CN105078444B - Noise detection method and device and medical detection equipment - Google Patents
Noise detection method and device and medical detection equipment Download PDFInfo
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- CN105078444B CN105078444B CN201410421280.3A CN201410421280A CN105078444B CN 105078444 B CN105078444 B CN 105078444B CN 201410421280 A CN201410421280 A CN 201410421280A CN 105078444 B CN105078444 B CN 105078444B
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Abstract
The invention discloses a noise detection method and device and medical detection equipment. The method comprises the following steps: calculating the input electrocardiosignals to obtain characteristic parameters of the electrocardiosignals; and judging whether the characteristic parameters meet the intermediate frequency noise condition, and if the characteristic parameters meet the intermediate frequency noise condition, outputting the detected intermediate frequency noise. By implementing the embodiment of the invention, the characteristic parameters of the electrocardiosignal can be calculated, and when the characteristic parameters meet the condition of intermediate frequency noise, the detected intermediate frequency noise is output to remind a doctor that the intermediate frequency noise exists in the electrocardiosignal, thereby preventing the doctor from misdiagnosing the patient.
Description
Technical Field
The invention relates to medical instruments, in particular to a noise detection method and device and medical detection equipment.
Background
The range of the electrocardiosignal amplitude of the human body is basically between 0.1mv and 5mv, and the electrocardiosignal amplitude is easily influenced by various environmental factors. At present, interference and noise in electrocardiosignals comprise power frequency interference, electromyographic interference, intermediate frequency noise and the like, wherein the intermediate frequency noise with the frequency of 5Hz to 40Hz and normal electrocardiosignals are mixed together and are difficult to filter by a filter filtering method. The noise is superposed in the normal electrocardiosignal, which can cause misjudgment of the patient electrocardio condition by a doctor, thereby causing misdiagnosis and having great harmfulness.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide a noise detection method, a noise detection device and medical detection equipment, which can detect the presence of intermediate frequency noise in an electrocardiographic signal.
In a first aspect, an embodiment of the present invention provides a noise detection method, including: calculating the input electrocardiosignals to obtain characteristic parameters of the electrocardiosignals; and judging whether the characteristic parameters meet the intermediate frequency noise condition, and if the characteristic parameters meet the intermediate frequency noise condition, outputting the detected intermediate frequency noise.
Optionally, after the calculating the input cardiac signal to obtain the characteristic parameter of the cardiac signal, the method further includes: and judging whether the characteristic parameters meet the baseline drift noise condition, and if the characteristic parameters meet the baseline drift noise condition, outputting the detected baseline drift noise.
Optionally, the feature parameters include the number of times of unchanged peaks in adjacent unit time and a baseline wander noise proportionality coefficient, the determining whether the feature parameters satisfy a baseline wander noise condition, and if the feature parameters satisfy the baseline wander noise condition, outputting the detected baseline wander noise includes: judging whether two conditions that the number of unchanged times of the wave crests of adjacent unit time is smaller than a first threshold value and the baseline drift noise proportional coefficient is larger than a second threshold value are met simultaneously; outputting the detected baseline wander noise if the two conditions are satisfied simultaneously; or, the characteristic parameter includes a baseline wander noise proportionality coefficient, the determining whether the characteristic parameter satisfies a baseline wander noise condition, and if the characteristic parameter satisfies the baseline wander noise condition, outputting the detected baseline wander noise includes: judging whether the baseline drift noise proportionality coefficient is larger than a third threshold value; if the baseline wander noise scaling factor is greater than a third threshold, then the output detects baseline wander noise.
Optionally, the feature parameters include a number of peaks in a unit time, a number of changes in a width direction of adjacent peaks, a maximum peak interval regularity parameter, and a minimum peak interval regularity parameter, and the determining whether the feature parameters satisfy an intermediate frequency noise condition, and if the feature parameters satisfy the intermediate frequency noise condition, outputting the detected intermediate frequency noise includes: judging whether the conditions that the number of wave crests in unit time is larger than or equal to a fourth threshold value, the ratio of the change times of the width direction of adjacent wave crests divided by the number of wave crests in unit time is larger than a fifth threshold value, and the maximum wave crest interval regularity parameter and the minimum wave crest interval regularity parameter are both smaller than a sixth threshold value are met; if three conditions are met simultaneously, outputting the detected intermediate frequency noise; or, the characteristic parameters include a unit time amplitude square sum, an amplitude square sum threshold, a unit time peak number threshold, a baseline drift noise ratio coefficient, and a unit time wide peak number ratio, the determining whether the characteristic parameters satisfy the intermediate frequency noise condition, and if the characteristic parameters satisfy the intermediate frequency noise condition, outputting the detected intermediate frequency noise includes: judging whether the sum of the squares of the amplitudes of the unit time is larger than m times of the sum of the squares of the amplitudes, the number of the wave crests of the unit time is larger than or equal to a seventh threshold value, the difference between the number of the wave crests of the unit time and the threshold value of the number of the wave crests of the unit time is larger than an eighth threshold value, the base line drift noise proportion coefficient is smaller than a ninth threshold value, and the proportion of the wide wave crests of the unit time is smaller than a tenth threshold value, wherein m is a real number; if the five conditions are met simultaneously, outputting the detected intermediate frequency noise; or, the characteristic parameter includes a number of peaks in unit time, a baseline drift noise ratio, a number of times that the number of peaks in adjacent unit time does not change, and a threshold value of the number of peaks in unit time, the determining whether the characteristic parameter satisfies an intermediate frequency noise condition, and if the characteristic parameter satisfies the intermediate frequency noise condition, outputting the detected intermediate frequency noise includes: judging whether the conditions that the number of the wave crests in unit time is greater than or equal to an eleventh threshold, the baseline drift noise proportionality coefficient is greater than a twelfth threshold, the number of unchanged times of the adjacent wave crests in unit time is less than a thirteenth threshold, and the number of the wave crests in unit time is greater than n times of the threshold of the number of the wave crests in unit time are met simultaneously, wherein n is a real number; if the four conditions are simultaneously satisfied, the detected intermediate frequency noise is output.
Optionally, the calculating the input cardiac signal to obtain the characteristic parameter of the cardiac signal comprises: and preprocessing the input detection signal to obtain an electrocardiosignal.
In a second aspect, an embodiment of the present invention provides a noise detection apparatus, including: the device comprises a calculation module and a first judgment module, wherein the calculation module is used for calculating the input electrocardiosignals to obtain the characteristic parameters of the electrocardiosignals; the first judging module is used for judging whether the characteristic parameter meets an intermediate frequency noise condition, and if the characteristic parameter meets the intermediate frequency noise condition, the detected intermediate frequency noise is output.
Optionally, the apparatus further includes a second determining module, where the second determining module is configured to determine whether the characteristic parameter satisfies a baseline wander noise condition, and if the characteristic parameter satisfies the baseline wander noise condition, output a detected baseline wander noise.
Optionally, the characteristic parameters include the number of times of no change of the number of peaks in adjacent unit time and a baseline wander noise proportionality coefficient, the second determining module is specifically configured to determine whether two conditions that the number of times of no change of the number of peaks in adjacent unit time is less than a first threshold and the baseline wander noise proportionality coefficient is greater than a second threshold are met at the same time, and when the two conditions are met at the same time, output that baseline wander noise is detected; or, the characteristic parameter includes a baseline wander noise proportionality coefficient, and the second determining module is specifically configured to determine whether the baseline wander noise proportionality coefficient is greater than a third threshold, and output the detected baseline wander noise when the baseline wander noise proportionality coefficient is greater than the third threshold.
Optionally, the characteristic parameters include the number of peaks in unit time, the number of changes in the width direction of adjacent peaks, a maximum peak interval regularity parameter, and a minimum peak interval regularity parameter, the first determining module is specifically configured to determine whether the number of peaks in unit time is greater than or equal to a fourth threshold, a ratio obtained by dividing the number of changes in the width direction of adjacent peaks by the number of peaks in unit time is greater than a fifth threshold, and both the maximum peak interval regularity parameter and the minimum peak interval regularity parameter are less than a sixth threshold, and when the three conditions are met, the detected intermediate frequency noise is output; or, the characteristic parameters include a unit time amplitude sum of squares, an amplitude sum of squares threshold, a unit time peak number threshold, a baseline drift noise ratio coefficient, and a unit time wide peak number ratio, the first determining module is specifically configured to determine whether the unit time amplitude sum of squares is greater than the m-fold amplitude sum of squares threshold, the unit time peak number is greater than or equal to a seventh threshold, a difference between the unit time peak number and the unit time peak number threshold is greater than an eighth threshold, the baseline drift noise ratio coefficient of squares is less than a ninth threshold, and the unit time wide peak number ratio is less than a tenth threshold, where m is a real number, and when the five conditions are satisfied simultaneously, the detected intermediate frequency noise is output; or, the characteristic parameters include the number of peaks in unit time, a baseline wandering noise proportionality coefficient, the number of times that the number of adjacent peaks in unit time does not change, and a threshold of the number of peaks in unit time, the first determining module is specifically configured to determine whether the number of peaks in unit time is greater than or equal to an eleventh threshold, the baseline wandering noise proportionality coefficient is greater than a twelfth threshold, the number of times that the number of adjacent peaks in unit time does not change is less than a thirteenth threshold, and the number of peaks in unit time is greater than n times the threshold of the number of peaks in unit time, where n is a real number, and when the four conditions are simultaneously satisfied, the detected intermediate frequency noise.
Optionally, the apparatus further includes a preprocessing module, which is configured to preprocess the input detection signal to obtain the cardiac signal.
In a third aspect, an embodiment of the present invention provides a medical detection apparatus, which includes any one of the noise detection devices described above.
By implementing the embodiment of the invention, the characteristic parameters of the electrocardiosignal can be calculated, and when the characteristic parameters meet the condition of intermediate frequency noise, the detected intermediate frequency noise is output to remind a doctor that the intermediate frequency noise exists in the electrocardiosignal, thereby preventing the doctor from misdiagnosing the patient.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of one embodiment of a noise detection method of the present invention;
FIG. 2 is a flow chart of another embodiment of a noise detection method of the present invention;
FIG. 3 is a schematic structural diagram of an embodiment of a noise detection apparatus according to the present invention;
fig. 4 is a schematic structural diagram of another embodiment of the noise detection device of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It is to be understood that the terminology used in the embodiments of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, fig. 1 is a flow chart of an embodiment of the noise detection method of the present invention. The method comprises the following steps:
step S110: and calculating the input electrocardiosignals to obtain the characteristic parameters of the electrocardiosignals.
Specifically, because the intermediate frequency noise and the normal electrocardiosignal are mixed together, the intermediate frequency noise is difficult to be filtered by a filter filtering method, and in order to identify whether the electrocardiosignal has the intermediate frequency noise, the input electrocardiosignal needs to be calculated to obtain the characteristic parameters of the electrocardiosignal.
Step S120: and judging whether the characteristic parameters meet the intermediate frequency noise condition, and if so, outputting the detected intermediate frequency noise.
Specifically, if the characteristic parameter includes the number of peaks in unit time, the number of changes in the width direction of adjacent peaks, the maximum peak interval regularity parameter, and the minimum peak interval regularity parameter, the intermediate frequency noise condition may be set according to the characteristic parameter as a condition that the number of peaks in unit time is greater than or equal to a fourth threshold, a ratio obtained by dividing the number of changes in the width direction of adjacent peaks by the number of peaks in unit time is greater than a fifth threshold, and both the maximum peak interval regularity parameter and the minimum peak interval regularity parameter are less than a sixth threshold. If the statistical characteristic parameters all meet the three conditions, the detected intermediate frequency noise is output.
Wherein, the number of wave crests in unit time is: the number of peaks in the most recent unit time.
The number of changes in the width direction of adjacent peaks is: two adjacent peaks are paired, and the width change direction (from wide to narrow or from narrow to wide) of the pair of peaks is determined, thereby determining the number of changes per pair of peaks in the width direction per unit time.
The maximum peak interval regularity parameters are: searching peaks in unit time, taking three adjacent peaks as a group, finding the maximum peak in each group, calculating the interval of the maximum peaks in the two adjacent groups to be recorded as the maximum peak interval, then calculating the regularity parameter in the peak interval in the unit time, wherein the initial value is 0, if the interval of the maximum peaks in the adjacent groups is less than 10%, adding 1 to the regularity parameter, and if not, subtracting 1, counting the accumulated sum of the regularity parameter in a time period to be recorded as the maximum peak interval regularity parameter.
The minimum peak interval regularity parameters are: searching peaks in unit time, taking three adjacent peaks as a group, finding the minimum peak in each group, calculating the interval of the minimum peaks in the two adjacent groups, recording the interval as the minimum peak interval, calculating the regularity parameter in the peak interval in the unit time, wherein the initial value is 0, if the interval of the adjacent minimum peaks is less than 10%, adding 1 to the regularity parameter, and if not, subtracting 1, counting the accumulated sum of the regularity parameter in a time period, and recording the accumulated sum as the minimum peak interval regularity parameter.
It is understood that, in this embodiment, the fourth threshold may be set to any value between 17 and 19, the fifth threshold may be set to any value between 0.8 and 1.2, and the sixth threshold may be set to any value between-6 and-4, and in other embodiments, the values of the fourth threshold, the fifth threshold, and the sixth threshold may also be set manually according to actual situations, and the present invention is not limited in particular.
Or, if the characteristic parameter includes a sum of squared amplitudes per unit time, a sum of squared amplitudes threshold, a number of peaks per unit time, a threshold for a number of peaks per unit time, a baseline wander noise proportionality coefficient, and a ratio of a number of peaks per unit time, the intermediate frequency noise condition may be set as a condition that the sum of squared amplitudes per unit time is greater than m times the sum of squared amplitudes per unit time threshold, the number of peaks per unit time is greater than or equal to a seventh threshold, a difference between the number of peaks per unit time and the threshold for the number of peaks per unit time is greater than an eighth threshold, the baseline wander noise proportionality coefficient is less than a ninth threshold, and the ratio of the number of peaks per unit time is less than a tenth threshold. If the statistical characteristic parameters all meet the five conditions, the detected intermediate frequency noise is output.
Wherein, the sum of the squares of the unit time amplitudes is: the sum of the squares of the 5hz high pass filtered data per unit time.
The sum of squared amplitudes threshold is: the sum of the squared amplitudes is counted once per unit time, and the average of the sum of the squared amplitudes over a period of time is calculated as the sum of the squared amplitudes threshold.
The number of wave crests in unit time is: the number of peaks in the most recent unit time.
The threshold value of the number of peaks in unit time is as follows: and counting the number of primary peaks per unit time, and calculating the average value of the number of peaks in a period of time as a threshold value of the number of peaks in the unit time.
The baseline drift noise scale factor is: the ratio of the sum of the squares of the 0.1 hz high-pass filtered data to the sum of the squares of the 5hz high-pass filtered data for the current unit of time.
The ratio of the width peak number per unit time is as follows: and after the wave crests are detected, judging the width of each wave crest, recording the wave crest as a wide peak when the width of the wave crest is more than 35 milliseconds, counting the number of the wide peaks in the current unit time, and calculating the ratio of the number of the wide peaks to the number of the wave peaks in the current unit time.
It is understood that in the present embodiment, m may be set to any value between 1.1 and 1.3, the seventh threshold may be set to any value between 7 and 9, the eighth threshold may be set to any value between 5 and 7, the ninth threshold may be set to any value between 0.93 and 0.95, and the tenth threshold may be set to any value between 0.43 and 0.47, and in other embodiments, the values of m, the seventh threshold, the eighth threshold, the ninth threshold, and the tenth threshold may also be set artificially according to the actual situation, and the present invention is not limited in particular.
Or, if the characteristic parameter includes the number of peaks per unit time, the baseline wandering noise proportionality coefficient, the number of times that the number of adjacent peaks per unit time does not change, and the threshold of the number of peaks per unit time, the condition of the intermediate frequency noise may be set as four conditions that the number of peaks per unit time is greater than or equal to the eleventh threshold, the baseline wandering noise proportionality coefficient is greater than the twelfth threshold, the number of times that the number of adjacent peaks per unit time does not change is less than the thirteenth threshold, and the number of peaks per unit time is greater than n times the threshold of the number of peaks per unit time, where n. If the statistical characteristic parameters all meet the four conditions, the detected intermediate frequency noise is output.
Wherein, the number of wave crests in unit time is: the number of peaks in the most recent unit time.
The baseline drift noise scale factor is: the ratio of the sum of the squares of the 0.1 hz high-pass filtered data to the sum of the squares of the 5hz high-pass filtered data for the current unit of time.
The number of unchanged times of the wave crests of adjacent unit time is as follows: two adjacent peaks are paired, and the width change direction (from wide to narrow or from narrow to wide) of the pair of peaks is determined, thereby determining the number of changes per pair of peaks in the width direction per unit time.
The threshold value of the number of peaks in unit time is as follows: and counting the number of primary peaks per unit time, and calculating the average value of the number of peaks in a period of time as a threshold value of the number of peaks in the unit time.
It is understood that, in the present embodiment, the eleventh threshold may be set to any value between 7 and 9, the twelfth threshold may be set to any value between 5 and 7, the thirteenth threshold may be set to 0.93 and 0.95, and n may be set to any value between 1.1 and 1.3, and in other embodiments, the values of the eleventh threshold, the twelfth threshold, the thirteenth threshold, and n may also be set manually according to actual conditions, and the present invention is not limited in particular.
In addition, the characteristic parameters may further include parameters such as amplitude probability density, and the like, and the condition of the if noise may also be flexibly set according to the selection of the characteristic parameters, and the present invention is not particularly limited.
By implementing the embodiment of the invention, the characteristic parameters of the electrocardiosignal can be calculated, and when the characteristic parameters meet the condition of intermediate frequency noise, the detected intermediate frequency noise is output to remind a doctor that the intermediate frequency noise exists in the electrocardiosignal, thereby preventing the doctor from misdiagnosing the patient.
Referring to fig. 2, fig. 2 is a flow chart of another embodiment of the noise detection method of the present invention. The method comprises the following steps:
step S210: and preprocessing the input detection signal to obtain an electrocardiosignal.
Specifically, when the device detects a subject, due to power frequency interference, myoelectricity interference, baseline drift noise and the like, a detection signal detected by the device includes a power frequency interference signal, a myoelectricity interference signal, baseline drift noise and the like in addition to a normal cardiac electricity signal. Therefore, in order to eliminate the influence of these interference signals and noise, the input detection signal needs to be preprocessed to obtain the electrocardiographic signal.
Since the power frequency interference signals are usually 50 hz and 60 hz signals, band-stop filters with bandwidths of 48 to 52 hz may be respectively set to filter the power frequency interference signals with a frequency of 50 hz, and band-stop filters with bandwidths of 58 to 62 hz may be respectively set to filter the power frequency interference signals with a frequency of 60 hz. Since the electromyographic interference signal is typically a high frequency signal, a low pass filter with a cut-off frequency of 41.7 hz may be provided to filter the electromyographic interference signal. The baseline wander noise is usually a low-frequency signal, and therefore, the baseline wander noise can be filtered by setting a high-pass filter with a cutoff frequency of 0.1 hz and a cutoff frequency of 5 hz.
If a cardiac pacemaker is arranged in the body of the tested person, the cardiac pacemaker can generate a spike pulse when sending a pulse signal to stimulate the heart to jump, so that the tested signal also carries the spike pulse. Therefore, at this time, a morphological filter is further required to be arranged and the spike pulse is filtered by the morphological filter, so as to prevent the spike pulse from being mistakenly regarded as a normal electrocardiosignal, and further to prevent the false judgment of a doctor.
Step S220: and calculating the input electrocardiosignals to obtain the characteristic parameters of the electrocardiosignals.
Specifically, because the intermediate frequency noise and the normal electrocardiosignal are mixed together, the intermediate frequency noise and the normal electrocardiosignal are difficult to be filtered by a filter filtering method, and in order to identify whether the electrocardiosignal has noise, the input electrocardiosignal needs to be calculated to obtain the characteristic parameters of the electrocardiosignal.
Step S230: and judging whether the characteristic parameters meet the intermediate frequency noise condition, and if so, outputting the detected intermediate frequency noise.
Specifically, if the characteristic parameter includes the number of peaks in unit time, the number of changes in the width direction of adjacent peaks, the maximum peak interval regularity parameter, and the minimum peak interval regularity parameter, the intermediate frequency noise condition may be set according to the characteristic parameter as a condition that the number of peaks in unit time is greater than or equal to a fourth threshold, a ratio obtained by dividing the number of changes in the width direction of adjacent peaks by the number of peaks in unit time is greater than a fifth threshold, and both the maximum peak interval regularity parameter and the minimum peak interval regularity parameter are less than a sixth threshold. If the statistical characteristic parameters all meet the three conditions, the detected intermediate frequency noise is output.
Wherein, the number of wave crests in unit time is: the number of peaks in the most recent unit time.
The number of changes in the width direction of adjacent peaks is: two adjacent peaks are paired, and the width change direction (from wide to narrow or from narrow to wide) of the pair of peaks is determined, thereby determining the number of changes per pair of peaks in the width direction per unit time.
The maximum peak interval regularity parameters are: searching peaks in unit time, taking three adjacent peaks as a group, finding the maximum peak in each group, calculating the interval of the maximum peaks in the two adjacent groups to be recorded as the maximum peak interval, then calculating the regularity parameter in the peak interval in the unit time, wherein the initial value is 0, if the interval of the maximum peaks in the adjacent groups is less than 10%, adding 1 to the regularity parameter, and if not, subtracting 1, counting the accumulated sum of the regularity parameter in a time period to be recorded as the maximum peak interval regularity parameter.
The minimum peak interval regularity parameters are: searching peaks in unit time, taking three adjacent peaks as a group, finding the minimum peak in each group, calculating the interval of the minimum peaks in the two adjacent groups, recording the interval as the minimum peak interval, calculating the regularity parameter in the peak interval in the unit time, wherein the initial value is 0, if the interval of the adjacent minimum peaks is less than 10%, adding 1 to the regularity parameter, and if not, subtracting 1, counting the accumulated sum of the regularity parameter in a time period, and recording the accumulated sum as the minimum peak interval regularity parameter.
It is understood that, in this embodiment, the fourth threshold may be set to any value between 17 and 19, the fifth threshold may be set to any value between 0.8 and 1.2, and the sixth threshold may be set to any value between-6 and-4, and in other embodiments, the values of the fourth threshold, the fifth threshold, and the sixth threshold may also be set manually according to actual situations, and the present invention is not limited in particular.
Or, if the characteristic parameter includes a sum of squared amplitudes per unit time, a sum of squared amplitudes threshold, a number of peaks per unit time, a threshold for a number of peaks per unit time, a baseline wander noise proportionality coefficient, and a ratio of a number of peaks per unit time, the intermediate frequency noise condition may be set as a condition that the sum of squared amplitudes per unit time is greater than m times the sum of squared amplitudes per unit time threshold, the number of peaks per unit time is greater than or equal to a seventh threshold, a difference between the number of peaks per unit time and the threshold for the number of peaks per unit time is greater than an eighth threshold, the baseline wander noise proportionality coefficient is less than a ninth threshold, and the ratio of the number of peaks per unit time is less than a tenth threshold. If the statistical characteristic parameters all meet the five conditions, the detected intermediate frequency noise is output.
Wherein, the sum of the squares of the unit time amplitudes is: the sum of the squares of the 5hz high pass filtered data per unit time.
The sum of squared amplitudes threshold is: the sum of the squared amplitudes is counted once per unit time, and the average of the sum of the squared amplitudes over a period of time is calculated as the sum of the squared amplitudes threshold.
The number of wave crests in unit time is: the number of peaks in the most recent unit time.
The threshold value of the number of peaks in unit time is as follows: and counting the number of primary peaks per unit time, and calculating the average value of the number of peaks in a period of time as a threshold value of the number of peaks in the unit time.
The baseline drift noise scale factor is: the ratio of the sum of the squares of the 0.1 hz high-pass filtered data to the sum of the squares of the 5hz high-pass filtered data for the current unit of time.
The ratio of the width peak number per unit time is as follows: and after the wave crests are detected, judging the width of each wave crest, recording the wave crest as a wide peak when the width of the wave crest is more than 35 milliseconds, counting the number of the wide peaks in the current unit time, and calculating the ratio of the number of the wide peaks to the number of the wave peaks in the current unit time.
It is understood that in the present embodiment, m may be set to any value between 1.1 and 1.3, the seventh threshold may be set to any value between 7 and 9, the eighth threshold may be set to any value between 5 and 7, the ninth threshold may be set to any value between 0.92 and 0.96, and the tenth threshold may be set to any value between 0.43 and 0.47, and in other embodiments, the values of m, the seventh threshold, the eighth threshold, the ninth threshold, and the tenth threshold may also be set artificially according to the actual situation, and the present invention is not limited in particular.
Or, if the characteristic parameter includes the number of peaks per unit time, the baseline wandering noise proportionality coefficient, the number of times that the number of adjacent peaks per unit time does not change, and the threshold of the number of peaks per unit time, the condition of the intermediate frequency noise may be set as four conditions that the number of peaks per unit time is greater than or equal to the eleventh threshold, the baseline wandering noise proportionality coefficient is greater than the twelfth threshold, the number of times that the number of adjacent peaks per unit time does not change is less than the thirteenth threshold, and the number of peaks per unit time is greater than n times the threshold of the number of peaks per unit time, where n. If the statistical characteristic parameters all meet the four conditions, the detected intermediate frequency noise is output.
Wherein, the number of wave crests in unit time is: the number of peaks in the most recent unit time.
The baseline drift noise scale factor is: the ratio of the sum of the squares of the 0.1 hz high-pass filtered data to the sum of the squares of the 5hz high-pass filtered data for the current unit of time.
The number of unchanged times of the wave crests of adjacent unit time is as follows: two adjacent peaks are paired, and the width change direction (from wide to narrow or from narrow to wide) of the pair of peaks is determined, thereby determining the number of changes per pair of peaks in the width direction per unit time.
The threshold value of the number of peaks in unit time is as follows: and counting the number of primary peaks per unit time, and calculating the average value of the number of peaks in a period of time as a threshold value of the number of peaks in the unit time.
It is understood that, in the present embodiment, the eleventh threshold may be set to any value between 7 and 9, the twelfth threshold may be set to any value between 5 and 7, the thirteenth threshold may be set to any value between 0.92 and 0.96, and n may be set to any value between 1.1 and 1.3, and in other embodiments, the values of the eleventh threshold, the twelfth threshold, the thirteenth threshold, and n may also be set manually according to the actual situation, and the present invention is not limited in particular.
In addition, the characteristic parameters may further include parameters such as amplitude probability density, and the like, and the condition of the if noise may also be flexibly set according to the selection of the characteristic parameters, and the present invention is not particularly limited.
Step S240: and judging whether the characteristic parameters meet the baseline drift noise condition, and outputting the detected baseline drift noise if the characteristic parameters meet the baseline drift noise condition.
Specifically, since the baseline wander noise and the normal electrocardiographic signal are usually mixed together in addition to the intermediate frequency noise, it is difficult to filter the intermediate frequency noise by a filter filtering method, and in order to identify whether the electrocardiographic signal has the baseline wander noise, it is necessary to determine whether the characteristic parameter satisfies the baseline wander noise condition.
If the characteristic parameters include the number of times of no change of the number of peaks in adjacent unit time and the baseline wander noise proportionality coefficient, the baseline wander noise condition may be set to be two conditions that the number of times of no change of the number of peaks in adjacent unit time is smaller than a first threshold value and the baseline wander noise proportionality coefficient is larger than a second threshold value. If the statistical characteristic parameters both satisfy the above two conditions, the output detects baseline drift noise.
Wherein, the number of unchanged times of the wave crests of adjacent unit time is as follows: two adjacent peaks are paired, and the width change direction (from wide to narrow or from narrow to wide) of the pair of peaks is determined, thereby determining the number of changes per pair of peaks in the width direction per unit time.
The baseline drift noise scale factor is: the ratio of the sum of the squares of the 0.1 hz high-pass filtered data to the sum of the squares of the 5hz high-pass filtered data for the current unit of time.
It is understood that, in this embodiment, the first threshold may be set to any value between 4.5 and 5.5, the second threshold may be set to any value between 9 and 11, and in other embodiments, the values of the first threshold and the second threshold may also be set artificially according to actual situations, and the present invention is not limited in particular.
Alternatively, if the characteristic parameter includes a baseline wander noise scale factor, the baseline wander noise condition may be set such that the baseline wander noise scale factor is greater than a third threshold. And if the statistical characteristic parameters meet the conditions, outputting the detected baseline drift noise.
Wherein, the baseline drift noise proportionality coefficient is: the ratio of the sum of the squares of the 0.1 hz high-pass filtered data to the sum of the squares of the 5hz high-pass filtered data for the current unit of time.
It is understood that, in this embodiment, the third threshold may be set to any value between 18 and 22, and in other embodiments, the value of the third threshold may also be set artificially according to actual conditions, and the present invention is not limited in particular.
The characteristic parameters may include other parameters, and the condition of the baseline shift noise may be flexibly set according to the selection of the characteristic parameters, and the present invention is not particularly limited.
By implementing the embodiment of the invention, the characteristic parameters of the electrocardiosignal can be calculated, and when the characteristic parameters meet the condition of intermediate frequency noise, the detected intermediate frequency noise is output to remind a doctor that the intermediate frequency noise exists in the electrocardiosignal, thereby preventing the doctor from misdiagnosing the patient.
In addition, in the embodiment, whether the characteristic parameters meet the baseline drift noise condition can be judged, and when the characteristic parameters meet the baseline drift noise condition, the detected baseline drift noise is output to remind a doctor that the baseline drift noise exists in the electrocardiosignal, so that the doctor is prevented from misdiagnosing the patient. And before the characteristic parameters are counted, the detection signals are preprocessed, some interference signals are filtered, and the detection accuracy is improved.
While the method of the embodiments of the present invention has been described in detail, in order to better implement the above-described aspects of the embodiments of the present invention, the following also provides the related apparatus for implementing the aspects.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a noise detection apparatus according to an embodiment of the present invention. The noise detection apparatus 300 includes: a calculation module 310 and a first determination module 320.
The calculating module 310 is configured to calculate an input electrocardiographic signal to obtain a characteristic parameter of the electrocardiographic signal;
the first determining module 320 is configured to determine whether the characteristic parameter meets an intermediate frequency noise condition, and if the characteristic parameter meets the intermediate frequency noise condition, output a detected intermediate frequency noise.
Optionally, the characteristic parameters include the number of peaks per unit time, the number of changes in the width direction of adjacent peaks, a maximum peak interval regularity parameter, and a minimum peak interval regularity parameter,
the first determining module 320 is specifically configured to determine whether three conditions, that is, whether the number of peaks in a unit time is greater than or equal to a fourth threshold, a ratio obtained by dividing the number of changes in the width direction of adjacent peaks by the number of peaks in the unit time is greater than a fifth threshold, and both a maximum peak interval regularity parameter and a minimum peak interval regularity parameter are less than a sixth threshold, are met at the same time, and output the detected intermediate frequency noise;
or, the characteristic parameters include amplitude square sum per unit time, amplitude square sum threshold, number of peaks per unit time, threshold of number of peaks per unit time, baseline wander noise ratio, sum, ratio of wide peaks per unit time,
the first determining module 320 is specifically configured to determine whether the sum of squared amplitudes of unit time is greater than m times the sum of squared amplitudes of the unit time and a threshold value, the number of peaks of unit time is greater than or equal to a seventh threshold value, a difference between the number of peaks of unit time and the threshold value of the number of peaks of unit time is greater than an eighth threshold value, a baseline drift noise proportionality coefficient is less than a ninth threshold value, and the ratio of the number of peaks of unit time is less than a tenth threshold value, where m is a real number, and when the five conditions are simultaneously satisfied, output that the intermediate frequency noise is detected;
or, the characteristic parameters include the number of peaks per unit time, the baseline wander noise proportionality coefficient, the number of times that the number of adjacent peaks per unit time does not change, and, the threshold value of the number of peaks per unit time,
the first determining module 320 is specifically configured to determine whether four conditions that the number of peaks in unit time is greater than or equal to an eleventh threshold, the baseline drift noise proportionality coefficient is greater than a twelfth threshold, the number of times that the number of peaks in adjacent unit times is not changed is less than a thirteenth threshold, and the number of peaks in unit time is greater than n times the threshold of the number of peaks in unit time are met, where n is a real number, and when the four conditions are met, the detected intermediate-frequency noise is output.
The noise detection apparatus 300 shown in fig. 3 may perform each step of the method shown in fig. 1, please refer to fig. 1 and the related description, which are not repeated herein.
By implementing the embodiment of the invention, the characteristic parameters of the electrocardiosignal can be calculated, and when the characteristic parameters meet the condition of intermediate frequency noise, the detected intermediate frequency noise is output to remind a doctor that the intermediate frequency noise exists in the electrocardiosignal, thereby preventing the doctor from misdiagnosing the patient.
Referring to fig. 4, fig. 4 is a schematic structural diagram of another embodiment of the noise detection apparatus of the present invention. The noise detection apparatus 400 includes: a preprocessing module 410, a calculating module 420, a first judging module 430 and a second judging module 440.
The preprocessing module 410 is configured to preprocess the input detection signal to obtain an electrocardiographic signal;
the calculating module 420 is configured to calculate an input electrocardiographic signal to obtain a characteristic parameter of the electrocardiographic signal;
the first judging module 430 is configured to judge whether the characteristic parameter meets an intermediate frequency noise condition, and if the characteristic parameter meets the intermediate frequency noise condition, output a detected intermediate frequency noise;
the second determining module 440 is configured to determine whether the characteristic parameter satisfies a baseline wander noise condition, and output a detected baseline wander noise if the characteristic parameter satisfies the baseline wander noise condition.
Optionally, the characteristic parameters include the number of times of unchanged peaks of adjacent unit time and the baseline drift noise proportionality coefficient,
the second determining module 440 is specifically configured to determine whether two conditions that the number of unchanged times of the peaks in adjacent unit times is smaller than a first threshold and the baseline wander noise proportionality coefficient is larger than a second threshold are met at the same time, and output the detected baseline wander noise when the two conditions are met at the same time;
or, the characteristic parameter includes a baseline drift noise scale factor,
the second determining module 440 is specifically configured to determine whether the baseline wander noise proportionality coefficient is greater than a third threshold, and output the detected baseline wander noise when the baseline wander noise proportionality coefficient is greater than the third threshold.
Optionally, the characteristic parameters include the number of peaks per unit time, the number of changes in the width direction of adjacent peaks, a maximum peak interval regularity parameter, and a minimum peak interval regularity parameter,
the first determining module 430 is specifically configured to determine whether three conditions that the number of peaks in a unit time is greater than or equal to a fourth threshold, a ratio obtained by dividing the number of changes in the width direction of adjacent peaks by the number of peaks in the unit time is greater than a fifth threshold, and both the maximum peak interval regularity parameter and the minimum peak interval regularity parameter are less than a sixth threshold are met, and when the three conditions are met, the detected intermediate frequency noise is output;
or, the characteristic parameters include amplitude square sum per unit time, amplitude square sum threshold, number of peaks per unit time, threshold of number of peaks per unit time, baseline wander noise ratio, sum, ratio of wide peaks per unit time,
the first determining module 430 is specifically configured to determine whether the sum of squared amplitudes of unit time is greater than m times the sum of squared amplitudes of the unit time and a threshold value, the number of peaks of unit time is greater than or equal to a seventh threshold value, a difference between the number of peaks of unit time and the threshold value of the number of peaks of unit time is greater than an eighth threshold value, a baseline drift noise proportionality coefficient is less than a ninth threshold value, and the ratio of the number of peaks of unit time is less than a tenth threshold value, where m is a real number, and when the five conditions are simultaneously satisfied, output that the intermediate-frequency noise is detected;
or, the characteristic parameters include the number of peaks per unit time, the baseline wander noise proportionality coefficient, the number of times that the number of adjacent peaks per unit time does not change, and, the threshold value of the number of peaks per unit time,
the first determining module 430 is specifically configured to determine whether four conditions that the number of peaks in unit time is greater than or equal to an eleventh threshold, the baseline drift noise proportionality coefficient is greater than a twelfth threshold, the number of times that the number of peaks in adjacent unit times is not changed is less than a thirteenth threshold, and the number of peaks in unit time is greater than n times the threshold of the number of peaks in unit time are met, where n is a real number, and when the four conditions are met, the detected intermediate-frequency noise is output.
The noise detection apparatus 400 shown in fig. 4 may perform the steps of the methods shown in fig. 1 and fig. 2, please refer to fig. 1 and fig. 2 and the related description, which are not repeated herein.
By implementing the embodiment of the invention, the characteristic parameters of the electrocardiosignal can be calculated, and when the characteristic parameters meet the condition of intermediate frequency noise, the detected intermediate frequency noise is output to remind a doctor that the intermediate frequency noise exists in the electrocardiosignal, thereby preventing the doctor from misdiagnosing the patient.
In addition, in the embodiment, whether the characteristic parameters meet the baseline drift noise condition can be judged, and when the characteristic parameters meet the baseline drift noise condition, the detected baseline drift noise is output to remind a doctor that the baseline drift noise exists in the electrocardiosignal, so that the doctor is prevented from misdiagnosing the patient. And before the characteristic parameters are counted, the detection signals are preprocessed, some interference signals are filtered, and the detection accuracy is improved.
The present invention further includes a medical detection device, which includes the noise detection apparatus, please refer to fig. 3, fig. 4 and related descriptions, which are not further described herein.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (11)
1. A noise detection method, comprising:
preprocessing an input detection signal to obtain an electrocardiosignal;
calculating the input electrocardiosignals to obtain characteristic parameters of the electrocardiosignals, wherein the characteristic parameters at least comprise the number of wave crests in unit time; the characteristic parameters also comprise the change times of the width direction of adjacent wave peaks, a maximum wave peak interval regularity parameter and a minimum wave peak interval regularity parameter; or; the characteristic parameters further comprise unit time amplitude square sum, amplitude square sum threshold, unit time peak number threshold, baseline drift noise proportion coefficient and unit time wide peak number proportion; or, the characteristic parameters further include a baseline drift noise proportionality coefficient, the number of times that the number of adjacent unit time peaks does not change, and a threshold value of the number of unit time peaks;
and judging whether the characteristic parameters meet the intermediate frequency noise condition, and if the characteristic parameters meet the intermediate frequency noise condition, outputting the detected intermediate frequency noise.
2. The method of claim 1,
under the condition that the characteristic parameters further comprise the change times of adjacent wave peak width directions, a maximum wave peak interval regularity parameter and a minimum wave peak interval regularity parameter, judging whether the characteristic parameters meet an intermediate frequency noise condition, and if the characteristic parameters meet the intermediate frequency noise condition, outputting the detected intermediate frequency noise to comprise:
judging whether the conditions that the number of wave crests in unit time is larger than or equal to a fourth threshold value, the ratio of the change times of the width direction of adjacent wave crests divided by the number of wave crests in unit time is larger than a fifth threshold value, and the maximum wave crest interval regularity parameter and the minimum wave crest interval regularity parameter are both smaller than a sixth threshold value are met;
if three conditions are met simultaneously, outputting the detected intermediate frequency noise;
or, under the condition that the characteristic parameters further include a unit time amplitude square sum, an amplitude square sum threshold, a unit time peak number threshold, a baseline drift noise proportion coefficient, and a unit time wide peak number proportion, the judging whether the characteristic parameters satisfy the intermediate frequency noise condition, and if the characteristic parameters satisfy the intermediate frequency noise condition, outputting the detected intermediate frequency noise includes:
judging whether the sum of the squares of the amplitudes of the unit time is larger than m times of the sum of the squares of the amplitudes, the number of the wave crests of the unit time is larger than or equal to a seventh threshold value, the difference between the number of the wave crests of the unit time and the threshold value of the number of the wave crests of the unit time is larger than an eighth threshold value, the base line drift noise proportion coefficient is smaller than a ninth threshold value, and the proportion of the wide wave crests of the unit time is smaller than a tenth threshold value, wherein m is a real number;
if the five conditions are met simultaneously, outputting the detected intermediate frequency noise;
or, under the condition that the characteristic parameter further includes a baseline drift noise proportionality coefficient, the number of adjacent unit time peaks does not change for a certain time, and a unit time peak number threshold, the judging whether the characteristic parameter satisfies an intermediate frequency noise condition, and if the characteristic parameter satisfies the intermediate frequency noise condition, outputting the detected intermediate frequency noise includes:
judging whether the conditions that the number of the wave crests in unit time is greater than or equal to an eleventh threshold, the baseline drift noise proportionality coefficient is greater than a twelfth threshold, the number of unchanged times of the adjacent wave crests in unit time is less than a thirteenth threshold, and the number of the wave crests in unit time is greater than n times of the threshold of the number of the wave crests in unit time are met simultaneously, wherein n is a real number;
if the four conditions are simultaneously satisfied, the detected intermediate frequency noise is output.
3. The method of claim 1, wherein the determining whether the characteristic parameter satisfies an if noise condition, and outputting, after detecting if the characteristic parameter satisfies the if noise condition, further comprises:
reminding doctors that intermediate frequency noise exists in the electrocardiosignals.
4. A noise detection method, comprising:
preprocessing an input detection signal to obtain an electrocardiosignal;
calculating the input electrocardiosignals to obtain characteristic parameters of the electrocardiosignals, wherein the characteristic parameters comprise a first characteristic parameter and a second characteristic parameter;
judging whether the first characteristic parameters meet baseline drift noise conditions or not, wherein the first characteristic parameters at least comprise baseline drift noise proportion parameters; outputting a detected baseline wander noise if the first characteristic parameter satisfies the baseline wander noise condition;
judging whether the second characteristic parameter meets an intermediate frequency noise condition, wherein the second characteristic parameter at least comprises the number of wave crests in unit time; if the second characteristic parameter meets the intermediate frequency noise condition, outputting the detected intermediate frequency noise, wherein the second characteristic parameter also comprises the change times of adjacent wave peak width directions, a maximum wave peak interval regularity parameter and a minimum wave peak interval regularity parameter; or; the second characteristic parameters further comprise unit time amplitude square sum, amplitude square sum threshold, unit time peak number threshold, baseline drift noise proportional coefficient and unit time wide peak number proportion; or, the second characteristic parameter further includes a baseline drift noise proportionality coefficient, a number of times that the number of adjacent unit time peaks does not change, and a threshold value of the number of unit time peaks.
5. The method of claim 4, wherein the first characteristic parameter further includes a number of times that a number of peaks adjacent to the first characteristic parameter does not change, wherein the determining whether the first characteristic parameter satisfies a baseline shift noise condition, and wherein outputting the detected baseline shift noise if the first characteristic parameter satisfies the baseline shift noise condition comprises:
judging whether two conditions that the number of unchanged times of the wave crests of adjacent unit time is smaller than a first threshold value and the baseline drift noise proportional coefficient is larger than a second threshold value are met simultaneously;
outputting the detected baseline wander noise if the two conditions are satisfied simultaneously;
or, the determining whether the first characteristic parameter satisfies a baseline wander noise condition, and if the first characteristic parameter satisfies the baseline wander noise condition, outputting the detected baseline wander noise includes:
judging whether the baseline drift noise proportionality coefficient is larger than a third threshold value;
if the baseline wander noise scaling factor is greater than a third threshold, then the output detects baseline wander noise.
6. A noise detection apparatus, comprising: a preprocessing module, a calculating module and a first judging module,
the preprocessing module is used for preprocessing the input detection signal to obtain an electrocardiosignal;
the calculating module is used for calculating the input electrocardiosignals to obtain characteristic parameters of the electrocardiosignals, wherein the characteristic parameters at least comprise the number of wave crests in unit time;
the first judging module is used for judging whether the characteristic parameter meets an intermediate frequency noise condition, and if the characteristic parameter meets the intermediate frequency noise condition, outputting the detected intermediate frequency noise; the characteristic parameters also comprise the change times of the width direction of adjacent wave peaks, a maximum wave peak interval regularity parameter and a minimum wave peak interval regularity parameter; or; the characteristic parameters further comprise unit time amplitude square sum, amplitude square sum threshold, unit time peak number threshold, baseline drift noise proportion coefficient and unit time wide peak number proportion; or, the characteristic parameters further include a baseline drift noise proportionality coefficient, the number of times that the number of adjacent unit time peaks does not change, and a threshold value of the number of unit time peaks.
7. The apparatus of claim 6,
the first judging module is specifically configured to judge whether three conditions that the number of peaks in a unit time is greater than or equal to a fourth threshold, a ratio obtained by dividing the number of peaks in the unit time by the number of changes in the width direction of adjacent peaks is greater than a fifth threshold, and the maximum peak interval regularity parameter and the minimum peak interval regularity parameter are both less than a sixth threshold are met simultaneously under the condition that the characteristic parameters further include the number of changes in the width direction of adjacent peaks, the maximum peak interval regularity parameter, and the minimum peak interval regularity parameter, and when the three conditions are met simultaneously, output that intermediate frequency noise is detected;
or, the first determining module is specifically configured to determine whether the sum of squared amplitudes of unit time, the sum of squared amplitudes of the threshold, the threshold of the number of peaks of unit time, the baseline wander noise proportionality coefficient, and the ratio of the number of wide peaks of unit time are simultaneously satisfied under the condition that the characteristic parameters further include the sum of squared amplitudes of unit time, the threshold of the number of peaks of unit time, the baseline wander noise proportionality coefficient, and the ratio of the number of wide peaks of unit time, where m is a real number, and when the five conditions are simultaneously satisfied, output that the intermediate-frequency noise is detected, where the sum of squared amplitudes of unit time and the sum of squared amplitudes of unit time are greater than m times the sum of squared amplitudes of squared peaks of unit time, the number of peaks of unit time is greater than or equal;
or, the first determining module is specifically configured to determine whether four conditions that the number of peaks in unit time is greater than or equal to an eleventh threshold, the baseline wander noise proportionality coefficient is greater than a twelfth threshold, the number of peaks in adjacent unit time is less than a thirteenth threshold, and the number of peaks in unit time is greater than n times the threshold of the number of peaks in unit time are met simultaneously, where n is a real number, and when the four conditions are met simultaneously, the detected intermediate frequency noise is output.
8. The apparatus of claim 6, wherein after the output detects the if noise, further comprising:
reminding doctors that intermediate frequency noise exists in the electrocardiosignals.
9. A noise detection apparatus, comprising: a preprocessing module, a calculating module, a first judging module and a second judging module,
the preprocessing module is used for preprocessing the input detection signal to obtain an electrocardiosignal;
the calculating module is used for calculating the input electrocardiosignals to obtain characteristic parameters of the electrocardiosignals, wherein the characteristic parameters comprise a first characteristic parameter and a second characteristic parameter;
the second judging module is used for judging whether the first characteristic parameter meets a baseline drift noise condition, wherein the first characteristic parameter at least comprises a baseline drift noise proportion parameter; outputting a detected baseline wander noise if the first characteristic parameter satisfies the baseline wander noise condition;
the first judging module is used for judging whether the second characteristic parameter meets an intermediate frequency noise condition, wherein the second characteristic parameter at least comprises the number of wave crests in unit time; if the second characteristic parameter meets the intermediate frequency noise condition, outputting the detected intermediate frequency noise; the second characteristic parameters also comprise the change times of the width direction of adjacent wave peaks, a maximum wave peak interval regularity parameter and a minimum wave peak interval regularity parameter; or; the second characteristic parameters further comprise unit time amplitude square sum, amplitude square sum threshold, unit time peak number threshold, baseline drift noise proportional coefficient and unit time wide peak number proportion; or, the second characteristic parameter further includes a baseline drift noise proportionality coefficient, a number of times that the number of adjacent unit time peaks does not change, and a threshold value of the number of unit time peaks.
10. The apparatus of claim 9,
the first characteristic parameter also comprises the number of times that the number of adjacent peaks in unit time does not change,
the second judging module is specifically configured to judge whether two conditions that the number of unchanged times of the peaks of adjacent unit times is smaller than a first threshold and the baseline wander noise proportionality coefficient is larger than a second threshold are met at the same time, and output the detected baseline wander noise when the two conditions are met at the same time;
or the like, or, alternatively,
the second judging module is specifically configured to judge whether the baseline wander noise proportionality coefficient is greater than a third threshold, and output the detected baseline wander noise when the baseline wander noise proportionality coefficient is greater than the third threshold.
11. A medical examination device, characterized in that the medical examination device comprises a noise detection means according to any of claims 6-10.
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| CN107224284A (en) * | 2016-03-25 | 2017-10-03 | 深圳华清心仪医疗电子有限公司 | The noise detecting method and system of a kind of digital electrocardiosignal |
| CN105866011B (en) * | 2016-03-31 | 2018-10-26 | 艾康生物技术(杭州)有限公司 | The particle counting methods of pulse base value calculating method and blood cell analysis machine |
| CN111278353A (en) * | 2017-10-31 | 2020-06-12 | 长桑医疗（海南）有限公司 | Method and system for detecting vital sign signal noise |
| CN110575159B (en) * | 2018-06-11 | 2023-12-05 | 深圳市理邦精密仪器股份有限公司 | Signal baseline resetting method and device and monitor |
| CN109199355B (en) * | 2018-09-18 | 2021-09-28 | 深圳和而泰数据资源与云技术有限公司 | Heart rate information detection method and device and detection equipment |
| CN109157200B (en) * | 2018-10-31 | 2021-03-23 | 安徽华米信息科技有限公司 | Noise detection method and device |
| CN111743530A (en) * | 2019-03-29 | 2020-10-09 | 丽台科技股份有限公司 | Electrocardiogram signal judging device and method |
| CN110179456B (en) * | 2019-05-23 | 2021-11-02 | 中国航天员科研训练中心 | Electrocardio noise recognition model training and electrocardio noise detection method and device |
| CN115345208B (en) * | 2022-10-19 | 2023-02-03 | 成都理工大学 | Neutron-gamma pulse accumulation discrimination method based on top-hat conversion |
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