JPS6128335B2 - - Google Patents

Description

[Detailed description of the invention]

The present invention relates to a biological information diagnostic device that processes brain waves of a living body to diagnose various functional disorders and abilities of the living body. Conventionally, brain waves are detected as electrical signals, the electrical signals are recorded on recording paper, and the recorded waveforms are visually observed.
It was used to diagnose brain tumors and other conditions.
Attempts are also being made to associate the recorded waveforms of brain waves with other biological information. Attempts have also been made to diagnose diseases and other biological information by processing electrical signals from brain waves. However, there was still no accurate diagnostic information available. Conventionally, only the detection of a single abnormality appearing in an electroencephalogram waveform or the detection of a difference between electroencephalograms detected from multiple parts of the head was considered. Therefore, even if the brain waves were abnormal, it could not be detected if they occurred regularly. When performing artificial kidney dialysis, in order to know how far the dialysis has progressed, conventionally, the weight of the person undergoing dialysis is measured, and the weight is calculated based on the amount of toxins released from the dialysis. It has been proposed to detect the end of dialysis from the decrease. However, this method requires accurate knowledge of normal body weight, but since normal body weight varies before and after urination, it is difficult to know the absolute value, so it is not possible to accurately detect the end of dialysis. . Furthermore, if dialysis is not performed correctly, water in the blood also leaks out, resulting in weight loss, and from this point of view as well, it is difficult to accurately know when dialysis has ended. In addition, in the past, blood was collected before and after dialysis and analyzed to measure various amounts of urea, creatylene, uric acid, etc., and based on that data and dialysis time, it was determined how much dialysis should be performed for the patient. I was trying to know. However, since obtaining such data requires time and date, it is not possible to know how far dialysis has progressed during dialysis. Therefore, it is necessary to estimate how much dialysis should be performed next based on data from multiple dialysis sessions, and if the patient's condition changes, it is not possible to accurately know when dialysis has ended. An object of the present invention is to provide a biological information diagnostic device that can detect even regular abnormalities in brain waves and can accurately diagnose biological functions and disorders. According to this invention, brain waves are extracted as a wave-like electrical signal corresponding to the waveform, and a value obtained by counting the number of times the electrical signal crosses a certain level (including zero crossings, the same shall apply hereinafter) for a certain period of time, and a constant value of the electrical brain wave signal The passage of time with respect to the standard deviation of the distribution of variations in the time intervals of level crossings is recorded and displayed as a graph on recording paper, displayed on a cathode ray tube display, or printed as numerical values on a printer. Based on this display, various functions and disorders of the living body are diagnosed. Furthermore, if necessary, the standard deviation of the dispersion distribution of peak values between constant level crossovers of the electroencephalogram electrical signal;
Standard deviation of signal area variation distribution between constant level crossovers of electric brain wave signals, standard deviation of amplitude rate density distribution (distribution of amplitude values) within a certain time of electric brain wave signals, standard deviation of power spectrum of electric brain wave signals, Furthermore, at least one parameter such as the average value of the count value, time interval, peak value, etc., or the difference between the highest and lowest values of each of these histograms is extracted, and the time course of the extracted parameter is displayed. Make it. Figures 1 and 2 each show experimental data in which the above parameters were measured during dialysis in different subjects undergoing artificial kidney dialysis. In both of these figures, curve K ₁ shows the number of crossings of a certain level of electroencephalogram electrical signals within a certain period of time, and curve K ₂ shows the standard deviation of the amplitude probability density distribution within a certain period of time.
K ₃ is the standard deviation of the peak value dispersion distribution between constant level crossovers, curve K ₄ is the standard deviation of the time interval dispersion distribution of constant level crossovers, and curve K ₅ is the standard deviation of the area dispersion distribution between constant level crossovers. be. The horizontal axis represents the elapsed time, and the vertical axis represents the number N and standard deviation σ. Curve K ₁ ,
K ₂ and K ₃ increase as dialysis progresses, and the curve
_K4 decreases as dialysis progresses. From these trends, it is possible to know the progress of dialysis and the end of dialysis. That is, by storing at least one of these initial values and comparing the corresponding current data with the initial value, the dialysis status can be known. The reason why dialysis status and brain waves are related is that before dialysis, there are relatively many toxins in the blood, which enter the brain and affect brain function, causing large variations in brain wave waveforms, but as dialysis progresses This is thought to be due to the fact that toxins entering the brain decrease, brain activity becomes more active, and brain waves are generated more frequently, resulting in less variation in waveforms. Figure 3 shows the transition of electroencephalogram parameters when the dog's liver was removed, and curves K ₁ to K ₅ are the same as those in Figures 1 and 2.
Identical to the corresponding one in the figure. Partially block the portal vein at time t ₁ , open it at time t ₂ , block the inferior vena cava at time t ₃ , completely block the portal vein at time t ₄ , and remove the liver at time t ₅ . , respectively indicate that the portal vein and inferior vena cava were opened at time t ₆ .
The curve K ₁ K ₂ K ₃ K ₅ is decreasing with time and the curve K ₄ is increasing with time. From these curves, it is possible to diagnose hepatitis, liver cirrhosis, fulminant hepatitis, and hepatic encephalopathy based on these, and also to know the progress status thereof. Furthermore, FIG. 4 shows the results of injecting aqueous ammonia through an intravenous drip to reduce kidney function, and recording the various parameters described above during the recovery process. t ₀
Start ammonia water injection at 100c.c. at t ₁ , 100c.c. at t ₂
The time points at which injection of 200c.c., 300c.c. at t ₃ , and 400c.c. at t ₄ were completed are shown, respectively. Curves K ₁ to _{K 5} are the same as in FIGS. 1 to 3, respectively. Kidney function decreases due to ammonia water injection, and curves K ₁ , K ₂ , and K ₃ decrease accordingly, and curve K ₄ rises, and as kidney function recovers after ammonia water injection is stopped. K ₁ , K ₂ , K ₃ gradually increases, and K ₄
is understood to be gradually decreasing. Kidney disorders and associated encephalopathy can be diagnosed from these curves. As understood from the above description and drawings, K ₁
Since the changes between K 1 and K ₄ are particularly large, and the changes are opposite to each other, correct diagnosis can be made by determining K ₁ and K ₄ . Next, an embodiment of the biological information diagnostic apparatus according to the present invention will be described with reference to FIG. Electrodes 2 are attached to a plurality of guiding points on the head 1, and these electrodes 2 are connected through connecting wires 3 and further connected to an electrode terminal plate 4.
It is connected to the switching device 5 for switching the induction point via. An electric brain wave signal (hereinafter simply referred to as a brain wave signal) from one induction point selected by the switch 5 is amplified by an amplifier 6. The configuration up to now is the same as that of a conventional electroencephalograph, and a conventional electroencephalograph generally has a
It is designed to extract 12 guiding points. When looking at simultaneity, it is sufficient to prepare a plurality of configurations including the amplifier 6 and below, and supply brain wave signals from different induction points to each of them. The output of the amplifier 6 is subjected to a wave generator 7 in which unnecessary components are removed. As the wave device 7, a combination of a low-pass wave device and a high-pass wave device, or a combination thereof or the like may be used, depending on the measurement state at that time. The output signal Vi of the wave generator 7 is sent to the sampling point detection circuit 8
and a mask signal detection circuit 9 for removing unnecessary signals, and a sampling command signal and a microphone command signal are respectively created. The electroencephalogram signal Vi is input to each signal processing section of a peak value detection circuit 11, a waveform duration detection circuit 12, a waveform area detection circuit 13, and a preamplifier 14 for amplitude probability density detection, and is subjected to signal processing according to each circuit. is done. At this time, these signal processing circuits 11 to 13
Gate circuits 15 to 17 provided at the next stage, and cumulative storage units 21 to 2 respectively continued therefrom.
A sampling command to 3 is issued at the same time. Sampling commands to the gate circuit 18 at the next stage of the preamplifier 14 and the data accumulation storage section 24 connected in cascade thereto are carried out by means of regular sampling commands from a pulse generator (not shown). An amplitude probability density distribution is measured. The data accumulation storage units 21 to 24 have a function of adding and accumulating analog input voltages in memories provided for each input voltage magnitude for each sampling command. The data stored in the data accumulation storage units 21 to 24 are further converted into analog values and recorded as a hard copy in the recorder 25, or are recorded on the display device 26.
The data is displayed on a cathode ray tube display device, and the data content is monitored. Furthermore, the data from these cumulative storage units 21 to 24 are input as digital values to a data processor 27, where data calculation processing described later is performed and a certain evaluation value is output. A control circuit 28 controls the data cumulative storage units 21 to 24, the recorder 25, the display device 26, the data processor 27, etc.
This is done by The number of zero level crossings of the electroencephalogram signal occurring within a predetermined time is counted by the crossing number counting circuit 151. In addition, the initial value of the standard deviation of the dispersion distribution of various data obtained by the data processor 27 and the initial value of the crossover number counting circuit 151 are stored in the initial value storage circuit 1.
52 respectively. Next, the details of each part in Fig. 5 will be explained, but since this device can be constructed using linear ICs and digital ICs, which are currently commercially available semiconductor integrated circuits, the explanations will be explained using logical formula names without any particular emphasis. do.
FIG. 6 shows a part of the sampling detection circuit 8 and the processing section, and shows the electroencephalogram signal input to the input terminal 29.
Vi is compared with a reference voltage from a variable voltage setter 31 by a comparator 32. This comparator 32 sets the variable voltage setter 31 so that the comparison is made at a level near the zero crossing of the signal Vi due to the nature of the signal. This setting is changed depending on the level of the electroencephalogram signal, and the zero crossing point of the signal is preferable, but the level is determined depending on the situation. Therefore, the output of the comparator 32 becomes a rectangular wave output Vob that changes from positive to negative and from negative to positive almost at each zero crossing point for the input electroencephalogram signal Vi shown in FIG. 11, and undergoes level conversion. It is supplied to the container 33.
At this time, if a DC component is added to the rectangular wave signal, this DC component must of course be cut off.
The rectangular wave signal Vob is level-converted to a TTL level by a converter 33 and is supplied to a delay circuit 34 and an exclusive OR circuit 35. Here, the TTL level rectangular wave corresponding to the input brain wave signal Vi is delayed by 1 to 2 μsec in the waveform delay circuit 34, and the exclusive OR of the delayed waveform and the waveform before the delay is performed in the circuit 35. The first falling point of the output signal of the exclusive OR circuit 35 is extracted by the waveform differentiating circuit 36 and used as a sampling command signal. This sampling command signal is also supplied to the crossover number counting circuit 151, and counting is repeated for a predetermined period of time. The output of the waveform delay circuit 34 is output from the TTL level by the waveform shaping circuit 37 of the waveform duration detection circuit 12.
It is converted to a square wave Vob with the polarity of . This signal is integrated by an integrating circuit 44 constituted by an integrating capacitor 42 and an operational amplifier 43 through resistors 39 and 41 in a signal zero shot circuit 38 which is cut off when the input electroencephalogram signal Vi is zero. On the other hand, the rising and falling points of the rectangular wave output from the waveform delay circuit 34 are detected by waveform differentiating circuits 45 and 46, respectively, and these detection signals drive a monostable multiviprator 48 through an OR circuit 47. A reset signal is applied to the FET switch 51 through the input terminal 49 for the operating time of the multi-biprator 48, which is turned on and the electric charge of the integrating capacitor 42 is discharged through the switch 51, and the integrating circuit 4
4 is reset. With this configuration, as shown in FIG. 11, the time from one zero-crossing point of the input electroencephalogram signal Vi to the next zero-crossing point is converted into a voltage _VOB , which is obtained at the output terminal 52 of the integrating circuit 44. The input brain wave signal Vi from the terminal 29 is supplied to the amplifier 54 through the input terminal 53 of the waveform area detection circuit 13. The output of the amplifier 54 is integrated through the resistors 56 and 57 in the signal zero shot circuit 55 by an integrating circuit 61 composed of an integrating capacitor 58 and an operational amplifier 59. The integrated output V _OC is output to terminal 62 (see FIG. 11). The integration circuit 61 is reset by inputting a reset signal from the reset input terminal 64 for the operating time of the monostable multivibrator 63 triggered by the output of the OR circuit 47 mentioned above, and turning on the FET switch 65 to perform the reset. It will be done. With this configuration, a signal V _OC indicating the area value of the waveform from a zero crossing point to the next zero crossing point of the electroencephalogram waveform is obtained at the terminal 62. In Fig. 6, the signal zero shot circuit 3
Commands to 8 and 55 are sent to the mask signal detection circuit 9, which will be described later.
given by FET switch 66,6 at that time
7 is turned on, and through their FET switches, the connection point of resistors 39 and 41, resistor 5
The connection points 6 and 57 are each set to approximately zero potential. Next, the peak point detection circuit 11 will be explained with reference to FIG. The input brain wave signal Vi is supplied from the terminal 68 to each non-inverting input terminal of the operational amplifiers 69 and 71 for buffering, and the positive side of the input brain wave signal Vi is connected to the diode 72.
The capacitor 73 is charged through the capacitor 73. The voltage of this capacitor 73 is supplied through a buffer operational amplifier 74 to a non-inverting input terminal of a summing operational amplifier 75. On the other hand, the negative side of the signal Vi at the terminal 68 is supplied from the buffer operational amplifier 71 to the capacitor 77 through the diode 76 . The voltage charged in the capacitor 77 is supplied through the buffer FET 78 to the non-inverting input terminal of the summing operational amplifier 75 at the next stage.
In this way, the peaks of the respective polarities are charged and held in the capacitors 73 and 77 at each polarity switching point, that is, each zero crossing point, of the input signal Vi, and these are alternately sent to the output terminal 7 of the summing operational amplifier 75 as the output V _OA .
Appears on the 9th. At this time, the reset is carried out by the output of the monostable multivibrator (not shown) triggered by the output of the OR circuit 47 in FIG. This is done by discharging the electric charge. With the above configuration, the peak voltage V _OA of the voltage from the zero crossing point of the input signal Vi to the next zero crossing point is detected at the terminal 79 as shown in FIG. The preamplifier 14 for amplitude probability density detection is composed of an operational amplifier 83, for example, as shown in FIG. The mask signal detection circuit 9 outputs a signal to inhibit signal processing when the input signal Vi is extremely small or too large, and is configured, for example, as shown in FIG. 9. Input brain wave signal from terminal 84
Vi is a comparator 85 and a variable voltage setter 186
The comparator 85 outputs a constant positive voltage if Vi is larger. This comparator output is connected to a level converter (not shown).
After level conversion is performed, the rising edge is extracted by the differentiating circuit 86, and the monostable multivibrator 87 at the next stage is triggered by the output thereof. This monostable multiviprator 87 is a retrigger type,
If the time from one positive side of the waveform of the input signal Vi to the next positive side is shorter than the operation setting time of the monostable multivibrator 87, the output of the monostable multivibrator 87 will rise to a high level from the rising pulse input thereafter. Continues for the set time. This high level output turns on transistor switch 88, which short-circuits capacitor 89.
The comparator 91 compares the voltage of the capacitor 89 and the voltage obtained by dividing the power supply voltage V ₁ by the variable resistor 90, and when the voltage of the capacitor 89 is higher, the output of the comparator 91 becomes zero level. . A connection point between the collector of the transistor switch 88 and the capacitor 89 is connected to a power supply through a variable resistor 92. If the input signal Vi remains zero for a period of time determined by the charging time determined by the variable resistor 92 and the capacitor 89 and the divided output voltage of the variable resistor 90, and no trigger is applied to the multivibrator 87. , comparator 91
The output of becomes zero level. The output of the gate circuit 93 to which the output is applied becomes a low level, and this low level prevents the sampling command signal from going to the next stage. The input signal Vi from the terminal 84 is compared with the reference voltage from the variable voltage setter 94 by a comparator 95, and when the signal Vi is greater than the voltage of the variable voltage setter 94, the output of the comparator 95 becomes a constant positive potential. The output of the comparator 95 is sent to the level converter 9
6, the voltage level is lowered and applied to the transistor switch 97, which is turned on, and the transistor switch 97 short-circuits the capacitor 98. The comparator 99 compares the divided voltage of the variable resistor 101 to which a constant voltage is applied and the voltage of the capacitor 98, and when the voltage of the capacitor 98 becomes higher than the former voltage, the comparator 99
9 outputs a high level, and when the voltage of the capacitor 98 is lower than the divided voltage of the variable resistor 101, it outputs a zero level. The capacitor 98 is connected to the power supply through the variable resistor 102.
Charging time constant of and capacitor 98 and variable resistor 1
If the input signal Vi continues to be extremely large for a period determined by the divided voltage of The output of the gate circuit 93 to which the output is applied becomes a low level, and the sampling command signal is blocked from passing to the next stage. By turning on the switch 103, a low level is applied to the gate circuit 93, forcing its output to a high level, and these signal masking operations can also be inhibited. Outputs V _OA processed by the peak point detection circuit 11, waveform duration detection circuit 12, waveform area detection circuit 13, and preamplifier 14, which have already been described, respectively.
.about.V _OD are classified and cumulatively stored in the data cumulative storage sections 21 to 24 shown in FIG. 5, respectively, according to the sampling command. These cumulative storage units 21 to 24 can have the same configuration, and a processed voltage, for example _VOA , is input to the analog input terminal 105 as shown in FIG. When a sampling command input is input to the sampling command terminal 106, the pulse shift circuit 109 is driven through the gate 108 to which the output of the gate circuit 93 shown in FIG. A signal is sent from terminal B of the shift circuit 109 to the sample hold circuit 111,
The analog input V _OA at terminal 105 is sampled and held. At this time, the pulse shift circuit 109 outputs a signal from its terminal C to the A/D converter 112, and the A/D converter 112 converts the output analog voltage of the sample hold circuit 111 in the previous stage into a digital voltage. The A/D converter 112 has an accuracy of 7 to 12 bits and is used for economic reasons, and in this embodiment, an 8-bit one is used. When the A/D converter 112 completes the conversion of the analog value into a digital value, the terminal ST of the A/D converter 112
A signal is output to the latch circuit 113, and the converted digital value is latched. The memory 1 uses this digital signal as an address through the address bus 124.
14a to 114c, select the memory,
The contents of the memory are latched in latch circuits 115a/115c by a command from terminal E of pulse shift circuit 109, and the outputs of these latch circuits 115a to 115c are input to adders 116a/116c.
The adders 116a to 116c are full adders, such as those manufactured by Texas Instruments.
7483 etc. is used, and the input terminal of the adder 116a
B ₂ , B ₃ , B ₄ , and each input terminal B ₁ to _{B 4} of the adders 116b and 116c are grounded, and the adder 11
By setting only the terminal _B1 of 6a to a high level, only one bit is added to one of the addition inputs _A1 to _A4 . Next, the parallel 4-bit addition outputs of the adders 116a to 116c are sent to the reset gates 117a to 1.
17c respectively to the memories 114a to 114.
c. The memories 114a to 114c use, for example, three 4-bit x 256 2101 memories manufactured by Intel Corporation, and a 12-bit configuration is shown here. A write command to memories 114a to 114c is given from terminal D of pulse shift circuit 109. With the above configuration, an analog value is converted into a digital value for each sampling command, and the memories 114a to 114c have the digital value as an address.
is read out, 1 is added to its contents, and the memory 11 is stored again.
A writing operation is performed to 4a to 114c. Next, the contents written in the memories 114a to 114c are determined by the timing of the addition mentioned earlier and by the data processor 27.
(FIG. 5), the address generator 118 sequentially inputs addresses to the memory 1.
14a to 114c, the memory contents at this time are latched to latch circuits 119a to 119c and supplied to the next stage multiplier 121, and the output is output as an analog output to the terminal 123 through the D/A converter 122. The information is displayed on the recorder 25 and display device 26 shown in FIG. 5 for monitoring. The multiplier 121 can use a bit slicing method in combination with a channel selector IC. If the address switching speed of the address generator 118 is set to every 0.5 seconds or 1.0 seconds, the terminal 1
The analog output of the terminal 123 is used for the recorder 25, and if the switching speed is set to every 1 to 100 microseconds, the analog output of the terminal 123 is used for the display device 26. The address bus 124 for the memories 114a to 114c is a gate 125 of a tri-state IC.
When an address is given from the latch circuit 113 during addition through the data processor 27, the memory 114a is
When reading out the memories 114a to 114c, priority is determined by a combination of the AND circuit 126, the NAND circuits 127 and 128, and the inverter 129 so that the order of use is when the memories 114a to 114c are displayed on the display device 26.
114c switching operation is performed. The outputs of the latch circuits 119a to 119c are connected to the data bus 131 by a tri-state buffer circuit 13.
2 and used for reading by the data processor 27. The memories 114a to 114c may be directly connected to this data bus 131 through a tristate buffer circuit 132. In FIG. 10, if a low level is supplied to the mask signal input terminal 107 from the gate circuit 93 of FIG. 9, the sample command is prohibited from entering this data accumulation storage section. Next, to obtain the amplitude probability density distribution of the signal, that is, the distribution of amplitude values within a certain period of time, the input signal Vi is input to the analog input terminal 105 in FIG. 10 through the preamplifier 14 in FIGS. 5 and 8. be done. Periodic pulses are input from a separately provided pulse generator (not shown) to the sample command input terminal 106 in FIG. 10, for example, every 100 μsec or every 1 msec. For each sample command pulse input, the 11th
The input signal Vi shown in FIG. 11 is sampled as V _OD in FIG. 11, and the sampled values are subjected to the processing described above to obtain amplitude probability density distributions in memories 114a-114c. The series of processing results explained above are illustrated in FIG. FIG. 12A is a distribution curve showing the amplitude probability density distribution obtained in the data cumulative storage unit 24, the number of samples N is 3000, the average is 135.8V, and the standard difference σ
was 31,312. Figure B is a curve showing the distribution of peak values between zero crossing points obtained in the cumulative storage unit 21, where N = 595, = 131.6V, σ = 35.1. This is an area value histogram between intersection points, N=595, =131.6Hz, σ=
10.3, and D in the figure is a time interval histogram of adjacent zero crossing points obtained in the cumulative storage unit 23, and N
= 595, = 132.4, σ = 10.3. These distributions were obtained in a relatively short period of time, those obtained over a relatively long period of time such as several hours, and those obtained at multiple induction points, that is, at each measurement point, and those obtained by stimulating the subject. From what was obtained for each brain wave at the time, for each,
Diagnosis is performed based on the pattern of the distribution curve, that is, the shape, the pattern of a certain singular point, the frequency, etc. Furthermore, evaluation based on the mean value and standard deviation σ of each distribution, or the difference between the highest and lowest values of the histogram, that is, the fluctuation range R, and evaluation based on σ and R of each singularity group within each distribution curve, that is, each population. ,
The singular point is extracted and compared with the initial value in the memory circuit 152, in other words, the time course of these various extracted parameters is known, and for example, as mentioned earlier, the progress state of artificial kidney dialysis, the disease state of nephritis or hepatitis, etc. can be diagnosed. Note that the standard deviation σ can be easily obtained from the various histograms shown in FIG. 12 in a short time using an electronic computer. The data processor 27 can have this function. At regular intervals, the standard deviation σ, the average value, the variation width R, and the number of level crossing points of the electroencephalogram signal are stored in a predetermined order in a plurality of registers 161, as shown in FIG. 13, for example. The data processor 27 controls the scanner 162 to read out the stored values in the register 161 from the oldest memory to the cathode ray tube display 163.
supplied to Therefore, the curves shown in FIGS. 1 to 4 are displayed on the display 163. You can also display the difference between the initial value and the current value, or divide the current value by the initial value and display it as a percentage. Based on these results, we can compare the results with similar patterns and data obtained for various previously known cancer conditions to determine whether the disease is normal, abnormal, or abnormal.
Furthermore, it is possible to determine the degree of the disease state. These operations can also be performed online by the data processor 27. In this way, the current electroencephalogram information, such as the left-right and front-back symmetry of the head, hypnotic waves, sound stimulation waves, optical stimulation waves, epileptic waves, brain waves in various medical conditions, and waveforms in unconscious states, can be statistically compared to normal people. Because comparative studies can be carried out faithfully, it is extremely effective, including in determining the course of disease progression. Furthermore, it is possible to know not only the direct damage to the brain itself, but also the names and conditions of diseases such as nephritis and hepatitis, as well as accurately detect the end of dialysis in an artificial kidney and test whether dialysis is required. In the above, the average value, standard deviation σ, fluctuation range R, etc. are obtained through the above calculation processing, and the elapsed time is recorded and displayed on an X-Y recorder, or not only displayed on a cathode ray tube display, but also printed as numerical values on a printer. It may also be displayed by printing it out as . In addition, in addition to the amplitude probability density distribution, peak point distribution, area distribution, etc., the analysis section obtains the power spectrum of the brain waves using a spectrum analyzer as described above. It can also be displayed to aid in diagnosis. As described above, according to the present invention, the count value of the number of level crossings of electrical signals of biological information within a predetermined time is displayed as a time passage, and the time passage of the standard deviation value of the distribution of level crossing interval lengths is displayed. displayed,
Each of these changes relatively significantly in response to various symptom changes, and they change inversely to each other, so by comparing the two, it is possible to more accurately diagnose symptom changes and various progress statuses. In the past, the degree of coma in hepatic encephalopathy was classified as shown in the table below.

[Table] Doctors used to observe the symptoms of hepatic coma in patients, determine which category the patient falls into, and take appropriate measures, but they are having difficulty determining the category. FIG. 14 shows the results of determining the level crossing value K ₁ and the standard deviation value K ₄ of the distribution of level crossing interval lengths for many patients with various symptoms. When the degree of hepatic coma of this target patient at that time is determined based on the above table, Fig. 14 can be divided into regions.
By calculating K ₁ and K ₄ from this Figure 14, the degree of hepatic coma can be immediately determined. Furthermore, for example, if only K ₁ is calculated, the number is slightly above 200, and the condition is determined to be at a level of hepatic coma, but if K ₄ is also calculated, K ₄ is close to 40, and the condition is judged to be in a state of hepatic coma. In some cases, it may be determined that the condition is close to degree. In other words, accurate determination can only be made by finding both K ₁ and K ₄ .

[Brief explanation of the drawing]

Figures 1 and 2 are curve diagrams showing changes in various parameters of the electroencephalogram signal during artificial kidney dialysis, Figure 3 is a curve diagram showing changes in various parameters of the electroencephalogram signal during liver removal, and Figure 4 is a curve diagram showing changes in various parameters of the electroencephalogram signal during liver removal. The figure is a curve diagram showing changes in various parameters of the electroencephalogram signal during ammonia water infusion and after it is stopped. FIG. 5 is a block diagram showing an example of the biological information diagnostic device according to the present invention. FIG. 6 is a sampling detection circuit 8. , a block diagram showing an example of the time interval detection circuit 12 and the area detection circuit 13, FIG. 7 is a block diagram showing an example of the peak detection circuit, FIG. 8 is a connection diagram showing an example of the preamplifier 14, and FIG. is a circuit diagram showing an example of a mask signal detection circuit, FIG. 10 is a block diagram showing an example of a data accumulation storage section, FIG. 11 is a waveform diagram showing an example of signal processing, and FIG. 12 is a diagram showing various distribution curves obtained. FIG. 13 is a block diagram showing an example of the memory display means, and FIG. 14 is a graph showing the relationship between K ₁ , K ₄ and the degree of hepatic coma.

Claims (1)

[Claims] 1. Means for detecting brain waves as electrical signals, means for counting the number of level crossings of the detected electrical signals within a predetermined time period, and means for obtaining a distribution of the length of level crossing intervals of the electrical signals within a predetermined time period. a means for calculating a standard deviation value from the distribution of the obtained level crossing interval length; a means for repeatedly calculating the number of level crossings and the standard deviation value at regular intervals; and a means for calculating the number of level crossings and the standard deviation. A biological information diagnostic device comprising means for displaying a value over time.