WO2017118127A1 - 一种心跳信号处理方法、装置和系统 - Google Patents

一种心跳信号处理方法、装置和系统 Download PDF

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WO2017118127A1
WO2017118127A1 PCT/CN2016/101249 CN2016101249W WO2017118127A1 WO 2017118127 A1 WO2017118127 A1 WO 2017118127A1 CN 2016101249 W CN2016101249 W CN 2016101249W WO 2017118127 A1 WO2017118127 A1 WO 2017118127A1
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Prior art keywords
waveform
heartbeat
curve
heart rate
rising edge
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PCT/CN2016/101249
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English (en)
French (fr)
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刘�文
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深圳和而泰智能控制股份有限公司
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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
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
    • G16Z99/00—Subject matter not provided for in other main groups of this subclass

Definitions

  • the present application relates to the field of signal processing technologies, and in particular, to a heartbeat signal processing method, apparatus, and system.
  • sleep monitoring mainly uses radar infrared, ultrasonic detection, and piezoelectric sensors to acquire people's heartbeat signals, respiratory signals, etc., and to process various sleep parameters such as heart rate values during sleep.
  • radar infrared and ultrasonic detection methods are costly, and will generate infrared or ultrasonic radiation, and the detection effect is not satisfactory.
  • the principle of monitoring the sleep parameters by the piezoelectric sensor is to monitor the mechanical signals caused by the thoracic retracting movement and the heartbeat tremor caused by the breathing by the piezoelectric film sensor with higher sensitivity, and then analyze the signal to obtain parameters such as breathing and heartbeat.
  • a piezoelectric sensor when using a piezoelectric sensor to monitor heart rate, it is susceptible to various signal interferences, such as: strong respiratory interference, human physiological signals (including body motion, etc.) interference, external environmental interference, and the like.
  • respiratory interference has the greatest impact, because the respiratory signal is relatively strong, and the heartbeat signal is basically submerged.
  • body motion and external environmental interference as well as signal interference in the frequency band of the concentricity rate, the originally weak heartbeat signal is weaker and more difficult to handle. How to perform heartbeat signal processing under interference and obtain an effective heartbeat signal has become an important research topic of current heart rate monitoring.
  • the main purpose of the present application is to provide a heartbeat signal processing method, apparatus and system, which aim to solve the technical problem that the heartbeat signal processing accuracy is poor and the error is large.
  • the embodiment of the present application provides a heartbeat signal processing method, where the heartbeat signal processing method includes the following steps:
  • a heart rate value is calculated.
  • the step of fitting the respiratory curve according to the peak and trough position information of the original waveform, and removing the interference of the breathing curve on the original waveform, the step of obtaining a heartbeat waveform includes:
  • the interference of the breathing curve on the original waveform is removed, and filtering is performed to obtain a heartbeat waveform.
  • the breathing curve comprises a rising edge breathing curve and a falling edge breathing curve
  • the heartbeat waveform comprising a rising edge heartbeat curve and a falling edge heartbeat curve, according to the breathing curve, removing the breathing curve from the original
  • the steps of waveform interference and filtering to obtain a heartbeat waveform include:
  • the falling edge breathing curve is removed on the falling edge waveform and filtered to obtain a falling edge heartbeat curve.
  • the step of calculating a heart rate value according to the heartbeat waveform comprises:
  • Abnormal data in the rising edge heart rate and the falling edge heart rate are removed according to a preset processing rule
  • the heart rate value is obtained based on the processed rising edge heart rate and the falling edge heart rate.
  • the embodiment of the present application further provides a heartbeat signal processing apparatus, where the heartbeat signal processing apparatus includes:
  • Obtaining a module configured to acquire a raw waveform of a heartbeat signal, and obtain peak and trough position information of the original waveform;
  • a waveform processing module configured to fit a respiratory curve according to peak and valley position information of the original waveform, and remove interference of the breathing curve on the original waveform to obtain a heartbeat waveform;
  • a calculation module configured to calculate a heart rate value according to the heartbeat waveform.
  • the waveform processing module comprises:
  • a waveform trend unit configured to obtain a waveform trend according to peak and valley position information of the original waveform
  • a fitting unit configured to obtain a breathing curve by fitting a respiratory waveform according to peak and trough position information of the original waveform, the waveform trend;
  • an interference cancellation unit configured to remove interference of the breathing curve on the original waveform according to the breathing curve, and perform filtering to obtain a heartbeat waveform.
  • the breathing curve comprises a rising edge breathing curve and a falling edge breathing curve
  • the heartbeat waveform comprising a rising edge heartbeat curve and a falling edge heartbeat curve
  • the de-interference unit comprising:
  • a waveform positioning sub-unit configured to obtain a rising edge waveform curve and a falling edge waveform curve of the original waveform according to the peak and trough position information of the original waveform and the waveform trend;
  • the calculation module comprises:
  • Calculating a subunit configured to calculate a corresponding rising edge heart rate and a falling edge heart rate according to the rising edge heartbeat curve and the falling edge heartbeat curve, respectively;
  • An exception processing subunit configured to remove abnormal data in the rising edge heart rate and the falling edge heart rate according to a preset processing rule
  • the integrated processing subunit is configured to obtain a heart rate value according to the processed rising edge heart rate and the falling edge heart rate.
  • an embodiment of the present application further provides a heartbeat signal processing system, which
  • the heartbeat signal processing system includes a piezoelectric sensor, an analog to digital converter, and a heart rate monitoring CPU, wherein:
  • the piezoelectric sensor is configured to collect a piezoelectric analog signal
  • the analog-to-digital converter is configured to convert the piezoelectric analog signal into a digital signal to obtain a raw waveform of a heartbeat signal;
  • the heart rate monitoring CPU includes an acquisition module, a waveform processing module, and a calculation module.
  • the embodiment of the present application further provides an electronic device, including:
  • At least one processor and,
  • the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method as described above.
  • a method, device and system for processing heartbeat signals obtains peak and valley position information of an original waveform by acquiring an original waveform of a heartbeat signal; and then, fitting a respiratory curve according to peak and valley position information of the original waveform And removing the interference from the original waveform according to the breathing curve to obtain a heartbeat waveform; then, according to the obtained heartbeat waveform, the heart rate value is calculated.
  • the embodiment of the present application achieves the removal of the interference caused by the breathing on the original waveform, solves the problem that the heartbeat signal is affected by the respiratory signal and causes a large error, and the accurate heart rate value calculated according to the heartbeat signal.
  • FIG. 1 is a schematic flowchart of a first embodiment of a method for processing a heartbeat signal according to the present application
  • FIG. 2 is a schematic flowchart of a second embodiment of a method for processing a heartbeat signal according to the present application
  • FIG. 3 is a schematic flowchart of a third embodiment of a method for processing a heartbeat signal according to the present application
  • FIG. 4 is a schematic flowchart of a raw waveform processing process of a heartbeat signal according to an embodiment of the present application
  • FIG. 5 is a schematic flowchart of a fourth embodiment of a method for processing a heartbeat signal according to the present application.
  • FIG. 6 is a schematic diagram of a heartbeat waveform in an embodiment of the present application.
  • FIG. 7 is a schematic diagram of functional modules of a first embodiment of a heartbeat signal processing apparatus of the present application.
  • FIG. 8 is a schematic diagram of functional modules of a second embodiment of a heartbeat signal processing apparatus of the present application.
  • FIG. 9 is a schematic diagram of functional modules of a third embodiment of a heartbeat signal processing apparatus of the present application.
  • FIG. 10 is a schematic diagram of functional modules of a fourth embodiment of a heartbeat signal processing apparatus of the present application.
  • FIG. 11 is a schematic block diagram of a first embodiment of a heartbeat signal processing system of the present application.
  • FIG. 12 is a schematic structural diagram of hardware of an electronic device according to an embodiment of the present application.
  • the main solution of the embodiment of the present application is: acquiring the original waveform of the heartbeat signal, and obtaining the peak and trough position information of the original waveform; then, fitting the respiratory curve according to the peak and trough position information of the original waveform, and comparing the original waveform according to the breathing curve.
  • the interference is removed to obtain a heartbeat waveform; then, based on the obtained heartbeat waveform, the heart rate value is calculated.
  • the heart rate value obtained according to the heartbeat signal has a large error.
  • the embodiment of the present application provides a solution for removing the interference caused by the breathing on the original waveform by fitting the breathing curve, and solving the problem that the heartbeat signal is greatly affected by the respiratory signal, and the accuracy is calculated according to the heartbeat signal. Heart rate value.
  • a first embodiment of a heartbeat signal processing method of the present application provides a heartbeat signal processing method, where the heartbeat signal processing method includes:
  • Step S10 Acquire a raw waveform of the heartbeat signal to obtain peak and trough position information of the original waveform.
  • This embodiment is mainly used for the processing of a heartbeat signal to obtain a heart rate.
  • the present embodiment acquires a body vibration signal of a user's breathing and heartbeat by using a piezoelectric sensor, for example, placing a piezoelectric sensor in a mattress or under a bed sheet to obtain a chest cavity caused by breathing when the human body sleeps.
  • a regular body vibration signal caused by vibration signals and heartbeats is included.
  • the piezoelectric sensor sends the obtained analog signal to the A/D converter (Analog to Digital Converter, analog to digital converter).
  • A/D converter Analog to Digital Converter, analog to digital converter
  • the A/D converter After receiving the analog signal, the A/D converter converts the analog line number into a digital signal, obtains the original waveform of the heartbeat signal, and sends the obtained original heartbeat waveform to the heart rate monitoring CPU, and the heart rate monitoring CPU processes the heartbeat signal.
  • the heart rate monitoring CPU receives the sampling wave (A/D Sample) sent by the A/D converter, and obtains the original waveform of the heartbeat signal, and first obtains the peak and trough position information of the original waveform.
  • the heart rate monitoring CPU can directly perform the differential threshold to obtain the peak-sample and sample-Trough position information of the original waveform and store the information.
  • the peak position information of the original waveform includes the position and amplitude of each peak in the original waveform
  • the valley position information of the original waveform includes the position and amplitude of each trough in the original waveform.
  • the heart rate monitoring CPU obtains a respiratory waveform through high-low-pass filtering.
  • the frequency cutoff range of the high and low pass filtering can be flexibly set according to the breathing cycle.
  • the frequency range of the respiratory waveform is set to 0.1-0.5 Hz
  • the waveform obtained by high-low-pass filtering is a waveform in the range of 0.1-0.5 Hz.
  • the heart rate monitoring CPU After the respiratory waveform is obtained, the heart rate monitoring CPU performs a differential threshold to obtain the Breath-Peak and Breath-Trough position information of the respiratory waveform.
  • the peaks and troughs of the corresponding positions on the original waveform are respectively found.
  • the interval range is set, and the maximum amplitude point within the range corresponding to the original waveform is searched for, and the point is the peak of the corresponding position of the original waveform.
  • the interval range is set, and the minimum amplitude point within the range corresponding to the original waveform is searched, and the point is the peak and valley of the corresponding position of the original waveform.
  • the peaks and valleys of the respiratory waveform According to the peaks and valleys of the respiratory waveform, the peaks and troughs of the corresponding positions on the original waveform can be searched to avoid the direct acquisition of the peaks and troughs of the original waveform.
  • the peaks and valleys acquired due to the interference factors are not the exact position of the respiratory waveform. , resulting in an inaccurate breathing curve of the fit.
  • the preset interval range can be flexibly set according to actual needs, for example, the data point collected within 2 seconds before and after the reference position is a preset interval range.
  • the heart rate monitoring CPU obtains the peak and trough position information of the original waveform based on the peak and trough position information of the respiratory waveform.
  • Step S20 fitting a breathing curve according to the peak and valley position information of the original waveform, and removing interference of the breathing curve on the original waveform to obtain a heartbeat waveform.
  • the heart rate monitoring CPU After acquiring the peak and valley position information of the original waveform, the heart rate monitoring CPU fits the peak of the original waveform and the trough position information to obtain a breathing curve, and removes the interference of the breathing curve on the original waveform to obtain a heartbeat waveform.
  • the heart rate monitoring CPU determines the waveform trend according to the peak and trough position information of the original waveform.
  • the waveform of the secondary peak is a rising edge (Wave-S); the previous point of a valley is For the peak, the waveform of the secondary peak is the falling edge (Wave-X).
  • the respiratory waveform is fitted on the original waveform to obtain a breathing curve.
  • the respiratory interference on the original waveform is removed according to the breathing curve, and the waveform after the respiratory interference is removed is obtained.
  • the obtained waveform after removing the respiratory interference is filtered.
  • the frequency range of the heartbeat waveform is set to 0.8-1.6 Hz
  • the waveform obtained by the high-low-pass filtering is a waveform in the range of 0.8-1.6 Hz.
  • Step S30 calculating a heart rate value according to the heartbeat waveform.
  • the heart rate monitoring CPU calculates the heart rate value based on the heartbeat waveform.
  • the beginning portion and the ending portion of the heartbeat waveform are removed, and the irregular heartbeat signals collected by the start portion and the end portion are removed.
  • the sampling interval time of the two peaks is obtained, and the heart rate value of the sampling time node is calculated according to the interval time of the two peaks.
  • the heart rate value can also be calculated from the position information of the trough.
  • the calculated heart rate value is selected and the range is limited.
  • the heart rate range is set to 40-120 times per minute, according to which the unreasonable too fast heartbeat or slow heartbeat caused by getting up or the like is removed; if the heart rate value of a sampling time node is obtained Compared with the heart rate value obtained by the previous time node of the time node, less than one third of the previous heart rate value or greater than three thirds of the previous heart rate value, the determined heart rate value is excessively large, and is removed. This heart rate value.
  • the heart rate value obtained by the smoothing process is taken as an average of a plurality of consecutive heart rate values, and the obtained heart rate value is the heart rate value during the sampling time.
  • the heart rate monitoring CPU acquires the original waveform of the heartbeat signal, and obtains the peak and trough position information of the original waveform; then, the respiratory curve is obtained according to the peak and valley position information of the original waveform, and the original waveform is removed according to the breathing curve. Interference, the heartbeat waveform is obtained; then, the heart rate value is calculated according to the obtained heartbeat waveform.
  • the interference caused by the breathing on the original waveform is removed, and the problem that the heartbeat signal is affected by the breathing signal and the error is large is solved, and the accurate heart rate value is calculated according to the heartbeat signal.
  • a second embodiment of the heartbeat signal processing method of the present application provides a method for processing a heartbeat signal.
  • the step S20 includes:
  • Step S21 Obtain a waveform trend according to peak and valley position information of the original waveform.
  • the heart rate monitoring CPU After acquiring the peak and valley position information of the original waveform, the heart rate monitoring CPU fits the peak of the original waveform and the trough position information to obtain a breathing curve, and removes the interference of the breathing curve on the original waveform to obtain a heartbeat waveform.
  • the heart rate monitoring CPU determines the waveform trend based on the peak and valley position information of the original waveform, and locates the rising edge and the falling edge of the original waveform.
  • the waveform trend of the peak is a rising edge; the previous point of a trough is a peak, then the trough The waveform trend is the falling edge.
  • Step S22 fitting a breathing curve according to the peak and trough position information of the original waveform and the waveform trend.
  • the heart rate monitoring CPU After acquiring the peak and valley position information of the original waveform, the heart rate monitoring CPU respectively fits the respiratory waveform according to the peak and trough position information and the waveform trend of the original waveform.
  • the adjacent waveform peaks and trough positions of the original waveform are used as the starting point and the ending point, and the respiratory waveform is fitted to the rising edge waveform to obtain a rising edge breathing curve; the respiratory waveform is fitted according to the falling edge waveform to obtain a falling edge breathing curve.
  • Step S23 removing interference of the breathing curve on the original waveform according to the breathing curve, and performing filtering to obtain a heartbeat waveform.
  • the heart rate monitoring CPU After obtaining the fitted breathing curve, the heart rate monitoring CPU removes the interference of the breathing curve on the original waveform to obtain a heartbeat waveform.
  • the obtained original waveform corresponds to the position of the breathing curve, on the original waveform, the breathing curve of the corresponding position is subtracted, and the same position on the waveform, the amplitude of the sampling point is subtracted, and the breathing curve is performed.
  • the obtained waveform is the waveform after removing the respiratory interference.
  • the obtained waveform after removing the respiratory interference is subjected to high-low-pass filtering.
  • the frequency range of the heartbeat waveform is set to 0.8-1.6 Hz, and the waveform obtained by the high-low-pass filtering is in the range of 0.8-1.6 Hz. Waveform.
  • the heart rate monitoring CPU obtains a waveform trend according to the peak and valley position information of the original waveform; then, according to the peak and valley position information and the waveform trend of the original waveform, the respiratory waveform is fitted to obtain a breathing curve;
  • the breathing curve removes the interference of the breathing curve on the original waveform and obtains the heartbeat waveform.
  • the waveform trend is obtained according to the peak and trough position information of the original waveform, and a more accurate breathing curve is obtained by fitting, and then the original waveform is subtracted from the corresponding position of the respiratory curve to remove the respiratory interference, and filtering is performed to remove the clutter interference, and the accurate is obtained. , interference-free heartbeat waveform.
  • a third embodiment of the heartbeat signal processing method of the present application provides a heartbeat signal processing method.
  • the breathing curve includes a rising edge breathing curve and a falling edge breathing curve.
  • the heartbeat waveform includes a rising edge heartbeat curve and a falling edge heartbeat curve, and the step S23 includes:
  • Step S231 obtaining a rising edge waveform curve and a falling edge waveform curve of the original waveform according to the peak and valley position information of the original waveform and the waveform trend.
  • the heart rate monitoring CPU After obtaining the fitted breathing curve, the heart rate monitoring CPU removes the interference of the breathing curve on the original waveform to obtain a heartbeat waveform.
  • the heart rate monitoring CPU respectively fits the rising edge waveform and the falling edge waveform according to the peak and valley position information of the original waveform, and the fitted breathing curve includes Rise along the breathing curve and the falling edge of the breathing curve.
  • the heart rate monitoring CPU obtains the rising edge waveform curve and the falling edge waveform curve of the original waveform according to the peak and valley position information and the waveform trend of the original waveform. For example, if the waveform trend of two adjacent points is a rising edge waveform, according to the position information of the two points, the original waveform is obtained as a rising edge breathing curve.
  • the rising edge waveform of the original waveform corresponds to the rising edge breathing curve obtained by the fitting, and the falling edge of the original waveform is matched with the falling edge of the breathing curve.
  • Step S232 removing the rising edge breathing curve on the rising edge waveform curve, and performing filtering to obtain a rising edge heartbeat curve; removing the falling edge breathing curve on the falling edge waveform curve, and performing filtering to obtain Falling along the heartbeat curve.
  • the heart rate monitoring CPU removes the respiratory signal interference of the original waveform, specifically, as an embodiment, on the original waveform, the heart rate monitoring CPU processes the rising edge waveform curve and the falling edge waveform curve, respectively.
  • the rising edge waveform is Ss
  • the falling edge waveform is Sx
  • the rising edge breathing curve is ys
  • the falling edge breathing curve is wx. Since the original waveform corresponds to the position of the breathing curve, the rising edge waveform Ss corresponds to the rising edge breathing curve ys, and the falling edge waveform curve Sx corresponds to the falling edge breathing curve wx.
  • the amplitude of the point on the curve Hs is the difference between the amplitude of the rising edge waveform point and the rising edge breathing curve point of the same position.
  • the curve Hs is subjected to high-low-pass filtering.
  • the frequency range of the heartbeat waveform is set to 0.8-1.6 Hz
  • the waveform obtained by the high-low-pass filtering is a rising edge heartbeat curve in the range of 0.8-1.6 Hz.
  • the amplitude of the point on the falling edge of the heartbeat curve is the difference between the amplitude of the falling curve edge point and the falling edge breathing curve point of the same position.
  • the curve Hx is subjected to high-low-pass filtering.
  • the frequency range of the heartbeat waveform is set to 0.8-1.6 Hz
  • the waveform obtained by the high-low-pass filtering is a rising edge heartbeat curve in the range of 0.8-1.6 Hz.
  • the heart rate monitoring CPU obtains a rising edge heartbeat curve and a falling edge heartbeat curve, that is, a heartbeat waveform is obtained.
  • the breathing curve includes a rising edge breathing curve and a falling edge breathing curve
  • the heart rate monitoring CPU first obtains a rising edge waveform curve and a falling edge waveform curve of the original waveform; then, removing the rising edge breathing curve on the rising edge waveform curve, And filtering to obtain a rising edge heartbeat curve; removing the falling edge breathing curve on the falling edge waveform curve, and performing filtering to obtain a falling edge heartbeat curve, thereby obtaining a heartbeat waveform.
  • the rising edge waveform and the falling edge waveform are located, and the rising edge breathing curve and the falling edge breathing curve are respectively obtained according to the rising edge waveform and the falling edge waveform, and then the rising edge waveform and the falling edge waveform of the original waveform are obtained.
  • the interference processing is performed separately to avoid the error caused by the interference caused by the peak of the respiratory signal and the peak of the heartbeat signal, and the accurate acquisition of the heartbeat signal is realized.
  • the fourth embodiment of the heartbeat signal processing method of the present application provides a method for processing a heartbeat signal. Based on the embodiment shown in FIG. 3, the step S30 includes:
  • Step S31 Calculate a corresponding rising edge heart rate and a falling edge heart rate according to the rising edge heartbeat curve and the falling edge heartbeat curve, respectively.
  • the heart rate monitoring CPU calculates the heart rate value separately.
  • the heart rate monitoring CPU acquires peak and trough position information of the rising edge heartbeat curve and the falling edge heartbeat curve.
  • the heart rate monitoring CPU obtains the sampling interval time of two peaks or troughs according to the distance between two peaks or valley sampling points of the rising edge heartbeat curve, and calculates the sampling time node according to the interval time of the two peaks or troughs. Heart rate value. Thereby, the rising edge heart rate value HR1 corresponding to the rising edge heartbeat curve is obtained.
  • the heart rate monitoring CPU obtains the sampling interval time of two peaks or troughs according to the distance between two adjacent peaks or trough sampling points of the falling edge heartbeat curve, and calculates the sampling time node according to the interval between the two peaks or troughs. Heart rate value. Thereby, the falling edge heart rate value HR2 corresponding to the falling edge heartbeat curve is obtained.
  • Step S32 removing abnormal data in the rising edge heart rate and the falling edge heart rate according to a preset processing rule.
  • the heart rate monitoring CPU After obtaining the rising edge heart rate value HR1 and the falling edge heart rate value HR2, the heart rate monitoring CPU processes the obtained heart rate value according to a preset rule to remove the abnormal data.
  • preset processing rules include a tradeoff, a range limit, and a smoothing process.
  • the heart rate monitoring CPU rounds off the initial waveform within a certain time range when the heartbeat signal is sampled, and rounds off the end waveform within a certain time range when the heartbeat sampling is finished.
  • the initial waveform and the end waveform may cause waveform anomalies due to external factors such as initialization of the sampling instrument.
  • the excessive or too small heart rate value is removed.
  • the normal heart rate value of the human body set the heart rate range to 40-120 times per minute, according to which the unreasonable too fast heartbeat or slow heartbeat caused by getting up or the like is removed.
  • the obtained heart rate value of a sampling time node is smaller than the heart rate value obtained by the previous time node of the time node, it is less than one third of the previous heart rate value or greater than three thirds of the previous heart rate value. Fourth, the determined heart rate value is too large to remove the heart rate value.
  • the heart rate value corresponding to the rising edge heartbeat curve or the falling edge heartbeat curve is discarded.
  • the preset processing rule may also include other abnormal data processing modes, which can be flexibly set according to actual needs.
  • the heart rate monitoring CPU obtains the rising edge heart rate data and the falling edge heart rate data after the abnormal data is removed.
  • Step S33 obtaining a heart rate value according to the processed rising edge heart rate and the falling edge heart rate.
  • the heart rate monitoring CPU comprehensively processes the rising edge heart rate and the falling edge heart rate to obtain the heart rate value corresponding to the original waveform.
  • the heart rate monitoring CPU averages the obtained heart rate values, and the obtained heart rate value is the heart rate value of the original waveform sampling time.
  • the heart rate monitoring CPU sorts the obtained rising edge heart rate and falling edge heart rate by corresponding sampling time nodes, and divides into a plurality of sampling regions in chronological order. Then, the heart rate average of each sampling area is separately obtained, and then the heart rate value of the original waveform sampling time is obtained by comprehensive processing.
  • the embodiment of the present application adopts segment processing, and when the heartbeat signal is collected, the original waveform of the preset length is processed as a piece of original waveform, and the heart rate value corresponding to the original waveform of the heart rate is calculated.
  • the preset length can be flexibly set according to actual needs.
  • the waveforms of the 70 points are processed as a piece of original waveform to obtain a corresponding heartbeat waveform.
  • the heart rate monitoring CPU obtains multiple segments of the heart. Jump waveform.
  • the heart rate monitoring CPU obtains the multi-segment heartbeat waveform, and removes the heartbeat waveform within the preset time range when starting sampling and ending sampling.
  • the heart rate monitoring CPU calculates a plurality of heart rate values.
  • the heart rate monitoring CPU performs comprehensive processing on the obtained plurality of heart rate values, removes the abnormal data, calculates an average heart rate value of the multi-segment heartbeat waveform, and obtains a final heart rate value.
  • the heart rate monitoring CPU calculates the corresponding rising edge heart rate and the falling edge heart rate according to the rising edge heartbeat curve and the falling edge heartbeat curve respectively; then, according to the preset processing rule, the rising edge heart rate and the falling edge heart rate are removed according to a preset processing rule.
  • the abnormal data in the middle; then, according to the processed rising edge heart rate and the falling edge heart rate, the heart rate value corresponding to the original waveform is obtained.
  • the obtained heart rate value is comprehensively processed to obtain the final heart rate value, and the heartbeat signal is processed to obtain the heart rate value.
  • the first embodiment of the heartbeat signal processing apparatus of the present application provides a heartbeat signal processing apparatus 07, and the heartbeat signal processing apparatus 07 includes:
  • the obtaining module 100 is configured to obtain a raw waveform of the heartbeat signal, and obtain peak and trough position information of the original waveform.
  • This embodiment is mainly used for the processing of a heartbeat signal to obtain a heart rate.
  • the present embodiment acquires a body vibration signal of a user's breathing and heartbeat by using a piezoelectric sensor, for example, placing a piezoelectric sensor in a mattress or under a bed sheet to obtain a chest cavity caused by breathing when the human body sleeps.
  • a regular body vibration signal caused by vibration signals and heartbeats is included.
  • the piezoelectric sensor transmits the obtained analog signal to an A/D converter (Analog to Digital Converter).
  • A/D converter Analog to Digital Converter
  • the A/D converter After receiving the analog signal, the A/D converter converts the analog line number into a digital signal, obtains the original waveform of the heartbeat signal, and sends the obtained original heartbeat waveform to the acquiring module 100, and the acquiring module 100 performs the processing of the heartbeat signal.
  • the obtaining module 100 receives the sampling wave (A/D Sample) sent by the A/D converter, and obtains the original waveform of the heartbeat signal, and first acquires the peak and trough position information of the original waveform.
  • A/D Sample the sampling wave sent by the A/D converter
  • the obtaining module 100 can directly perform a differential threshold to obtain an original waveform. Sample-Peak and Sample-Trough location information is stored.
  • the peak position information of the original waveform includes the position and amplitude of each peak in the original waveform
  • the valley position information of the original waveform includes the position and amplitude of each trough in the original waveform.
  • the acquisition module 100 obtains a respiratory waveform by high-low-pass filtering.
  • the frequency cutoff range of the high and low pass filtering can be flexibly set according to the breathing cycle.
  • the frequency range of the respiratory waveform is set to 0.1-0.5 Hz
  • the waveform obtained by high-low-pass filtering is a waveform in the range of 0.1-0.5 Hz.
  • the heart rate monitoring CPU After the respiratory waveform is obtained, the heart rate monitoring CPU performs a differential threshold to obtain the Breath-Peak and Breath-Trough position information of the respiratory waveform.
  • the peaks and troughs of the corresponding positions on the original waveform are respectively found.
  • the interval range is set, and the maximum amplitude point within the range corresponding to the original waveform is searched for, and the point is the peak of the corresponding position of the original waveform.
  • the interval range is set, and the minimum amplitude point within the range corresponding to the original waveform is searched, and the point is the peak and valley of the corresponding position of the original waveform.
  • the peaks and valleys of the respiratory waveform According to the peaks and valleys of the respiratory waveform, the peaks and troughs of the corresponding positions on the original waveform can be searched to avoid the direct acquisition of the peaks and troughs of the original waveform.
  • the peaks and valleys acquired due to the interference factors are not the exact position of the respiratory waveform. , resulting in an inaccurate breathing curve of the fit.
  • the preset interval range can be flexibly set according to actual needs, for example, the data point collected within 2 seconds before and after the reference position is a preset interval range.
  • the acquisition module 100 obtains the peak and trough position information of the original waveform based on the peak and trough position information of the respiratory waveform.
  • the waveform processing module 200 is configured to fit a breathing curve according to peak and valley position information of the original waveform, and remove interference of the breathing curve on the original waveform to obtain a heartbeat waveform.
  • the waveform processing module 200 After acquiring the peak and valley position information of the original waveform, the waveform processing module 200 fits the peak of the original waveform and the trough position information to obtain a breathing curve, and removes the interference of the breathing curve on the original waveform to obtain a heartbeat waveform.
  • the waveform processing module 200 determines the waveform trend according to the peak and trough position information of the original waveform.
  • the waveform of the secondary peak is a rising edge (Wave-S); the previous point of a valley is For the peak, the waveform of the secondary peak is the falling edge (Wave-X).
  • the respiratory waveform is fitted on the original waveform to obtain a breathing curve.
  • the respiratory interference on the original waveform is removed according to the breathing curve, and the waveform after the respiratory interference is removed is obtained.
  • the obtained waveform after removing the respiratory interference is filtered.
  • the frequency range of the heartbeat waveform is set to 0.8-1.6 Hz
  • the waveform obtained by the high-low-pass filtering is a waveform in the range of 0.8-1.6 Hz.
  • the calculating module 300 is configured to calculate a heart rate value according to the heartbeat waveform.
  • the calculation module 300 calculates the heart rate value from the heartbeat waveform.
  • the beginning portion and the ending portion of the heartbeat waveform are removed, and the irregular heartbeat signals collected by the start portion and the end portion are removed.
  • the sampling interval time of the two peaks is obtained, and the heart rate value of the sampling time node is calculated according to the interval time of the two peaks.
  • the heart rate value can also be calculated from the position information of the trough.
  • the calculated heart rate value is selected and the range is limited.
  • the heart rate range is set to 40-120 times per minute, according to which the unreasonable too fast heartbeat or slow heartbeat caused by getting up or the like is removed; if the heart rate value of a sampling time node is obtained Compared with the heart rate value obtained by the previous time node of the time node, less than one third of the previous heart rate value or greater than three thirds of the previous heart rate value, the determined heart rate value is excessively large, and is removed. This heart rate value.
  • the heart rate value obtained by the smoothing process is taken as an average of a plurality of consecutive heart rate values, and the obtained heart rate value is the heart rate value during the sampling time.
  • the acquisition module 100 acquires the original waveform of the heartbeat signal, and obtains the peak and trough position information of the original waveform. Then, the waveform processing module 200 fits the peak of the original waveform and the position information of the trough to obtain a breathing curve, and according to the breathing curve. Remove interference from the original waveform to obtain a heartbeat waveform; Then, the calculation module 300 calculates a heart rate value according to the obtained heartbeat waveform. In this embodiment, by fitting the breathing curve, the interference caused by the breathing on the original waveform is removed, and the problem that the heartbeat signal is affected by the respiratory signal and the error is large is solved. The heart rate value calculated according to the heartbeat signal in this embodiment is very accurate and close. The actual heart rate value.
  • the second embodiment of the heartbeat signal processing apparatus of the present application provides a heartbeat signal processing apparatus 08.
  • the waveform processing module 200 includes:
  • the waveform trending unit 210 is configured to obtain a waveform trend according to peak and valley position information of the original waveform.
  • the waveform processing module 200 After acquiring the peak and valley position information of the original waveform, the waveform processing module 200 fits the peak of the original waveform and the trough position information to obtain a breathing curve, and removes the interference of the breathing curve on the original waveform to obtain a heartbeat waveform.
  • the waveform trending unit 210 determines the waveform trend based on the peak and valley position information of the original waveform, and locates the rising edge and the falling edge of the original waveform.
  • the waveform trend of the peak is a rising edge; the previous point of a trough is a peak, then the trough The waveform trend is the falling edge.
  • the fitting unit 220 is configured to obtain a breathing curve by fitting a respiratory waveform according to the peak and trough position information of the original waveform and the waveform trend.
  • the fitting unit 220 After acquiring the peak and trough position information of the original waveform, the fitting unit 220 respectively fits the respiratory waveform according to the peak and trough position information and the waveform trend of the original waveform.
  • the adjacent waveform peaks and trough positions of the original waveform are used as the starting point and the ending point, and the respiratory waveform is fitted to the rising edge waveform to obtain a rising edge breathing curve; the respiratory waveform is fitted according to the falling edge waveform to obtain a falling edge breathing curve.
  • the interference cancellation unit 230 is configured to remove interference of the breathing curve on the original waveform according to the breathing curve, and perform filtering to obtain a heartbeat waveform.
  • the de-interference unit 230 removes the interference of the breathing curve on the original waveform to obtain a heartbeat waveform.
  • the obtained original waveform corresponds to the position of the breathing curve, on the original waveform, the breathing curve of the corresponding position is subtracted, and the same position on the waveform, the amplitude of the sampling point is subtracted, and the breathing curve is performed.
  • the obtained waveform is the waveform after removing the respiratory interference.
  • the obtained waveform after removing the respiratory interference is subjected to high-low-pass filtering.
  • the frequency range of the heartbeat waveform is set to 0.8-1.6 Hz, and the waveform obtained by the high-low-pass filtering is in the range of 0.8-1.6 Hz. Waveform.
  • the waveform trending unit 210 obtains a waveform trend according to the peak and valley position information of the original waveform; then, the fitting unit 220 obtains a breathing curve by fitting the respiratory waveform according to the peak and valley position information of the original waveform and the waveform trend. Then, the de-interference unit 230 removes the interference of the breathing curve on the original waveform according to the obtained breathing curve, and obtains a heartbeat waveform.
  • the waveform trend is obtained according to the peak and trough position information of the original waveform, and a more accurate breathing curve is obtained by fitting, and then the original waveform is subtracted from the corresponding position of the respiratory curve to remove the respiratory interference, and filtering is performed to remove the clutter interference, and the accurate is obtained. , interference-free heartbeat waveform.
  • the third embodiment of the heartbeat signal processing apparatus of the present application provides a heartbeat signal processing apparatus 09.
  • the breathing curve includes a rising edge breathing curve and a falling edge breathing curve.
  • the heartbeat waveform includes a rising edge heartbeat curve and a falling edge heartbeat curve, and the de-interference unit 230 includes:
  • the waveform positioning sub-unit 231 is configured to obtain a rising edge waveform curve and a falling edge waveform curve of the original waveform according to the peak and valley position information of the original waveform and the waveform trend.
  • the waveform positioning sub-unit 231 removes the interference of the breathing curve on the original waveform to obtain a heartbeat waveform.
  • the waveform positioning sub-unit 231 respectively fits the rising edge waveform and the falling edge waveform according to the peak and valley position information of the original waveform, and the fitted breathing curve includes the rising edge breathing curve and the falling edge. Breathing curve.
  • the waveform positioning sub-unit 231 acquires a rising edge waveform curve and a falling edge waveform curve of the original waveform according to the peak and valley position information and the waveform trend of the original waveform. For example, if the waveforms of two adjacent points tend to The potential is the rising edge waveform, and according to the position information of the two points, the original waveform is obtained as the rising edge breathing curve.
  • the rising edge waveform of the original waveform corresponds to the rising edge breathing curve obtained by the fitting, and the falling edge of the original waveform is matched with the falling edge of the breathing curve.
  • the sub-unit 232 is configured to remove the rising edge breathing curve on the rising edge waveform and perform filtering to obtain a rising edge heartbeat curve; removing the falling edge breathing curve on the falling edge waveform curve, and Filtering is performed to obtain a falling edge heartbeat curve.
  • the removal sub-unit 232 removes the respiratory signal interference of the original waveform, specifically, as an embodiment, on the original waveform, the removal sub-unit 232 processes the rising edge waveform and the falling edge waveform respectively.
  • the rising edge waveform is Ss
  • the falling edge waveform is Sx
  • the rising edge breathing curve is ys
  • the falling edge breathing curve is wx. Since the original waveform corresponds to the position of the breathing curve, the rising edge waveform Ss corresponds to the rising edge breathing curve ys, and the falling edge waveform curve Sx corresponds to the falling edge breathing curve wx.
  • the amplitude of the point on the curve Hs is the difference between the amplitude of the rising edge waveform point and the rising edge breathing curve point of the same position.
  • the curve Hs is subjected to high-low-pass filtering.
  • the frequency range of the heartbeat waveform is set to 0.8-1.6 Hz
  • the waveform obtained by the high-low-pass filtering is a rising edge heartbeat curve in the range of 0.8-1.6 Hz.
  • the amplitude of the point on the falling edge of the heartbeat curve is the difference between the amplitude of the falling curve edge point and the falling edge breathing curve point of the same position.
  • the curve Hx is subjected to high-low-pass filtering.
  • the frequency range of the heartbeat waveform is set to 0.8-1.6 Hz
  • the waveform obtained by the high-low-pass filtering is a rising edge heartbeat curve in the range of 0.8-1.6 Hz.
  • the removal sub-unit 232 obtains a rising edge heartbeat curve and a falling edge heartbeat curve, that is, a heartbeat waveform is obtained.
  • the breathing curve includes a rising edge breathing curve and a falling edge breathing curve
  • the waveform positioning subunit 231 obtains a rising edge waveform curve and a falling edge waveform curve of the original waveform
  • the unit 232 removes the rising edge breathing curve on the rising edge waveform curve and performs filtering to obtain a rising edge heartbeat curve
  • the rising edge waveform and the falling edge waveform are located, and the rising edge breathing curve and the falling edge breathing curve are respectively obtained according to the rising edge waveform and the falling edge waveform, and then the rising edge waveform and the falling edge waveform of the original waveform are obtained.
  • the interference processing is performed separately to avoid the error caused by the interference caused by the peak of the respiratory signal and the peak of the heartbeat signal, and the accurate acquisition of the heartbeat signal is realized.
  • the fourth embodiment of the heartbeat signal processing apparatus of the present application provides a heartbeat signal processing apparatus 010.
  • the calculation module 300 includes:
  • the calculating subunit 310 is configured to calculate a corresponding rising edge heart rate and a falling edge heart rate according to the rising edge heartbeat curve and the falling edge heartbeat curve, respectively.
  • the calculation subunit 310 calculates the heart rate value, respectively.
  • the calculation subunit 310 acquires peak and trough position information of the rising edge heartbeat curve and the falling edge heartbeat curve.
  • the calculation sub-unit 310 obtains the sampling interval time of the two peaks or troughs according to the distance between the two peaks or the trough sampling point positions of the rising edge heartbeat curve, and calculates the sampling time node according to the interval time of the two peaks or troughs. Heart rate value. Thereby, the rising edge heart rate value HR1 corresponding to the rising edge heartbeat curve is obtained.
  • the calculation subunit 310 obtains the sampling interval time of two peaks or troughs according to the distance between two peaks or valley sampling points of the falling edge heartbeat curve, and calculates the sampling time according to the interval between the two peaks or troughs.
  • the heart rate value of the node Thereby, the falling edge heart rate value HR2 corresponding to the falling edge heartbeat curve is obtained.
  • the exception processing sub-unit 320 is configured to remove abnormal data in the rising edge heart rate and the falling edge heart rate according to a preset processing rule.
  • the abnormality processing sub-unit 320 After obtaining the rising edge heart rate value HR1 and the falling edge heart rate value HR2, the abnormality processing sub-unit 320 processes the obtained heart rate value according to a preset rule to remove the abnormal data.
  • preset processing rules include a tradeoff, a range limit, and a smoothing process.
  • the abnormality processing sub-unit 320 rounds off the initial waveform within a certain time range when starting the heartbeat signal sampling, and rounds off the end waveform within a certain time range when the heartbeat sampling is ended.
  • the initial waveform and the end waveform may cause waveform anomalies due to external factors such as initialization of the sampling instrument.
  • the excessive or too small heart rate value is removed.
  • the normal heart rate value of the human body set the heart rate range to 40-120 times per minute, according to which the unreasonable too fast heartbeat or slow heartbeat caused by getting up or the like is removed.
  • the obtained heart rate value of a sampling time node is smaller than the heart rate value obtained by the previous time node of the time node, it is less than one third of the previous heart rate value or greater than three thirds of the previous heart rate value. Fourth, the determined heart rate value is too large to remove the heart rate value.
  • the heart rate value corresponding to the rising edge heartbeat curve or the falling edge heartbeat curve is discarded.
  • the preset processing rule may also include other abnormal data processing modes, which can be flexibly set according to actual needs.
  • the abnormality processing sub-unit 320 obtains the rising edge heart rate data and the falling edge heart rate data after the abnormal data is removed.
  • the integrated processing sub-unit 330 is configured to obtain a heart rate value according to the processed rising edge heart rate and the falling edge heart rate.
  • the comprehensive processing sub-unit 330 comprehensively processes the rising edge heart rate and the falling edge heart rate to obtain the heart rate value corresponding to the original waveform.
  • the integrated processing sub-unit 330 averages the obtained heart rate values, and the obtained heart rate value is the heart rate value of the original waveform sampling time.
  • the integrated processing sub-unit 330 sorts the obtained rising edge heart rate and falling edge heart rate by corresponding sampling time nodes, and divides into a plurality of sampling regions in chronological order. Then, the heart rate average of each sampling area is separately obtained, and then the heart rate value of the original waveform sampling time is obtained by comprehensive processing.
  • the embodiment of the present application adopts segment processing, and when the heartbeat signal is collected, the original waveform of the preset length is processed as a piece of original waveform, and the heart rate value corresponding to the original waveform of the heart rate is calculated.
  • the preset length can be flexibly set according to actual needs.
  • the waveforms of the 70 points are processed as a piece of original waveform to obtain a corresponding heartbeat waveform.
  • the calculation module 300 obtains multiple segments of the heart. Jump waveform.
  • the calculation module 300 obtains the multi-segment heartbeat waveform, and removes the heartbeat waveform within the preset time range when starting sampling and ending sampling.
  • the calculation module 300 calculates a plurality of heart rate values.
  • the calculation module 300 performs comprehensive processing on the obtained plurality of heart rate values, removes the abnormal data, calculates an average heart rate value of the multi-segment heartbeat waveform, and obtains a final heart rate value.
  • the calculating sub-unit 310 calculates the corresponding rising edge heart rate and falling edge heart rate according to the rising edge heartbeat curve and the falling edge heartbeat curve respectively; then, the abnormality processing sub-unit 320 removes the rising according to the preset processing rule. The abnormal data in the heart rate and the falling edge heart rate; then, the integrated processing sub-unit 330 obtains the heart rate value corresponding to the original waveform according to the processed rising edge heart rate and the falling edge heart rate.
  • the obtained heart rate value is comprehensively processed to obtain the final heart rate value, and the heartbeat signal is processed to obtain the heart rate value.
  • a heartbeat signal processing system provides a heartbeat signal processing system 011, which includes a piezoelectric sensor A, an analog to digital converter B, and a heart rate monitoring CPU C, wherein:
  • the piezoelectric sensor A is used to collect a piezoelectric analog signal.
  • the piezoelectric vibration sensor A obtains the body vibration signal of the user's breathing and heartbeat.
  • the piezoelectric sensor is placed in the mattress or under the bed sheet to obtain the chest cavity vibration signal and the heartbeat caused by the breathing when the human body sleeps. Regular body vibration signals.
  • the piezoelectric sensor A can be set to 1.2 meters long, 1-2 mm thick, and 5 cm wide, and placed in a position close to the chest when the human body is lying down, and continuously collects piezoelectric signals.
  • the signal collected by the piezoelectric sensor A is an analog signal.
  • Piezoelectric sensor A then sends the resulting analog signal to analog to digital converter B.
  • the analog-to-digital converter B is configured to convert the piezoelectric analog signal into a digital signal to obtain a heartbeat signal original waveform.
  • the analog to digital converter B can employ an A/D converter (Analog to Digital Converter, analog to digital converter).
  • A/D converter Analog to Digital Converter, analog to digital converter
  • the A/D converter After receiving the analog signal sent by the piezoelectric sensor A, the A/D converter converts the analog line number into a digital signal, and continuously obtains the original waveform of the multi-segment heartbeat signal.
  • the A/D converter sends the obtained multi-segment heartbeat signal original waveforms to the heart rate monitoring CPU C in chronological order.
  • the heart rate monitoring CPU C includes an acquisition module 100, a waveform processing module 200, and a calculation module 300.
  • the heart rate monitoring CPU C processes the obtained multi-segment heartbeat signal original waveform separately.
  • the acquisition module 100 receives the original waveform of the heartbeat signal sent by the analog-to-digital converter B, and extracts a respiratory waveform having a frequency range of 0.1-0.5 Hz through high-low-pass filtering.
  • the acquisition module 100 then performs a differential threshold to obtain peaks and trough positions of the respiratory waveform. Then, based on the peak and valley positions of the respiratory waveform, the peaks and troughs in the range corresponding to the original waveform are found. Among them, the preset interval range can be flexibly set according to actual needs.
  • the acquisition module 100 obtains peak and trough position information of the original waveform.
  • the waveform processing module 200 determines the waveform trend based on the peak and valley position information of the original waveform. Then, according to the positions of the peaks and troughs, the adjacent peaks and troughs are taken as the starting point or the ending point, and according to the waveform trend, the rising edge breathing curve and the falling edge breathing curve are respectively obtained.
  • the waveform processing module 200 obtains a rising edge waveform curve and a falling edge waveform curve of the original waveform.
  • the rising edge waveform of the original waveform corresponds to the position of the rising edge breathing curve
  • the falling edge waveform curve corresponds to the falling edge breathing curve position.
  • the waveform processing module 200 subtracts the rising edge breathing curve of the corresponding position from the rising edge waveform curve, and performs high-low-pass filtering on the obtained waveform to obtain a rising edge heartbeat curve; and subtracts the falling edge of the corresponding position from the falling edge waveform curve. Curve and high-low-pass filtering the obtained waveform to obtain a falling edge heartbeat curve.
  • the calculation module 300 calculates the corresponding rising edge heart rate and falling edge heart rate according to the obtained rising edge heartbeat curve and the falling edge breathing curve respectively.
  • the calculation module 300 removes the abnormal data from the obtained rising edge heart rate and the falling edge heart rate, respectively, and obtains rising edge heart rate data and falling edge heart rate data within a reasonable range of the human heart rate.
  • the calculation module 300 averages the rising edge heart rate data and the falling edge heart rate data to obtain a heart rate value of the current original waveform.
  • the calculation module 300 discards the abnormal data, for example, the heart rate value corresponding to the heartbeat waveform with less than two heartbeats. Smoothing is performed to obtain a heart rate value within the final current detection time range.
  • the heartbeat signal processing system 011 includes a piezoelectric sensor A, an analog to digital converter B, and a heart rate monitoring CPU C, wherein the piezoelectric sensor A collects a piezoelectric analog signal and sends it to an analog to digital converter B; an analog to digital converter B converts the piezoelectric analog signal into a digital signal, and obtains the original waveform of the heartbeat signal, and sends it to the heart rate monitoring CPU C; the heart rate monitoring CPU C receives the original waveform of the heartbeat signal sent by the analog-to-digital converter B and processes it to remove the interference of the respiratory signal. , calculate the heart rate value.
  • the signal is collected by the piezoelectric sensor, the heart rate monitoring CPU removes the serious interference of the heartbeat signal, and the heartbeat waveform is calculated and optimized according to the obtained heartbeat waveform, and the final heart rate value is obtained, and the accuracy is high.
  • FIG. 12 is a schematic diagram showing the hardware structure of an electronic device 012 according to an embodiment of the present application. As shown in FIG. 12, the electronic device 012 includes:
  • processors 1210 and memory 1220 one processor 1210 is exemplified in FIG.
  • the processor 1210 and the memory 1220 may be connected by a bus or other means, as exemplified by a bus connection in FIG.
  • the memory 1220 is a non-volatile computer readable storage medium, and can be used for storing a non-volatile software program, a non-volatile computer executable program, and a module, such as a program corresponding to the heartbeat signal processing method in the embodiment of the present application. Instruction/module.
  • the processor 1210 executes various functional applications and data processing of the server by executing non-volatile software programs, instructions, and modules stored in the memory 1220, that is, implementing the heartbeat signal processing method of the above method embodiment.
  • the memory 1220 may include a storage program area and an storage data area, wherein the storage program area may store an operating system, an application required for at least one function; the storage data area may store data created according to use of the heartbeat signal processing device, and the like.
  • memory 1220 can include high speed random access memory, and can also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device.
  • memory 1220 can optionally include memory remotely located relative to processor 1210, which can be connected to the heartbeat signal processing device over a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • the one or more modules are stored in the memory 1220, and when executed by the one or more processors 1210, perform a heartbeat signal processing method in any of the above method embodiments, for example, performing the above described FIG.
  • the module 100 in FIG. 7 is implemented. 300, the functions of modules 100-300 in Figure 8, modules 100-300 in Figure 9, and modules 100-330 in Figure 10.
  • the electronic device of the embodiment of the present application exists in various forms, including but not limited to:
  • Server A device that provides computing services.
  • the server consists of a processor, a hard disk, a memory, a system bus, etc.
  • the server is similar to a general-purpose computer architecture, but because of the need to provide highly reliable services, processing power and stability High reliability in terms of reliability, security, scalability, and manageability.
  • Embodiments of the present application provide a non-transitory computer readable storage medium storing computer-executable instructions that are executed by one or more processors, such as in FIG.
  • the processor 1210 is configured to enable the one or more processors to perform the heartbeat signal processing method in any of the foregoing method embodiments, for example, to perform the method steps S10 to S30 in FIG. 1 described above, in FIG. Method steps S10 to S30, method steps S10 to S30 in FIG. 3, and method steps S10 to S33 in FIG. 5, implementing modules 100-300 in FIG. 7, and modules 100-300 in FIG. 8, FIG.
  • the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, ie may be located A place, or it can be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • the implementation can be implemented by means of software plus a general hardware platform, and of course also by hardware.
  • a person skilled in the art can understand that all or part of the process of implementing the above embodiments can be completed by a computer program to instruct related hardware, and the program can be stored in a computer readable storage medium. When executed, the flow of an embodiment of the methods as described above may be included.
  • the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

Abstract

一种心跳信号处理方法、装置和系统，该方法包括：获取心跳信号原始波形，得到所述原始波形的波峰和波谷位置信息（S10）；根据所述原始波形的波峰和波谷位置信息拟合得到呼吸曲线，并去除所述呼吸曲线对所述原始波形的干扰，得到心跳波形（S20）；根据所述心跳波形，计算得到心率值（S30）。该方法通过拟合呼吸曲线，实现了去除原始波形上由呼吸引起的干扰，解决了心跳信号受呼吸信号影响而导致误差大的问题，并根据心跳信号计算得到了精准心率值。

Description

一种心跳信号处理方法、装置和系统
相关申请的交叉参考
本申请要求于2016年1月5日提交中国专利局，申请号为CN201610005670.1，发明名称为“心跳信号处理方法、装置和系统”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及信号处理技术领域，尤其涉及一种心跳信号处理方法、装置和系统。
背景技术
当前，睡眠监控主要是通过雷达红外、超声波检测、压电传感器，获取人们的心跳信号、呼吸信号等，进行处理得到人们睡眠时的心率值等各项睡眠参数。其中，雷达红外和超声检测手段成本较高，并且会产生红外或者超声辐射，检测效果也不理想。
通过压电传感器监测睡眠参数的原理是，通过灵敏度较高的压电薄膜传感器监测受测者由呼吸引起的胸腔收扩运动和心跳震颤引起的机械信号，进而分析信号得到呼吸、心跳等参数。
因而，使用压电传感器监测心率时，容易受到各种信号干扰，例如：较强烈的呼吸干扰、人体生理信号(包括体动等)干扰、外界环境干扰等。其中，呼吸干扰影响最大，因为呼吸信号较为强烈，基本淹没了心跳信号。另一方面，加上体动和外界环境干扰，以及同心率频率段的信号干扰，使得原本就微弱的心跳信号更加弱小，加大处理难度。如何在干扰下进行心跳信号处理，得到有效的心跳信号，成为当前心率监测的重要研究课题。
发明内容
本申请的主要目的在于提供一种心跳信号处理方法、装置和系统，旨在解决心跳信号处理准确性差、误差大的技术问题。
为实现上述目的，本申请实施例提供一种心跳信号处理方法，所述心跳信号处理方法包括以下步骤：
获取心跳信号原始波形，得到所述原始波形的波峰和波谷位置信息；
根据所述原始波形的波峰和波谷位置信息拟合得到呼吸曲线，并去除所述呼吸曲线对所述原始波形的干扰，得到心跳波形；
根据所述心跳波形，计算得到心率值。
优选的，所述根据所述原始波形的波峰和波谷位置信息拟合得到呼吸曲线，并去除所述呼吸曲线对所述原始波形的干扰，得到心跳波形的步骤包括：
根据所述原始波形的波峰和波谷位置信息，得到波形趋势；
根据所述原始波形的波峰和波谷位置信息、所述波形趋势，拟合呼吸波形得到呼吸曲线；
根据所述呼吸曲线，去除所述呼吸曲线对所述原始波形的干扰，并进行滤波，得到心跳波形。
优选的，所述呼吸曲线包括上升沿呼吸曲线和下降沿呼吸曲线，所述心跳波形包括上升沿心跳曲线和下降沿心跳曲线，所述根据所述呼吸曲线，去除所述呼吸曲线对所述原始波形的干扰，并进行滤波，得到心跳波形的步骤包括：
根据所述原始波形的波峰和波谷位置信息、所述波形趋势，得到原始波形的上升沿波形曲线和下降沿波形曲线；
在所述上升沿波形曲线上去除所述上升沿呼吸曲线，并进行滤波，得到上升沿心跳曲线；
在所述下降沿波形曲线上去除所述下降沿呼吸曲线，并进行滤波，得到下降沿心跳曲线。
优选的，所述根据所述心跳波形，计算得到心率值的步骤包括：
分别根据所述上升沿心跳曲线和所述下降沿心跳曲线，计算得到对应的上升沿心率和下降沿心率；
根据预设的处理规则，去除所述上升沿心率和下降沿心率中的异常数据；
根据所述处理后的上升沿心率和下降沿心率，得到心率值。
此外，为实现上述目的，本申请实施例还提供一种心跳信号处理装置，所述心跳信号处理装置包括：
获取模块，用于获取心跳信号原始波形，得到所述原始波形的波峰和波谷位置信息；
波形处理模块，用于根据所述原始波形的波峰和波谷位置信息拟合得到呼吸曲线，并去除所述呼吸曲线对所述原始波形的干扰，得到心跳波形；
计算模块，用于根据所述心跳波形，计算得到心率值。
优选的，所述波形处理模块包括：
波形趋势单元，用于根据所述原始波形的波峰和波谷位置信息，得到波形趋势；
拟合单元，用于根据所述原始波形的波峰和波谷位置信息、所述波形趋势，拟合呼吸波形得到呼吸曲线；
去干扰单元，用于根据所述呼吸曲线，去除所述呼吸曲线对所述原始波形的干扰，并进行滤波，得到心跳波形。
优选的，所述呼吸曲线包括上升沿呼吸曲线和下降沿呼吸曲线，所述心跳波形包括上升沿心跳曲线和下降沿心跳曲线，所述去干扰单元包括：
波形定位子单元，用于根据所述原始波形的波峰和波谷位置信息、所述波形趋势，得到原始波形的上升沿波形曲线和下降沿波形曲线；
去除子单元，用于在所述上升沿波形曲线上去除所述上升沿呼吸曲线，并进行滤波，得到上升沿心跳曲线；在所述下降沿波形曲线上去除所述下降沿呼吸曲线，并进行滤波，得到下降沿心跳曲线。
优选的，所述计算模块包括：
计算子单元，用于分别根据所述上升沿心跳曲线和所述下降沿心跳曲线，计算得到对应的上升沿心率和下降沿心率；
异常处理子单元，用于根据预设的处理规则，去除所述上升沿心率和下降沿心率中的异常数据；
综合处理子单元，用于根据所述处理后的上升沿心率和下降沿心率，得到心率值。
此外，为实现上述目的，本申请实施例还提供一种心跳信号处理系统，其 特征在于，所述心跳信号处理系统包括压电传感器、模数转换器和心率监测CPU，其中：
所述压电传感器用于，采集压电模拟信号；
所述模数转换器用于，将所述压电模拟信号转换为数字信号，得到心跳信号原始波形；
所述心率监测CPU，包括获取模块、波形处理模块和计算模块。
本申请实施例还提供了一种电子设备，包括：
至少一个处理器；以及，
与所述至少一个处理器通信连接的存储器；其中，
所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行如上所述的方法。
本申请实施例提出的一种心跳信号处理方法、装置和系统，通过获取心跳信号原始波形，得到原始波形的波峰和波谷位置信息；然后，根据原始波形的波峰和波谷位置信息拟合得到呼吸曲线，并根据呼吸曲线对原始波形去除干扰，得到心跳波形；然后，根据得到的心跳波形，计算得到心率值。本申请实施例通过拟合呼吸曲线，实现了去除原始波形上呼吸引起的干扰，解决了心跳信号受呼吸信号影响而导致误差大的问题，根据心跳信号计算得到的了精准心率值。
附图说明
一个或多个实施例通过与之对应的附图中的图片进行示例性说明，这些示例性说明并不构成对实施例的限定，附图中具有相同参考数字标号的元件表示为类似的元件，除非有特别申明，附图中的图不构成比例限制。
图1为本申请心跳信号处理方法第一实施例的流程示意图；
图2为本申请心跳信号处理方法第二实施例的流程示意图；
图3为本申请心跳信号处理方法第三实施例的流程示意图；
图4为本申请实施例的一种心跳信号原始波形处理流程示意图；
图5为本申请心跳信号处理方法第四实施例的流程示意图；
图6为本申请实施例中的一种心跳波形示意图；
图7为本申请心跳信号处理装置第一实施例的功能模块示意图；
图8为本申请心跳信号处理装置第二实施例的功能模块示意图；
图9为本申请心跳信号处理装置第三实施例的功能模块示意图；
图10为本申请心跳信号处理装置第四实施例的功能模块示意图；
图11为本申请心跳信号处理系统第一实施例的模块示意图；
图12是本申请实施例提供的电子设备的硬件结构示意图。
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
本申请实施例的主要解决方案是：获取心跳信号原始波形，得到原始波形的波峰和波谷位置信息；然后，根据原始波形的波峰和波谷位置信息拟合得到呼吸曲线，并根据呼吸曲线对原始波形去除干扰，得到心跳波形；然后，根据得到的心跳波形，计算得到心率值。
由于现有技术不能有效的去除呼吸信号的干扰，根据心跳信号得到的心率值误差大。
本申请实施例提供一种解决方案，通过拟合呼吸曲线，实现了去除原始波形上呼吸引起的干扰，解决了心跳信号受呼吸信号影响而导致误差大的问题，根据心跳信号计算得到的了精准心率值。
参照图1，本申请心跳信号处理方法第一实施例提供一种心跳信号处理方法，所述心跳信号处理方法包括：
步骤S10、获取心跳信号原始波形，得到所述原始波形的波峰和波谷位置信息。
本实施例主要用于心跳信号的处理，得到心率。
具体的，作为一种实施方式，本实施例通过压电传感器获取用户呼吸和心跳的身体振动信号，例如，将压电传感器置于床垫中或床单下，获取人体睡觉时，呼吸引起的胸腔收括振动信号和心跳引起的有规律的身体震动信号。
然后，压电传感器将得到的模拟信号发送给A/D转换器(Analog to Digital  Converter，模数转换器)。
A/D转换器收到模拟信号后，将模拟线号转换为数字信号，得到心跳信号原始波形，并将得到的心跳信号原始波形发送给心率监测CPU，由心率监测CPU进行心跳信号的处理。
心率监测CPU接收A/D转换器发送的采样波(A/D Sample)，得到心跳信号的原始波形后，首先获取原始波形的波峰和波谷位置信息。
作为一种实施方式，心率监测CPU可直接进行差分阈值，得到原始波形的波峰(Sample-Peak)和波谷(Sample-Trough)位置信息并存储。
原始波形的波峰位置信息包括原始波形中各波峰的位置和振幅，原始波形的波谷位置信息包括原始波形中各波谷的位置和振幅。
作为一种实施方式，首先，心率监测CPU通过高低通滤波，得到呼吸波形。
需要说明的是，由于人体正常的呼吸周期约为1.5秒到5s一次，因此，可根据呼吸周期灵活设置高低通滤波的频率截止范围。例如，本实施例设置呼吸波形的频率范围为0.1—0.5Hz，则通过高低通滤波得到的波形即为0.1—0.5Hz范围内的波形。
在得到呼吸波形后，心率监测CPU进行差分阈值，得到呼吸波形的波峰(Breath-Peak)和波谷(Breath-Trough)位置信息。
然后，分别根据呼吸波形的波峰和波谷的位置，查找原始波形上对应的位置的波峰和波谷。
例如，以当前呼吸波形波峰的位置为基准，设置区间范围，查找原始波形对应位置区间范围内的振幅最大点，以此点为原始波形对应位置的波峰。同样的，以当前呼吸波形波谷的位置为基准，设置区间范围，查找原始波形对应位置区间范围内的振幅最小点，以此点为原始波形对应位置的峰谷。
根据呼吸波形的波峰和波谷的位置，查找原始波形上对应的位置的波峰和波谷，可以避免直接获取原始波形的波峰和波谷时，由于干扰因素导致获取的波峰、波谷位置不是呼吸波形的准确位置，从而导致拟合的呼吸曲线不准确。
需要说明的是，预设的区间范围可以根据实际需要灵活设置，例如取基准位置前后2秒内采集到的数据点为预设的区间范围。
由此，心率监测CPU根据呼吸波形的波峰和波谷位置信息得到了原始波形的波峰和波谷位置信息。
步骤S20、根据所述原始波形的波峰和波谷位置信息拟合得到呼吸曲线，并去除所述呼吸曲线对所述原始波形的干扰，得到心跳波形。
在获取原始波形的波峰和波谷位置信息后，心率监测CPU根据得到的原始波形的波峰和波谷位置信息拟合得到呼吸曲线，并去除呼吸曲线对原始波形的干扰，得到心跳波形。
具体的，作为一种实施方式，首先，心率监测CPU根据原始波形的波峰和波谷位置信息，判断波形趋势。
例如，根据得到的原始波形的波峰和波谷位置信息，若原始波形上，一个波峰的前一个点为波谷，则次波峰的波形趋势为上升沿(Wave-S)；一个波谷的前一个点为波峰，则次波峰的波形趋势为下降沿(Wave-X)。
然后，根据原始波形相邻波峰和波谷的位置信息，及波形趋势，在原始波形上拟合呼吸波形，得到呼吸曲线。
然后，根据呼吸曲线去除原始波形上的呼吸干扰，得到去除呼吸干扰后的波形。
对得到的去除呼吸干扰后波形进行滤波，根据人体正常心跳周期，设置心跳波形的频率范围为0.8—1.6Hz，则通过高低通滤波得到的波形即为0.8—1.6Hz范围内的波形。
由此，得到心跳波形。
步骤S30、根据所述心跳波形，计算得到心率值。
在得到心跳波形后，心率监测CPU根据心跳波形计算心率值。
具体的，作为一种实施方式，首先，去除心跳波形的开始部分和结束部分，用于去除开始部分和结束部分采集的不规律心跳信号。
然后，根据心跳波形的相邻波峰采样点距离得到两个波峰的采样间隔时间，根据两个波峰的间隔时间，计算得到采样时间节点的心率值。同理，也可以根据波谷的位置信息计算得到心率值。
然后，对计算得到心率值进行取舍、范围限制等优化。例如，根据人体正常心率值，设置心率范围为每分钟40-120次，根据此范围去掉由起床等原因引起的不合理的过快心跳或慢心跳；若得到的某个采样时间节点的心率值，与此时间节点的前一个时间节点得到的心率值相比，小于前一个心率值的三分之一或大于前一个心率值的三分之四，则判定得到的心率值浮动过大，去除此心率 值。
然后，平滑处理得到的心率值，取连续的数个心率值的平均值，得到的心率值即为采样时间内的心率值。
在本实施例中，心率监测CPU获取心跳信号原始波形，得到原始波形的波峰和波谷位置信息；然后，根据原始波形的波峰和波谷位置信息拟合得到呼吸曲线，并根据呼吸曲线对原始波形去除干扰，得到心跳波形；然后，根据得到的心跳波形，计算得到心率值。本实施例通过拟合呼吸曲线，实现了去除原始波形上呼吸引起的干扰，解决了心跳信号受呼吸信号影响而导致误差大的问题，根据心跳信号计算得到了精准心率值。
进一步的，参照图2，本申请心跳信号处理方法第二实施例提供一种心跳信号处理方法，基于上述图1所示的实施例，所述步骤S20包括：
步骤S21、根据所述原始波形的波峰和波谷位置信息，得到波形趋势。
在获取原始波形的波峰和波谷位置信息后，心率监测CPU根据得到的原始波形的波峰和波谷位置信息拟合得到呼吸曲线，并去除呼吸曲线对原始波形的干扰，得到心跳波形。
作为另一种实施方式，首先，心率监测CPU根据原始波形的波峰和波谷位置信息，判断波形趋势，定位原始波形的上升沿和下降沿。
例如，根据得到的原始波形的波峰和波谷位置信息，若原始波形上，一个波峰的前一个点为波谷，则此波峰的波形趋势为上升沿；一个波谷的前一个点为波峰，则此波谷的波形趋势为下降沿。
步骤S22、根据所述原始波形的波峰和波谷位置信息、所述波形趋势，拟合得到呼吸曲线。
在获取原始波形的波峰和波谷位置信息后，心率监测CPU根据原始波形的波峰和波谷位置信息、波形趋势分别拟合呼吸波形。
将原始波形相邻波峰和波谷位置，作为起始点和结束点，对上升沿波形拟合呼吸波形，得到上升沿呼吸曲线；根据下降沿波形拟合呼吸波形，得到下降沿呼吸曲线。
由此，连接相邻的、有一端点相同的上升沿呼吸曲线和下降沿呼吸曲线， 得到原始波形上拟合的呼吸曲线。
步骤S23、根据所述呼吸曲线，去除所述呼吸曲线对所述原始波形的干扰，并进行滤波，得到心跳波形。
在得到拟合的呼吸曲线后，心率监测CPU去除呼吸曲线对原始波形的干扰，得到心跳波形。
具体的，作为一种实施方式，由于得到的原始波形与呼吸曲线位置对应，在原始波形上，减去对应位置的呼吸曲线，通过波形上相同位置，采样点振幅的相减，进行呼吸曲线的去除或削弱，得到的波形即为去除呼吸干扰后的波形。
然后，对得到的去除呼吸干扰后波形进行高低通滤波，根据人体正常心跳周期，设置心跳波形的频率范围为0.8—1.6Hz，则通过高低通滤波得到的波形即为0.8—1.6Hz范围内的波形。
由此，得到心跳波形。
在本实施例中，心率监测CPU根据原始波形的波峰和波谷位置信息，得到波形趋势；然后，根据原始波形的波峰和波谷位置信息、波形趋势，拟合呼吸波形得到呼吸曲线；然后，根据得到的呼吸曲线，去除呼吸曲线对原始波形的干扰，得到心跳波形。本实施例根据原始波形的波峰和波谷位置信息得到波形趋势，拟合得到更准确的呼吸曲线，然后将原始波形减去对应位置的呼吸曲线去除呼吸干扰，并进行滤波去除杂波干扰，得到准确的、无干扰的心跳波形。
进一步的，参照图3，本申请心跳信号处理方法第三实施例提供一种心跳信号处理方法，基于上述图2所示的实施例，所述呼吸曲线包括上升沿呼吸曲线和下降沿呼吸曲线，所述心跳波形包括上升沿心跳曲线和下降沿心跳曲线，所述步骤S23包括：
步骤S231、根据所述原始波形的波峰和波谷位置信息、所述波形趋势，得到原始波形的上升沿波形曲线和下降沿波形曲线。
在得到拟合的呼吸曲线后，心率监测CPU去除呼吸曲线对原始波形的干扰，得到心跳波形。
具体的，作为一种实施方式，心率监测CPU根据原始波形的波峰和波谷位置信息，针对上升沿波形和下降沿波形分别拟合，拟合得到的呼吸曲线包括上 升沿呼吸曲线和下降沿呼吸曲线。
心率监测CPU根据原始波形的波峰和波谷位置信息、波形趋势，获取原始波形的上升沿波形曲线和下降沿波形曲线。例如，若相邻两点的波形趋势为上升沿波形，则根据这两点的位置信息，得到这段原始波形为上升沿呼吸曲线。
根据波峰和波谷位置，原始波形的上升沿波形曲线与拟合得到的上升沿呼吸曲线对应，原始波形的下降沿波形曲线与拟合得到的下降沿呼吸曲线。
步骤S232、在所述上升沿波形曲线上去除所述上升沿呼吸曲线，并进行滤波，得到上升沿心跳曲线；在所述下降沿波形曲线上去除所述下降沿呼吸曲线，并进行滤波，得到下降沿心跳曲线。
心率监测CPU去除原始波形的呼吸信号干扰时，具体的，作为一种实施方式，在原始波形上，心率监测CPU分别对上升沿波形曲线和下降沿波形曲线进行处理。
参照图4，取上升沿波形曲线为Ss，下降沿波形曲线为Sx，上升沿呼吸曲线为ys，下降沿呼吸曲线为wx。由于原始波形与呼吸曲线位置对应，上升沿波形曲线Ss对应上升沿呼吸曲线ys，下降沿波形曲线Sx对应下降沿呼吸曲线wx。
则上升沿波形曲线减去上升沿呼吸曲线，得到曲线Hs，曲线Hs＝Ss-ys。曲线Hs上点的振幅，为相同位置的上升沿波形曲线点与上升沿呼吸曲线点的振幅之差。
然后，对曲线Hs进行高低通滤波，根据人体正常心跳周期，设置心跳波形的频率范围为0.8—1.6Hz，则通过高低通滤波得到的波形即为0.8—1.6Hz范围内的上升沿心跳曲线。
同理，下降沿波形曲线减去下降沿呼吸曲线，得到曲线Hx，曲线Hx＝Sx-wx。下降沿心跳曲线上点的振幅，为相同位置的下降沿波形曲线点与下降沿呼吸曲线点的振幅之差。
然后，对曲线Hx进行高低通滤波，根据人体正常心跳周期，设置心跳波形的频率范围为0.8—1.6Hz，则通过高低通滤波得到的波形即为0.8—1.6Hz范围内的上升沿心跳曲线。
由此，心率监测CPU得到上升沿心跳曲线和下降沿心跳曲线，也即得到心跳波形。
本实施例中，呼吸曲线包括上升沿呼吸曲线和下降沿呼吸曲线，心率监测CPU首先得到原始波形的上升沿波形曲线和下降沿波形曲线；然后，在上升沿波形曲线上去除上升沿呼吸曲线，并进行滤波，得到上升沿心跳曲线；在下降沿波形曲线上去除下降沿呼吸曲线，并进行滤波，得到下降沿心跳曲线，由此，得到心跳波形。本实施例通过波形趋势，定位上升沿波形和下降沿波形，根据上升沿波形和下降沿波形分别拟合得到上升沿呼吸曲线和下降沿呼吸曲线，然后对原始波形的上升沿波形和下降沿波形的分别进行去干扰处理，避免呼吸信号的波峰与心跳信号的波峰一致而导致的去干扰产生的误差，实现了心跳信号的准确获取。
进一步的，参照图5，本申请心跳信号处理方法第四实施例提供一种心跳信号处理方法，基于上述图3所示的实施例，所述步骤S30包括：
步骤S31、分别根据所述上升沿心跳曲线和所述下降沿心跳曲线，计算得到对应的上升沿心率和下降沿心率。
在获取上升沿心跳曲线和下降沿心跳曲线后，心率监测CPU分别计算心率值。
具体的，作为一种实施方式，首先，心率监测CPU获取上升沿心跳曲线和下降沿心跳曲线的波峰、波谷位置信息。
然后，心率监测CPU根据上升沿心跳曲线相邻两个波峰或波谷采样点位置的距离，得到两个波峰或波谷的采样间隔时间，根据两个波峰或波谷的间隔时间，计算得到采样时间节点的心率值。由此，得到上升沿心跳曲线对应的上升沿心率值HR1。
同理，心率监测CPU根据下降沿心跳曲线相邻两个波峰或波谷采样点位置的距离，得到两个波峰或波谷的采样间隔时间，根据两个波峰或波谷的间隔时间，计算得到采样时间节点的心率值。由此，得到下降沿心跳曲线对应的下降沿心率值HR2。
步骤S32、根据预设的处理规则，去除所述上升沿心率和下降沿心率中的异常数据。
在得到上升沿心率值HR1和下降沿心率值HR2后，心率监测CPU根据预设的规则对得到心率值进行处理，去除异常数据。
具体的，作为一种实施方式，预设的处理规则包括取舍、范围限制、平滑处理。
首先，心率监测CPU舍去开始进行心跳信号采样时、一定时间范围内的初始波形，舍去结束心跳采样时、一定时间范围内的结束波形。初始波形和结束波形由于受到采样仪器的初始化等外部原因，会引起波形异常。
然后，根据预先设置的心率范围，去除过大或过小的心率值。根据人体正常心率值，设置心率范围为每分钟40-120次，根据此范围去掉由起床等原因引起的不合理的过快心跳或慢心跳。
另外，若得到的某个采样时间节点的心率值，与此时间节点的前一个时间节点得到的心率值相比，小于前一个心率值的三分之一或大于前一个心率值的三分之四，则判定得到的心率值浮动过大，去除此心率值。
另外，若一个上升沿心跳曲线或下降沿心跳曲线对应的心跳数量小于2个，则舍弃此上升沿心跳曲线或下降沿心跳曲线对应的心率值。
需要说明的是，预设的处理规则还可以包括其他异常数据处理方式，可根据实际需要灵活设置。
由此，心率监测CPU得到去除异常数据后的上升沿心率数据和下降沿心率数据。
步骤S33、根据所述处理后的上升沿心率和下降沿心率，得到心率值。
在得到去除异常数据后的上升沿心率数据和下降沿心率数据后，心率监测CPU综合处理上升沿心率和下降沿心率，得到原始波形对应的心率值。
具体的，作为一种实施方式，心率监测CPU对得到的心率值平均值，得到的心率值即为原始波形采样时间内的心率值。
或者，作为另一种实施方式，心率监测CPU将得到的上升沿心率和下降沿心率按对应的采样时间节点排序，并按时间顺序划分为数个采样区。然后，分别求各采样区的心率平均值，再综合处理得到原始波形采样时间内的心率值。
本申请实施例采用段处理，采集心跳信号时，将得到的预设长度的原始波形作为一段原始波形进行处理，计算得到心率此段原始波形对应的心率值。其中，预设的长度可根据实际需要灵活设置。
例如，每采集70个点后，将这70个点的波形作为一段原始波形进行处理，得到对应的心跳波形。参照图6所示的心跳波形图，心率监测CPU得到多段心 跳波形。
由于开始心跳信号采样和结束心跳信号采样时，心跳信号误差较大，因此心率监测CPU得到多段心跳波形后，去除开始采样和结束采样时预设时间范围内的心跳波形。
由此，心率监测CPU计算得到多个心率值。
然后，心率监测CPU对得到的多个心率值进行综合处理，去除异常数据，计算多段心跳波形的平均心率值，得到最终的心率值。
在本实施例中，心率监测CPU分别根据上升沿心跳曲线和下降沿心跳曲线，计算得到对应的上升沿心率和下降沿心率；然后，根据预设的处理规则，去除上升沿心率和下降沿心率中的异常数据；然后，根据处理后的上升沿心率和下降沿心率，得到原始波形对应的心率值。本实施例根据上升沿心跳曲线和下降沿心跳曲线分别计算得到心率之后，在对得到的心率值进行综合处理得到最终的心率值，实现了对心跳信号的处理，得到心率值。
参照图7，本申请心跳信号处理装置第一实施例提供一种心跳信号处理装置07，所述心跳信号处理装置07包括：
获取模块100，用于获取心跳信号原始波形，得到所述原始波形的波峰和波谷位置信息。
本实施例主要用于心跳信号的处理，得到心率。
具体的，作为一种实施方式，本实施例通过压电传感器获取用户呼吸和心跳的身体振动信号，例如，将压电传感器置于床垫中或床单下，获取人体睡觉时，呼吸引起的胸腔收括振动信号和心跳引起的有规律的身体震动信号。
然后，压电传感器将得到的模拟信号发送给A/D转换器(Analog to Digital Converter，模数转换器)。
A/D转换器收到模拟信号后，将模拟线号转换为数字信号，得到心跳信号原始波形，并将得到的心跳信号原始波形发送给获取模块100，由获取模块100进行心跳信号的处理。
获取模块100接收A/D转换器发送的采样波(A/D Sample)，得到心跳信号的原始波形后，首先获取原始波形的波峰和波谷位置信息。
作为一种实施方式，获取模块100可直接进行差分阈值，得到原始波形的 波峰(Sample-Peak)和波谷(Sample-Trough)位置信息并存储。
原始波形的波峰位置信息包括原始波形中各波峰的位置和振幅，原始波形的波谷位置信息包括原始波形中各波谷的位置和振幅。
作为一种实施方式，首先，获取模块100通过高低通滤波，得到呼吸波形。
需要说明的是，由于人体正常的呼吸周期约为1.5秒到5s一次，因此，可根据呼吸周期灵活设置高低通滤波的频率截止范围。例如，本实施例设置呼吸波形的频率范围为0.1—0.5Hz，则通过高低通滤波得到的波形即为0.1—0.5Hz范围内的波形。
在得到呼吸波形后，心率监测CPU进行差分阈值，得到呼吸波形的波峰(Breath-Peak)和波谷(Breath-Trough)位置信息。
然后，分别根据呼吸波形的波峰和波谷的位置，查找原始波形上对应的位置的波峰和波谷。
例如，以当前呼吸波形波峰的位置为基准，设置区间范围，查找原始波形对应位置区间范围内的振幅最大点，以此点为原始波形对应位置的波峰。同样的，以当前呼吸波形波谷的位置为基准，设置区间范围，查找原始波形对应位置区间范围内的振幅最小点，以此点为原始波形对应位置的峰谷。
根据呼吸波形的波峰和波谷的位置，查找原始波形上对应的位置的波峰和波谷，可以避免直接获取原始波形的波峰和波谷时，由于干扰因素导致获取的波峰、波谷位置不是呼吸波形的准确位置，从而导致拟合的呼吸曲线不准确。
需要说明的是，预设的区间范围可以根据实际需要灵活设置，例如取基准位置前后2秒内采集到的数据点为预设的区间范围。
由此，获取模块100根据呼吸波形的波峰和波谷位置信息得到了原始波形的波峰和波谷位置信息。
波形处理模块200，用于根据所述原始波形的波峰和波谷位置信息拟合得到呼吸曲线，并去除所述呼吸曲线对所述原始波形的干扰，得到心跳波形。
在获取原始波形的波峰和波谷位置信息后，波形处理模块200根据得到的原始波形的波峰和波谷位置信息拟合得到呼吸曲线，并去除呼吸曲线对原始波形的干扰，得到心跳波形。
具体的，作为一种实施方式，首先，波形处理模块200根据原始波形的波峰和波谷位置信息，判断波形趋势。
例如，根据得到的原始波形的波峰和波谷位置信息，若原始波形上，一个波峰的前一个点为波谷，则次波峰的波形趋势为上升沿(Wave-S)；一个波谷的前一个点为波峰，则次波峰的波形趋势为下降沿(Wave-X)。
然后，根据原始波形相邻波峰和波谷的位置信息，及波形趋势，在原始波形上拟合呼吸波形，得到呼吸曲线。
然后，根据呼吸曲线去除原始波形上的呼吸干扰，得到去除呼吸干扰后的波形。
对得到的去除呼吸干扰后波形进行滤波，根据人体正常心跳周期，设置心跳波形的频率范围为0.8—1.6Hz，则通过高低通滤波得到的波形即为0.8—1.6Hz范围内的波形。
由此，得到心跳波形。
计算模块300，用于根据所述心跳波形，计算得到心率值。
在得到心跳波形后，计算模块300根据心跳波形计算心率值。
具体的，作为一种实施方式，首先，去除心跳波形的开始部分和结束部分，用于去除开始部分和结束部分采集的不规律心跳信号。
然后，根据心跳波形的相邻波峰采样点距离得到两个波峰的采样间隔时间，根据两个波峰的间隔时间，计算得到采样时间节点的心率值。同理，也可以根据波谷的位置信息计算得到心率值。
然后，对计算得到心率值进行取舍、范围限制等优化。例如，根据人体正常心率值，设置心率范围为每分钟40-120次，根据此范围去掉由起床等原因引起的不合理的过快心跳或慢心跳；若得到的某个采样时间节点的心率值，与此时间节点的前一个时间节点得到的心率值相比，小于前一个心率值的三分之一或大于前一个心率值的三分之四，则判定得到的心率值浮动过大，去除此心率值。
然后，平滑处理得到的心率值，取连续的数个心率值的平均值，得到的心率值即为采样时间内的心率值。
在本实施例中，获取模块100获取心跳信号原始波形，得到原始波形的波峰和波谷位置信息；然后，波形处理模块200根据原始波形的波峰和波谷位置信息拟合得到呼吸曲线，并根据呼吸曲线对原始波形去除干扰，得到心跳波形； 然后，计算模块300根据得到的心跳波形，计算得到心率值。本实施例通过拟合呼吸曲线，实现了去除原始波形上呼吸引起的干扰，解决了心跳信号受呼吸信号影响而导致误差大的问题，本实施例根据心跳信号计算得到的心率值非常精准，接近于实际心率值。
进一步的，参照图8，本申请心跳信号处理装置第二实施例提供一种心跳信号处理装置08，基于上述图7所示的实施例，所述波形处理模块200包括：
波形趋势单元210，用于根据所述原始波形的波峰和波谷位置信息，得到波形趋势。
在获取原始波形的波峰和波谷位置信息后，波形处理模块200根据得到的原始波形的波峰和波谷位置信息拟合得到呼吸曲线，并去除呼吸曲线对原始波形的干扰，得到心跳波形。
作为另一种实施方式，首先，波形趋势单元210根据原始波形的波峰和波谷位置信息，判断波形趋势，定位原始波形的上升沿和下降沿。
例如，根据得到的原始波形的波峰和波谷位置信息，若原始波形上，一个波峰的前一个点为波谷，则此波峰的波形趋势为上升沿；一个波谷的前一个点为波峰，则此波谷的波形趋势为下降沿。
拟合单元220，用于根据所述原始波形的波峰和波谷位置信息、所述波形趋势，拟合呼吸波形得到呼吸曲线。
在获取原始波形的波峰和波谷位置信息后，拟合单元220根据原始波形的波峰和波谷位置信息、波形趋势分别拟合呼吸波形。
将原始波形相邻波峰和波谷位置，作为起始点和结束点，对上升沿波形拟合呼吸波形，得到上升沿呼吸曲线；根据下降沿波形拟合呼吸波形，得到下降沿呼吸曲线。
由此，连接相邻的、有一端点相同的上升沿呼吸曲线和下降沿呼吸曲线，得到原始波形上拟合的呼吸曲线。
去干扰单元230，用于根据所述呼吸曲线，去除所述呼吸曲线对所述原始波形的干扰，并进行滤波，得到心跳波形。
在得到拟合的呼吸曲线后，去干扰单元230去除呼吸曲线对原始波形的干扰，得到心跳波形。
具体的，作为一种实施方式，由于得到的原始波形与呼吸曲线位置对应，在原始波形上，减去对应位置的呼吸曲线，通过波形上相同位置，采样点振幅的相减，进行呼吸曲线的去除或削弱，得到的波形即为去除呼吸干扰后的波形。
然后，对得到的去除呼吸干扰后波形进行高低通滤波，根据人体正常心跳周期，设置心跳波形的频率范围为0.8—1.6Hz，则通过高低通滤波得到的波形即为0.8—1.6Hz范围内的波形。
由此，得到心跳波形。
在本实施例中，波形趋势单元210根据原始波形的波峰和波谷位置信息，得到波形趋势；然后，拟合单元220根据原始波形的波峰和波谷位置信息、波形趋势，拟合呼吸波形得到呼吸曲线；然后，去干扰单元230根据得到的呼吸曲线，去除呼吸曲线对原始波形的干扰，得到心跳波形。本实施例根据原始波形的波峰和波谷位置信息得到波形趋势，拟合得到更准确的呼吸曲线，然后将原始波形减去对应位置的呼吸曲线去除呼吸干扰，并进行滤波去除杂波干扰，得到准确的、无干扰的心跳波形。
进一步的，参照图9，本申请心跳信号处理装置第三实施例提供一种心跳信号处理装置09，基于上述图8所示的实施例，所述呼吸曲线包括上升沿呼吸曲线和下降沿呼吸曲线，所述心跳波形包括上升沿心跳曲线和下降沿心跳曲线，所述去干扰单元230包括：
波形定位子单元231，用于根据所述原始波形的波峰和波谷位置信息、所述波形趋势，得到原始波形的上升沿波形曲线和下降沿波形曲线。
在得到拟合的呼吸曲线后，波形定位子单元231去除呼吸曲线对原始波形的干扰，得到心跳波形。
具体的，作为一种实施方式，波形定位子单元231根据原始波形的波峰和波谷位置信息，针对上升沿波形和下降沿波形分别拟合，拟合得到的呼吸曲线包括上升沿呼吸曲线和下降沿呼吸曲线。
波形定位子单元231根据原始波形的波峰和波谷位置信息、波形趋势，获取原始波形的上升沿波形曲线和下降沿波形曲线。例如，若相邻两点的波形趋 势为上升沿波形，则根据这两点的位置信息，得到这段原始波形为上升沿呼吸曲线。
根据波峰和波谷位置，原始波形的上升沿波形曲线与拟合得到的上升沿呼吸曲线对应，原始波形的下降沿波形曲线与拟合得到的下降沿呼吸曲线。
去除子单元232，用于在所述上升沿波形曲线上去除所述上升沿呼吸曲线，并进行滤波，得到上升沿心跳曲线；在所述下降沿波形曲线上去除所述下降沿呼吸曲线，并进行滤波，得到下降沿心跳曲线。
去除子单元232去除原始波形的呼吸信号干扰时，具体的，作为一种实施方式，在原始波形上，去除子单元232分别对上升沿波形曲线和下降沿波形曲线进行处理。
参照图4，取上升沿波形曲线为Ss，下降沿波形曲线为Sx，上升沿呼吸曲线为ys，下降沿呼吸曲线为wx。由于原始波形与呼吸曲线位置对应，上升沿波形曲线Ss对应上升沿呼吸曲线ys，下降沿波形曲线Sx对应下降沿呼吸曲线wx。
则上升沿波形曲线减去上升沿呼吸曲线，得到曲线Hs，曲线Hs＝Ss-ys。曲线Hs上点的振幅，为相同位置的上升沿波形曲线点与上升沿呼吸曲线点的振幅之差。
然后，对曲线Hs进行高低通滤波，根据人体正常心跳周期，设置心跳波形的频率范围为0.8—1.6Hz，则通过高低通滤波得到的波形即为0.8—1.6Hz范围内的上升沿心跳曲线。
同理，下降沿波形曲线减去下降沿呼吸曲线，得到曲线Hx，曲线Hx＝Sx-wx。下降沿心跳曲线上点的振幅，为相同位置的下降沿波形曲线点与下降沿呼吸曲线点的振幅之差。
然后，对曲线Hx进行高低通滤波，根据人体正常心跳周期，设置心跳波形的频率范围为0.8—1.6Hz，则通过高低通滤波得到的波形即为0.8—1.6Hz范围内的上升沿心跳曲线。
由此，去除子单元232得到上升沿心跳曲线和下降沿心跳曲线，也即得到心跳波形。
本实施例中，呼吸曲线包括上升沿呼吸曲线和下降沿呼吸曲线，波形定位子单元231得到原始波形的上升沿波形曲线和下降沿波形曲线；然后，去除子 单元232在上升沿波形曲线上去除上升沿呼吸曲线，并进行滤波，得到上升沿心跳曲线；在下降沿波形曲线上去除下降沿呼吸曲线，并进行滤波，得到下降沿心跳曲线，由此，得到心跳波形。本实施例通过波形趋势，定位上升沿波形和下降沿波形，根据上升沿波形和下降沿波形分别拟合得到上升沿呼吸曲线和下降沿呼吸曲线，然后对原始波形的上升沿波形和下降沿波形的分别进行去干扰处理，避免呼吸信号的波峰与心跳信号的波峰一致而导致的去干扰产生的误差，实现了心跳信号的准确获取。
进一步的，参照图10，本申请心跳信号处理装置第四实施例提供一种心跳信号处理装置010，基于上述图9所示的实施例，所述计算模块300包括：
计算子单元310，用于分别根据所述上升沿心跳曲线和所述下降沿心跳曲线，计算得到对应的上升沿心率和下降沿心率。
在获取上升沿心跳曲线和下降沿心跳曲线后，计算子单元310分别计算心率值。
具体的，作为一种实施方式，首先，计算子单元310获取上升沿心跳曲线和下降沿心跳曲线的波峰、波谷位置信息。
然后，计算子单元310根据上升沿心跳曲线相邻两个波峰或波谷采样点位置的距离，得到两个波峰或波谷的采样间隔时间，根据两个波峰或波谷的间隔时间，计算得到采样时间节点的心率值。由此，得到上升沿心跳曲线对应的上升沿心率值HR1。
同理，计算子单元310根据下降沿心跳曲线相邻两个波峰或波谷采样点位置的距离，得到两个波峰或波谷的采样间隔时间，根据两个波峰或波谷的间隔时间，计算得到采样时间节点的心率值。由此，得到下降沿心跳曲线对应的下降沿心率值HR2。
异常处理子单元320，用于根据预设的处理规则，去除所述上升沿心率和下降沿心率中的异常数据。
在得到上升沿心率值HR1和下降沿心率值HR2后，异常处理子单元320根据预设的规则对得到心率值进行处理，去除异常数据。
具体的，作为一种实施方式，预设的处理规则包括取舍、范围限制、平滑处理。
首先，异常处理子单元320舍去开始进行心跳信号采样时、一定时间范围内的初始波形，舍去结束心跳采样时、一定时间范围内的结束波形。初始波形和结束波形由于受到采样仪器的初始化等外部原因，会引起波形异常。
然后，根据预先设置的心率范围，去除过大或过小的心率值。根据人体正常心率值，设置心率范围为每分钟40-120次，根据此范围去掉由起床等原因引起的不合理的过快心跳或慢心跳。
另外，若得到的某个采样时间节点的心率值，与此时间节点的前一个时间节点得到的心率值相比，小于前一个心率值的三分之一或大于前一个心率值的三分之四，则判定得到的心率值浮动过大，去除此心率值。
另外，若一个上升沿心跳曲线或下降沿心跳曲线对应的心跳数量小于2个，则舍弃此上升沿心跳曲线或下降沿心跳曲线对应的心率值。
需要说明的是，预设的处理规则还可以包括其他异常数据处理方式，可根据实际需要灵活设置。
由此，异常处理子单元320得到去除异常数据后的上升沿心率数据和下降沿心率数据。
综合处理子单元330，用于根据所述处理后的上升沿心率和下降沿心率，得到心率值。
在得到去除异常数据后的上升沿心率数据和下降沿心率数据后，综合处理子单元330综合处理上升沿心率和下降沿心率，得到原始波形对应的心率值。
具体的，作为一种实施方式，综合处理子单元330对得到的心率值平均值，得到的心率值即为原始波形采样时间内的心率值。
或者，作为另一种实施方式，综合处理子单元330将得到的上升沿心率和下降沿心率按对应的采样时间节点排序，并按时间顺序划分为数个采样区。然后，分别求各采样区的心率平均值，再综合处理得到原始波形采样时间内的心率值。
本申请实施例采用段处理，采集心跳信号时，将得到的预设长度的原始波形作为一段原始波形进行处理，计算得到心率此段原始波形对应的心率值。其中，预设的长度可根据实际需要灵活设置。
例如，每采集70个点后，将这70个点的波形作为一段原始波形进行处理，得到对应的心跳波形。参照图6所示的心跳波形图，计算模块300得到多段心 跳波形。
由于开始心跳信号采样和结束心跳信号采样时，心跳信号误差较大，因此计算模块300得到多段心跳波形后，去除开始采样和结束采样时预设时间范围内的心跳波形。
由此，计算模块300计算得到多个心率值。
然后，计算模块300对得到的多个心率值进行综合处理，去除异常数据，计算多段心跳波形的平均心率值，得到最终的心率值。
在本实施例中，计算子单元310分别根据上升沿心跳曲线和下降沿心跳曲线，计算得到对应的上升沿心率和下降沿心率；然后，异常处理子单元320根据预设的处理规则，去除上升沿心率和下降沿心率中的异常数据；然后，综合处理子单元330根据处理后的上升沿心率和下降沿心率，得到原始波形对应的心率值。本实施例根据上升沿心跳曲线和下降沿心跳曲线分别计算得到心率之后，在对得到的心率值进行综合处理得到最终的心率值，实现了对心跳信号的处理，得到心率值。
参照图11，本申请实施例心跳信号处理系统提供一种心跳信号处理系统011，所述心跳信号处理系统011包括压电传感器A、模数转换器B和心率监测CPU C，其中：
所述压电传感器A用于，采集压电模拟信号。
本实施例通过压电传感器A获取用户呼吸和心跳的身体振动信号，例如，将压电传感器置A于床垫中或床单下，获取人体睡觉时，呼吸引起的胸腔收括振动信号和心跳引起的有规律的身体震动信号。
例如，可以将压电传感器A设置为1.2米长、1-2毫米厚、5厘米宽，放置于人体平躺时靠近胸腔的位置，持续采集压电信号。
压电传感器A采集到的信号为模拟信号。
然后，压电传感器A将得到的模拟信号发送给模数转换器B。
所述模数转换器B用于，将所述压电模拟信号转换为数字信号，得到心跳信号原始波形。
在本实施例中，模数转换器B可以采用A/D转换器(Analog to Digital  Converter，模数转换器)。
A/D转换器收到压电传感器A发送的模拟信号后，将模拟线号转换为数字信号，持续得到多段心跳信号原始波形。
然后，A/D转换器将得到的多段心跳信号原始波形，按照时间顺序，依次发送给心率监测CPU C。
所述心率监测CPU C，包括获取模块100、波形处理模块200和计算模块300。
心率监测CPU C分别对得到的多段心跳信号原始波形进行处理。
具体的，作为一种事实方式，首先，获取模块100接收模数转换器B发送的心跳信号原始波形，并通过高低通滤波提取出频率范围为0.1-0.5Hz的呼吸波形。
然后，获取模块100进行差分阈值，得到呼吸波形的波峰和波谷位置。然后，根据呼吸波形的波峰和波谷位置查找原始波形对应位置区间范围内的波峰和波谷。其中，预设的区间范围可根据实际需要灵活设置。
由此，获取模块100得到原始波形的波峰和波谷位置信息。
然后，波形处理模块200根据原始波形的波峰和波谷位置信息，判断波形趋势。然后，根据波峰和波谷的位置，将相邻的波峰和波谷作为起始点或结束点，并根据波形趋势，分别拟合得到上升沿呼吸曲线和下降沿呼吸曲线。
然后，波形处理模块200得到原始波形的上升沿波形曲线和下降沿波形曲线。原始波形的上升沿波形曲线与上升沿呼吸曲线位置对应，下降沿波形曲线与下降沿呼吸曲线位置对应。
然后，波形处理模块200将上升沿波形曲线减去对应位置的上升沿呼吸曲线，并对得到的波形进行高低通滤波，得到上升沿心跳曲线；将下降沿波形曲线减去对应位置的下降沿呼吸曲线，并对得到的波形进行高低通滤波，得到下降沿心跳曲线。
然后，计算模块300根据得到的上升沿心跳曲线和下降沿呼吸曲线，分别计算得到对应的上升沿心率和下降沿心率。
然后，计算模块300对得到的上升沿心率和下降沿心率分别去除异常数据，得到人体心率合理范围内的上升沿心率数据和下降沿心率数据。
然后，计算模块300对上升沿心率数据和下降沿心率数据取平均值，得到当前原始波形的心率值。
由于本实施例包括多段心跳信号原始波形，在分别对每段原始波形进行处理，得到对应的心率值后，计算模块300舍去异常数据，例如心跳数小于2个的心跳波形对应的心率值，进行平滑处理，得到最终当前检测时间范围内的心率值。
本实施例中，心跳信号处理系统011包括压电传感器A、模数转换器B和心率监测CPU C，其中压电传感器A采集压电模拟信号并发送给模数转换器B；模数转换器B将压电模拟信号转换为数字信号，得到心跳信号原始波形，并发送给心率监测CPU C；心率监测CPU C接收模数转换器B发送的心跳信号原始波形并进行处理，去除呼吸信号的干扰，计算得到心率值。本实施例通过压电传感器采集信号，心率监测CPU去除心跳信号的严重干扰，根据得到的心跳波形计算并优化处理，得到了最终的心率值，准确性高。
图12是本申请实施例提供的电子设备012的硬件结构示意图，如图12所示，该电子设备012包括：
一个或多个处理器1210以及存储器1220，图12中以一个处理器1210为例。
处理器1210和存储器1220可以通过总线或者其他方式连接，图12中以通过总线连接为例。
存储器1220作为一种非易失性计算机可读存储介质，可用于存储非易失性软件程序、非易失性计算机可执行程序以及模块，如本申请实施例中的心跳信号处理方法对应的程序指令/模块。处理器1210通过运行存储在存储器1220中的非易失性软件程序、指令以及模块，从而执行服务器的各种功能应用以及数据处理，即实现上述方法实施例心跳信号处理方法。
存储器1220可以包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需要的应用程序；存储数据区可存储根据心跳信号处理装置的使用所创建的数据等。此外，存储器1220可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实施例中，存储器1220可选包括相对于处理器1210远程设置的存储器，这些远程存储器可以通过网络连接至心跳信号处理装置。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
所述一个或者多个模块存储在所述存储器1220中，当被所述一个或者多个处理器1210执行时，执行上述任意方法实施例中的心跳信号处理方法，例如，执行以上描述的图1中的方法步骤S10至步骤S30，图2中的方法步骤S10至步骤S30，图3中的方法步骤S10至步骤S30，图5中的方法步骤S10至步骤S33，实现图7中的模块100-300、图8中的模块100-300，图9中的模块100-300，图10中的模块100-330的功能。
上述产品可执行本申请实施例所提供的方法，具备执行方法相应的功能模块和有益效果。未在本实施例中详尽描述的技术细节，可参见本申请实施例所提供的方法。
本申请实施例的电子设备以多种形式存在，包括但不限于:
(1)服务器:提供计算服务的设备，服务器的构成包括处理器、硬盘、内存、系统总线等，服务器和通用的计算机架构类似，但是由于需要提供高可靠的服务，因此在处理能力、稳定性、可靠性、安全性、可扩展性、可管理性等方面要求较高。
(2)其他具有数据交互功能的电子装置。
本申请实施例提供了一种非易失性计算机可读存储介质，所述计算机可读存储介质存储有计算机可执行指令，该计算机可执行指令被一个或多个处理器执行，例如图12中的一个处理器1210，可使得上述一个或多个处理器可执行上述任意方法实施例中的心跳信号处理方法，例如，执行以上描述的图1中的方法步骤S10至步骤S30，图2中的方法步骤S10至步骤S30，图3中的方法步骤S10至步骤S30，图5中的方法步骤S10至步骤S33，实现图7中的模块100-300、图8中的模块100-300，图9中的模块100-300，图10中的模块100-330的功能。
以上所描述的装置实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
通过以上的实施方式的描述，本领域普通技术人员可以清楚地了解到各实 施方式可借助软件加通用硬件平台的方式来实现，当然也可以通过硬件。本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程是可以通过计算机程序来指令相关的硬件来完成，所述的程序可存储于一计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，所述的存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory,ROM)或随机存储记忆体(Random Access Memory,RAM)等。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (10)

  1. 一种心跳信号处理方法，所述方法应用于电子设备，其特征在于，所述心跳信号处理方法包括以下步骤：
    获取心跳信号原始波形，得到所述原始波形的波峰和波谷位置信息；
    根据所述原始波形的波峰和波谷位置信息拟合得到呼吸曲线，并去除所述呼吸曲线对所述原始波形的干扰，得到心跳波形；
    根据所述心跳波形，计算得到心率值。
  2. 如权利要求1所述的心跳信号处理方法，其特征在于，所述根据所述原始波形的波峰和波谷位置信息拟合得到呼吸曲线，并去除所述呼吸曲线对所述原始波形的干扰，得到心跳波形的步骤包括：
    根据所述原始波形的波峰和波谷位置信息，得到波形趋势；
    根据所述原始波形的波峰和波谷位置信息、所述波形趋势，拟合呼吸波形得到呼吸曲线；
    根据所述呼吸曲线，去除所述呼吸曲线对所述原始波形的干扰，并进行滤波，得到心跳波形。
  3. 如权利要求2所述的心跳信号处理方法，其特征在于，所述呼吸曲线包括上升沿呼吸曲线和下降沿呼吸曲线，所述心跳波形包括上升沿心跳曲线和下降沿心跳曲线，所述根据所述呼吸曲线，去除所述呼吸曲线对所述原始波形的干扰，并进行滤波，得到心跳波形的步骤包括：
    根据所述原始波形的波峰和波谷位置信息、所述波形趋势，得到原始波形的上升沿波形曲线和下降沿波形曲线；
    在所述上升沿波形曲线上去除所述上升沿呼吸曲线，并进行滤波，得到上升沿心跳曲线；
    在所述下降沿波形曲线上去除所述下降沿呼吸曲线，并进行滤波，得到下降沿心跳曲线。
  4. 如权利要求3所述的心跳信号处理方法，其特征在于，所述根据所述心跳波形，计算得到心率值的步骤包括：
    分别根据所述上升沿心跳曲线和所述下降沿心跳曲线，计算得到对应的上升沿心率和下降沿心率；
    根据预设的处理规则，去除所述上升沿心率和下降沿心率中的异常数据；
    根据所述处理后的上升沿心率和下降沿心率，得到心率值。
  5. 一种心跳信号处理装置，其特征在于，所述心跳信号处理装置包括：
    获取模块，用于获取心跳信号原始波形，得到所述原始波形的波峰和波谷位置信息；
    波形处理模块，用于根据所述原始波形的波峰和波谷位置信息拟合得到呼吸曲线，并去除所述呼吸曲线对所述原始波形的干扰，得到心跳波形；
    计算模块，用于根据所述心跳波形，计算得到心率值。
  6. 如权利要求5所述的心跳信号处理装置，其特征在于，所述波形处理模块包括：
    波形趋势单元，用于根据所述原始波形的波峰和波谷位置信息，得到波形趋势；
    拟合单元，用于根据所述原始波形的波峰和波谷位置信息、所述波形趋势，拟合呼吸波形得到呼吸曲线；
    去干扰单元，用于根据所述呼吸曲线，去除所述呼吸曲线对所述原始波形的干扰，并进行滤波，得到心跳波形。
  7. 如权利要求6所述的心跳信号处理装置，其特征在于，所述呼吸曲线包括上升沿呼吸曲线和下降沿呼吸曲线，所述心跳波形包括上升沿心跳曲线和下降沿心跳曲线，所述去干扰单元包括：
    波形定位子单元，用于根据所述原始波形的波峰和波谷位置信息、所述波形趋势，得到原始波形的上升沿波形曲线和下降沿波形曲线；
    去除子单元，用于在所述上升沿波形曲线上去除所述上升沿呼吸曲线，并进行滤波，得到上升沿心跳曲线；在所述下降沿波形曲线上去除所述下降沿呼吸曲线，并进行滤波，得到下降沿心跳曲线。
  8. 如权利要求7所述的心跳信号处理装置，其特征在于，所述计算模块包 括：
    计算子单元，用于分别根据所述上升沿心跳曲线和所述下降沿心跳曲线，计算得到对应的上升沿心率和下降沿心率；
    异常处理子单元，用于根据预设的处理规则，去除所述上升沿心率和下降沿心率中的异常数据；
    综合处理子单元，用于根据所述处理后的上升沿心率和下降沿心率，得到心率值。
  9. 一种心跳信号处理系统，其特征在于，所述心跳信号处理系统包括压电传感器、模数转换器和心率监测CPU，其中：
    所述压电传感器用于，采集压电模拟信号；
    所述模数转换器用于，将所述压电模拟信号转换为数字信号，得到心跳信号原始波形；
    所述心率监测CPU，包括如权利要求5-8任一项所述的心跳信号处理装置。
  10. 一种电子设备，其特征在于，包括：
    至少一个处理器；以及，
    与所述至少一个处理器通信连接的存储器；其中，
    所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行权利要求1-4任一项所述的方法。
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