CN113171073A - Non-inductive heart rate detection method based on detector - Google Patents
Non-inductive heart rate detection method based on detector Download PDFInfo
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- CN113171073A CN113171073A CN202110539090.1A CN202110539090A CN113171073A CN 113171073 A CN113171073 A CN 113171073A CN 202110539090 A CN202110539090 A CN 202110539090A CN 113171073 A CN113171073 A CN 113171073A
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- 238000001514 detection method Methods 0.000 title abstract description 12
- 238000005311 autocorrelation function Methods 0.000 claims abstract description 17
- 238000000034 method Methods 0.000 claims abstract description 14
- 238000004364 calculation method Methods 0.000 claims abstract description 8
- 238000001914 filtration Methods 0.000 claims abstract description 5
- 238000006243 chemical reaction Methods 0.000 claims abstract description 3
- 238000005070 sampling Methods 0.000 claims description 11
- 230000001939 inductive effect Effects 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
Abstract
A method for detecting a heart rate without sensing based on a detector is characterized by comprising the following steps of S100: detecting heart vibration by using a geophone, and converting a heartbeat signal into an electric signal; s101: converting the signal detected in S100 into digital signals in an analog-to-digital mode, and dividing the signals after the analog-to-digital conversion into windows with equal length; s102: filtering the signal of S101 by a low-pass filter, and then calculating an autocorrelation function; s103: extracting a sample autocorrelation function peak value; s104: extracting heartbeat by utilizing the peak value of the autocorrelation function; s105: the heart rate is calculated. The detection method can reduce the calculation amount, does not need to be worn by a patient, and improves the detection comfort.
Description
Technical Field
The invention relates to the field of heart rate detection, in particular to a detector-based non-inductive heart rate detection.
Background
Heart rate detection during sleep is critical to ensure the health of patients as well as the elderly.
Some wearable heart rate detection methods or devices are also available in the market, but generally need to be tied to other mobile devices and need to be charged frequently, which is inconvenient for users such as the elderly. Or the sensitivity of heart rate detection equipment on the market is low at present, and when the old people sleep, the detection signal of the heart rate is very weak after covering objects such as a mattress, a quilt and the like are separated, so that the condition that the heart rate detection result is incomplete or unclear is caused.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a wave detector-based non-inductive heart rate detection method which has the advantages of reducing the calculation amount and avoiding mattress interference.
In order to achieve the above object, the present invention provides a method for detecting a heart rate without sensing by a detector, comprising the steps of,
s100: detecting heart vibration by using a geophone, and converting a heartbeat signal into an electric signal;
s101: converting the signal detected in S100 into digital signals in an analog-to-digital mode, and dividing the signals after the analog-to-digital conversion into windows with equal length;
s102: filtering the signal of S101 by a low-pass filter, and then calculating an autocorrelation function;
s103: extracting a sample autocorrelation function peak value;
s104: extracting heartbeat by utilizing the peak value of the autocorrelation function;
s105: the heart rate is calculated.
The autocorrelation function is formulated as follows:
wherein n represents the number of signal sampling points, h represents the time shift,
representing the mean value of the signal, xtExpressed as the square of the signal amplitude obtained by subtracting the mean value of the heartbeat signal from the heartbeat signal and then subtracting the mean value of the heartbeat signal from the heartbeat signal.
In S102, calculation
Derivative of and
if the difference is greater than the threshold of the preset threshold, the heartbeat peak value is between t and t +1, all points between t and t +1 are fitted through a least square method, and the peak value position is determined by the fitted heartbeat signal waveform.
In S101, each of the windows obtains a plurality of peak values in the manner in S103, and an average heartbeat interval is calculated by using the first 20% of peak values in the peak value set included in each window, and assuming that n peak values are total 20%, and the time interval between the first peak value and the nth peak value is T, the average heartbeat interval is:
in S105, assuming that there are m heart beat peaks in the window, and the time interval between the first peak and the m-th peak is S, the heart rate is bpm m/S.
Has the advantages that: 1. the approximate position of the heartbeat peak value is found through the autocorrelation function, then the points of the area near the sampling points are fitted through the least square method to obtain the heartbeat peak value, and fitting calculation is not needed to be carried out on all the sampling points.
2. The geophone is adopted to detect the heartbeat, so that the condition of weak signals caused by the interference of a mattress and the like is avoided.
Drawings
The present invention will be further described and illustrated with reference to the following drawings.
Figure 1 is a flow chart of the preferred embodiment of the invention as a whole,
Detailed Description
The technical solution of the present invention will be more clearly and completely explained by the description of the preferred embodiments of the present invention with reference to the accompanying drawings.
As shown in fig. 1, a method for detecting a heart rate without sensing based on a detector in a preferred embodiment of the present invention comprises the following steps,
s100: the heart vibration is detected by using a geophone, and a heartbeat signal is converted into an electric signal.
The invention adopts the seismic wave detector to detect the heartbeat, and can detect the tiny heartbeat signal due to the high sensitivity of the seismic wave detector, thereby avoiding the influence of the heartbeat signal on a blocking object. The detector can capture tiny signals, when the heart vibrates at a certain frequency, the generated sound waves are transmitted to the spring of the detector through the mattress, and the magnet is driven to move in the coil through the movement of the spring to generate a voltage signal.
S101: the signal detected in S100 is analog-to-digital converted into a digital signal, and the analog-to-digital converted signal is divided into equal-length windows. Each window comprises a plurality of heartbeat position points and heartbeat peak values.
S102: and filtering the signal of the S101 by a low-pass filter, and then calculating an autocorrelation function.
The cut-off frequency of the low-pass filter is an important parameter of the heart rate monitoring system. The frequency of the body motion is mostly above 6Hz, and then through a plurality of experiments, the highest cut-off frequency of the low-pass filter is adjusted until the minimum frequency capable of filtering the body motion frequency.
The autocorrelation function is expressed as two formulas:
wherein n represents the number of signal sampling points, h represents the time shift,
which represents the mean value of the signal and,
xtis defined as: the average of the signal is subtracted from the filtered signal to obtain the signal amplitude, which is then squared to produce a power signal proportional to the instantaneous mechanical power in the system.
S104: the heartbeat is extracted using the autocorrelation function peak.
The first step is as follows: computing
Derivative of and
the difference between the derivatives of (a).
The second step is that: comparison
Derivative of and
the magnitude between the derivative difference of (c) and the threshold.
The third step: if the difference value is larger than the threshold value threshold, the slope of the heartbeat waveform begins to change from positive to negative, the peak value of the heartbeat waveform is between t and t +1, a plurality of sampling points are arranged between t and t +1 in a window, the sampling points are fitted through a least square method, and the peak value of the heartbeat waveform is determined through the fitted graph.
The method has the advantages that an autocorrelation function related to the heartbeat waveform is established, the autocorrelation function is utilized to quickly determine the approximate positions (t and t +1) of sampling points where the peak value of the heartbeat waveform is located, the sampling points between t and t +1 are fitted through a least square method, and the fitted graph searches for the maximum value of the heartbeat waveform.
The problem that can be solved is that fitting of the whole heartbeat sampling point is avoided, only the sampling points within a specific range (t and t +1) are required to be fitted, the calculation amount is saved, and the calculation cost is also saved.
Assuming that each window obtains a plurality of peaks in the manner in S103, calculating an average heartbeat interval by using the first 20% of peaks in the set of peaks contained in each window, assuming that n peaks are contained in the 20% of peaks, and the time interval between the first peak and the nth peak is T, averaging the heartbeat interval:
s105: the heart rate is calculated.
Assuming that the heart beat peak value in the window is m, the time interval between the first peak value and the m-th peak value is S, the heart rate is bpm which is m/S.
The above detailed description merely describes preferred embodiments of the present invention and does not limit the scope of the invention. Without departing from the spirit and scope of the present invention, it should be understood that various changes, substitutions and alterations can be made herein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims and their equivalents. The scope of the invention is defined by the claims.
Claims (5)
1. A method for detecting the heart rate without sensing based on a detector is characterized by comprising the following steps,
s100: detecting heart vibration by using a geophone, and converting a heartbeat signal into an electric signal;
s101: converting the signal detected in S100 into digital signals in an analog-to-digital mode, and dividing the signals after the analog-to-digital conversion into windows with equal length;
s102: after filtering by using a low-pass filter, performing autocorrelation function calculation on the signal sample in S100;
s103: extracting a sample autocorrelation function peak value;
s104: extracting heartbeat by utilizing the peak value of the autocorrelation function;
s105: the heart rate is calculated.
2. The method of claim 1, wherein the autocorrelation function is formulated as follows:
wherein n represents the number of signal sampling points, h represents the time shift,
representing the mean value of the signal, xtExpressed as the square of the signal amplitude obtained by subtracting the mean value of the heartbeat signal from the heartbeat signal and then subtracting the mean value of the heartbeat signal from the heartbeat signal.
3. The method for detecting heart rate by using a geophone according to claim 2, wherein in S102, the calculation is performed
Derivative of and
if the difference is greater than the threshold of the preset threshold, the heartbeat peak value is between t and t +1, all points between t and t +1 are fitted through a least square method, and the peak value position is determined by the fitted heartbeat signal waveform.
4. The method according to claim 3, wherein in step S101, each of the windows obtains a plurality of peaks in the manner of step S103, and an average heartbeat interval is calculated from the first 20% of the peaks in the set of peaks included in each window, and assuming that the 20% of the peaks are n, and the time interval between the first peak and the nth peak is T, the average heartbeat interval is:
5. the method as claimed in claim 4, wherein in step S105, assuming that m heart beat peaks are in the window, and the time interval between the first peak and the m-th peak is S, the heart rate is bpm-m/S.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1992009232A1 (en) * | 1990-11-27 | 1992-06-11 | Harry Herbert Peel, Iii | Vital life sign detector |
| US20100152600A1 (en) * | 2008-04-03 | 2010-06-17 | Kai Sensors, Inc. | Non-contact physiologic motion sensors and methods for use |
| US20140073956A1 (en) * | 2012-09-11 | 2014-03-13 | Nellcor Puritan Bennett Llc | Methods and systems for determining physiological information based on a cross-correlation waveform |
| US20160239464A1 (en) * | 2013-10-16 | 2016-08-18 | Faz Technology Limited | Method and System for Tracking the Centre of a Peak from a plurality of Sample Points in an Optical System |
| CN106923812A (en) * | 2017-03-30 | 2017-07-07 | 华南理工大学 | A kind of rate calculation method based on cardiechema signals autocorrelation analysis |
| CN109414203A (en) * | 2016-06-30 | 2019-03-01 | 美国亚德诺半导体公司 | Online heart rate estimation based on optical measurement |
| CN109635786A (en) * | 2019-01-22 | 2019-04-16 | 佛山市百步梯医疗科技有限公司 | A kind of rate calculation algorithm based on heart sound auto-correlation function |
| WO2020160351A1 (en) * | 2019-02-01 | 2020-08-06 | University Of Georgia Research Foundation, Inc. | Contactless monitoring of sleep activities and body vital signs via seismic sensing |
-
2021
- 2021-05-18 CN CN202110539090.1A patent/CN113171073A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1992009232A1 (en) * | 1990-11-27 | 1992-06-11 | Harry Herbert Peel, Iii | Vital life sign detector |
| US20100152600A1 (en) * | 2008-04-03 | 2010-06-17 | Kai Sensors, Inc. | Non-contact physiologic motion sensors and methods for use |
| US20140073956A1 (en) * | 2012-09-11 | 2014-03-13 | Nellcor Puritan Bennett Llc | Methods and systems for determining physiological information based on a cross-correlation waveform |
| US20160239464A1 (en) * | 2013-10-16 | 2016-08-18 | Faz Technology Limited | Method and System for Tracking the Centre of a Peak from a plurality of Sample Points in an Optical System |
| CN109414203A (en) * | 2016-06-30 | 2019-03-01 | 美国亚德诺半导体公司 | Online heart rate estimation based on optical measurement |
| CN106923812A (en) * | 2017-03-30 | 2017-07-07 | 华南理工大学 | A kind of rate calculation method based on cardiechema signals autocorrelation analysis |
| CN109635786A (en) * | 2019-01-22 | 2019-04-16 | 佛山市百步梯医疗科技有限公司 | A kind of rate calculation algorithm based on heart sound auto-correlation function |
| WO2020160351A1 (en) * | 2019-02-01 | 2020-08-06 | University Of Georgia Research Foundation, Inc. | Contactless monitoring of sleep activities and body vital signs via seismic sensing |
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