (B0S) in the low frequency region of 110 Hz depending on the amount of released (Ca ²⁺ ) i. This indicates that the amount of free estrogen (f-ES) is higher than normal. As a result, a large amount of (Ca ^{2 +} ) i accumulates in the cells, and the homeostasis of Ca ^{2 +} metabolism is disrupted. It is thought that MAPK is stimulated abnormally, and that the metabolic system downstream from this will become abnormal and progress to cancer.

Claims

The scope of the claims

1. A method of detecting a time series of a specific frequency region in a detected real-time body surface signal and detecting cancer information by obtaining an area of the time series.

2. Detection means for detecting an electrical signal from a living body, frequency component detection means for detecting a specific frequency component from the signal detected by the detection means, specific frequency component obtained by the frequency component detection means A biological signal processing apparatus based on the relationship between breast cancer and estrogen based on a real-time body surface signal, comprising a comparing means for detecting area information and comparing the detected area information with a predetermined threshold value.

3. The biological signal processing apparatus based on the relationship between breast cancer and estrogen according to the real-time body surface signal according to claim 2, wherein the specific frequency component has a burst type vibration.