WO2014094415A1 - 基于生物电阻抗的睡眠呼吸模式识别方法及装置 - Google Patents

基于生物电阻抗的睡眠呼吸模式识别方法及装置 Download PDF

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WO2014094415A1
WO2014094415A1 PCT/CN2013/078374 CN2013078374W WO2014094415A1 WO 2014094415 A1 WO2014094415 A1 WO 2014094415A1 CN 2013078374 W CN2013078374 W CN 2013078374W WO 2014094415 A1 WO2014094415 A1 WO 2014094415A1
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signal
electrical impedance
chest
impedance signal
respiratory
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French (fr)
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蒋庆
汪洪彬
宋嵘
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Sun Yat Sen University
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Sun Yat Sen University
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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
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A61B5/085—Measuring impedance of respiratory organs or lung elasticity
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/113—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb occurring during breathing
    • 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
    • A61B5/4812—Detecting sleep stages or cycles

Definitions

  • the present invention relates to the field of medical monitoring, and in particular to a method and apparatus for recognizing a sleep breathing pattern based on bioelectrical impedance.
  • the polysomnography is often used to monitor the patient's sleep.
  • the device is cumbersome and complicated to operate. It requires the patient to check at the sleep monitoring center, which is inconvenient to carry around.
  • the method of simply monitoring the patient's sleep apnea is mostly detected by the nose and mouth airflow sensor. This method can effectively monitor the number of pauses in sleep breathing, but it cannot effectively classify and identify sleep apnea as a blocked pause, a central pause or a hybrid. Type pause.
  • Bioelectrical impedance technology utilizes the electrical properties of biological tissues and organs to extract non-invasive monitoring techniques for human physiological and pathological information.
  • Human tissues and organs have unique electrical properties, and changes in the state or function of tissues and organs will be accompanied by changes in electrical properties.
  • the degree of synchronization of the degree of fatigue of the diaphragm is correlated with the degree of synchronization of the chest respiratory electrical impedance signal and the peak of the abdominal respiratory electrical impedance signal, and the synchronization degree of the peak according to the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal is established.
  • the difference divides the degree of diaphragmatic fatigue into different types.
  • bioelectrical impedance technology show its advantages of non-invasive, long-term monitoring and low cost in clinical medicine, which makes bioelectrical impedance technology have great potential and value in clinical medicine. Summary of the invention
  • the technical problem solved by the present invention is to overcome the deficiencies of the prior art and provide a method for accurately identifying a patient's sleep breathing pattern in real time based on bioelectrical impedance technology.
  • the present invention also provides an identification device that accurately recognizes a patient's sleep breathing pattern in real time based on bioelectrical impedance.
  • a bioelectrical impedance-based sleep breathing pattern recognition method includes the following steps:
  • step (a) the specific steps of the step (a) are:
  • the signal detecting electrode synchronously collects the voltage amplitude data of the human chest and the abdomen, and after calculation, obtains the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal.
  • the specific steps of the step (b) are: sequentially performing filtering and analog-to-digital conversion processing on the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal to obtain a digital signal class.
  • Type chest respiratory electrical impedance signal and abdominal respiratory electrical impedance signal are: sequentially performing filtering and analog-to-digital conversion processing on the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal to obtain a digital signal class.
  • step (C) the specific steps of the step (C) are:
  • (c2) Analyze the spectral variation relationship between the chest respiratory impedance signal and the abdominal respiratory impedance signal in each phase relationship segmentation signal.
  • the chest breathing electrical impedance signal and the abdominal respiratory electrical impedance signal are classified and identified according to the relationship of amplitude variation, phase relationship and spectral variation to determine the sleep breathing pattern of the human body.
  • step (cl) are:
  • the starting point and the ending point of the segmented signal are the segmentation points of the first and last phase relationship of the segment signal respectively, and the chest respiratory impedance signal between adjacent phase relationship segment points is extracted.
  • the abdominal respiratory electrical impedance signal is a phase relationship segmentation signal;
  • step (c2) are:
  • a sleep resistance pattern recognition device based on bioelectrical impedance comprising an electrode and a monitor, wherein
  • the electrode includes an excitation electrode and a detection electrode.
  • the excitation electrode is worn on the chest and abdomen of the human body to provide an excitation current to the human body;
  • the detection electrode is worn on the chest and abdomen of the human body for receiving a voltage signal of the chest and abdomen of the human body;
  • the hardware portion of the monitor includes:
  • An excitation current module for providing a stable current excitation to the excitation electrode
  • a multi-channel switch module connected to the electrode, the excitation current module and the impedance calculation module, for supplying current excitation to the excitation electrode of different parts, receiving the voltage signal of the detection electrode and receiving the voltage signal of the detection electrode to the impedance calculation module;
  • the impedance calculation module is connected to the multi-channel switch module, and calculates a chest respiratory electrical impedance signal and an abdominal respiratory electrical impedance signal according to the voltage signal provided by the detecting electrode;
  • the main control module is connected to the impedance calculation module, and is used for performing analog-to-digital conversion on the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal, and analyzing and processing the signal, and then classifying and identifying the breathing mode of the human body;
  • An alarm module connected to the main control module, for alerting the timeout apnea; the power module provides analog voltage and digital voltage for each of the above modules;
  • the monitor generates excitation current to the multi-channel switch module by exciting the current module, and the multi-channel switch module selects to conduct different excitation paths to transmit the excitation current to the excitation electrode, the excitation electrode injects the excitation current into the human body, and the detection electrode collects the chest and the abdomen
  • the multi-channel switch module sends the received voltage signal to the impedance calculation module, and the impedance calculation module calculates the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal according to the voltage signal, and finally the chest
  • the respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal are sent to the main control module to be converted into a digital signal, and then the signal is analyzed and processed to determine the breathing pattern of the human body.
  • the invention can uniformly measure the measurement data of different breathing modes, effectively eliminate the influence of physiological activity interference, power frequency interference, etc., has good anti-interference effect, can clearly and accurately display the measurement data, and the operation is safer and more convenient, and can quickly and accurately identify Breathing modes at different times in sleep breathing can provide supplementary reference information for medical staff to detect, diagnose and treat patients' diseases.
  • FIG. 1 is a flow chart of a specific embodiment of a bioelectrical impedance-based sleep breathing pattern recognition method according to the present invention
  • FIG. 2 is a test view of an electrode on a human body in the present invention
  • FIG. 3 is a chest respiratory electrical impedance signal and abdominal respiratory resistance signal after filtering treatment in the present invention Schematic diagram of the number
  • FIG. 4 is a schematic diagram of processing a chest respiration electrical impedance signal and a abdominal respiratory electrical impedance signal using a sliding window function in the present invention
  • FIG. 5 is a phase diagram showing the relationship between the chest respiration electrical impedance signal and the abdominal respiratory electrical impedance signal in the segmentation signal of the amplitude change using the sliding window function in the present invention
  • FIG. 6 is a schematic structural view of a bioelectrical impedance-based sleep breathing pattern recognition device according to an embodiment of the present invention.
  • the present invention combines the respiratory and electrical impedance signals of the chest and the abdomen, and combines the chest respiratory electrical impedance signals obtained in the experiment. And the parametric model relationship of abdominal respiratory electrical impedance signals, using segmentation for phase, amplitude and spectrum comparison, to identify different respiratory signals into different breathing patterns.
  • FIG. 1 is a flowchart of a specific embodiment of a bioelectrical impedance-based sleep breathing pattern recognition method according to the present invention
  • the specific steps of the bioelectrical impedance-based sleep breathing pattern recognition method in the specific embodiment include:
  • Step S101 collecting a chest respiratory electrical impedance signal and a abdominal respiratory electrical impedance signal of the human body;
  • Step S102 performing data processing on the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal into digital signal data respectively;
  • Step S103 segment, identify, and determine the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal according to the relationship between the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal Breathing mode.
  • step S101 uses a quadrupole method to measure the body electrical impedance values of the patient's chest and abdomen. Specifically, as shown in Fig. 2, the two pairs of excitation electrodes Ip, In and the two pairs of detection electrodes Vp, Vn are respectively fixed at corresponding positions on the chest and the abdomen.
  • the voltage signal of the human chest and abdomen is collected, and the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal are obtained after calculation; as an alternative, the excitation electrode and the detection electrode of the chest and abdomen positions can exchange the pasting position.
  • the present invention adopts multi-channel switching technology to realize real-time acquisition of bioelectrical impedance signals of the chest and the abdomen, and simplifies the hardware when the time difference between the collection of the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal is much smaller than the respiratory signal period. Circuit design.
  • step S102 since the electrodes are attached to the chest and the abdomen of the human body, they may be interfered by other physiological signals, and may be accompanied by an impedance overload phenomenon caused by occasional electrode contact problems. Therefore, for the collected electrical impedance signals, it is necessary to perform Data processing, specifically, the chest respiration electrical impedance signal and the abdominal respiratory electrical impedance signal are separately filtered, and then converted into a digital signal type chest respiratory electrical impedance signal and abdominal respiratory electrical impedance signal by analog-to-digital conversion processing.
  • the filtering can be implemented by a bandpass filter.
  • the bandpass filter can be a Chebyshev bandpass filter with a order of 4 steps and a passband frequency of 0.1 Hz-4 Hz.
  • Figure 3 is filtered Post-thoracic respiratory electrical impedance signal and abdominal respiratory electrical impedance signal data.
  • step S103 the specific embodiment segments the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal by the following steps:
  • Step S1031 setting a maximum value point change threshold A 1 ;
  • Step S1032 processing the chest respiration electrical impedance signal that has been digital signal data by using a sliding window function, and extracting an average value Y of the maximum value of the respiratory signal in the sliding window function, and the average value of the adjacent sliding window function The absolute value of the difference ⁇ 1+1 - ⁇ " is compared with the maximum value point change threshold.
  • a sliding window function that adds a certain window width to the chest respiratory electrical impedance signal of the digital signal type is extracted, and a maximum value of the chest respiratory electrical impedance signal in the window function is extracted, and then the window function is calculated.
  • the window function is slid on the chest respiratory impedance signal according to the given step size, and the maximum value point P M in the window function is calculated in turn, and then the window function is calculated.
  • the starting point of the +1 window function is a amplitude change segmentation point of the chest respiratory electrical impedance signal, and the abdominal respiratory electrical impedance signal at the corresponding position is segmented, and the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal are segmented.
  • the width of the sliding window function can be set according to different sampling rates f. Generally, the width of the window function is 30*f, that is, 30 seconds.
  • the breath data is the width of the sliding window function.
  • the step size of the sliding window function can be set to the width of the window function.
  • the width and step size of the sliding window function can be adjusted as needed.
  • the maximum value point change threshold ⁇ is 0.1, and it can be adjusted to other values according to the degree of change of the chest respiratory impedance signal.
  • the specific embodiment may add a step of calculating the average value of the change in the amplitude of the respiratory impedance during the monitoring process, and the step acquires each individual The average value of the change in the amplitude of the respiratory impedance at rest.
  • the average value is used as a reference standard for judging the degree of changes in chest respiratory impedance and abdominal respiratory impedance, which can effectively ensure the effectiveness of the entire algorithm and eliminate the interference of abnormal changes.
  • Step S1033 adding a sliding window function to the respiratory electrical impedance signal in the segmental signal corresponding to the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal, respectively extracting the chest respiratory electrical impedance signal and the abdominal in the sliding window function A series of moments t ai and t bl appearing at the signal peak of the respiratory impedance signal ; calculating the chest respiratory electrical impedance signal and abdominal respiratory resistance signal in the sliding window function
  • Step S1034 Set the phase threshold A 2 , compare the phase relationship G with the phase threshold A 2 , and if the G value is greater than the phase threshold A 2 , the data starting point of the sliding window function is a phase relationship segment point to each segment
  • the start and end points of the amplitude change segmentation signal are the first and last phase relationship segmentation points of the segment signal respectively, and extract the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal between adjacent phase relationship segmentation points. Segmenting the signal for the phase relationship;
  • the respiratory impedance signal corresponding to the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal is simultaneously added with a sliding window function having a window width of W 2 and a step size of L 2 . Then extract the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal in the window function respectively.
  • phase relationship A 2 is set for the phase relationship between the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal, when indicating chest respiratory electrical impedance signal and abdominal breathing
  • the starting point of the data in the window function that generates the G value is a phase relationship of the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal, and the starting point and the ending point of the segmented signal are respectively changed for each segment.
  • the first and last phase relationship segmentation points of the signal extract the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal between the adjacent phase relationship segmentation points as a phase relationship segmentation signal, and the chest respiratory electrical impedance signal and
  • the phase relationship of the abdominal respiratory electrical impedance signal is one-to-one correspondence.
  • the width of the sliding window function ⁇ 2 can be set to different values according to different sampling rates f. Generally, the width of the window function is 30*f, that is, the breathing data of 30 seconds is used as the width of the sliding window function.
  • the step size 1 ⁇ 2 of the sliding window function is set to the width W 2 of the window function. The width W 2 and the step length L 2 of the sliding window function can be adjusted as needed.
  • the change threshold A 2 of the phase relationship between the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal is set to 1 according to the respiratory frequency, and may be adjusted to other values according to the change of the respiratory frequency.
  • Step S1035 respectively extract the chest respiratory electrical impedance signal and the maximum value point P e ⁇ PP ai of the abdominal respiratory electrical impedance signal in each phase relationship segmentation signal ; calculate the chest call nyp ci in each phase relationship segmentation signal.
  • the average value of the maximum point of the electrical impedance signal and the abdominal respiratory impedance signal F e ⁇ ⁇ and n
  • Step S1037 calculating the spectrum of the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal in each phase relationship segmentation signal; then calculating the chest respiratory electrical impedance signal and the l - in each phase relationship segmentation signal
  • the effective spectral components range from 0.2 to 0.45 Hz, and other spectral components are effectively filtered out.
  • the spectrum of the chest respiratory electrical impedance signal is significantly different from the spectrum of the abdominal respiratory electrical impedance signal, and the same frequency component occupies a different proportion in the chest respiratory electrical impedance signal spectrum and the abdominal respiratory electrical impedance signal spectrum.
  • Step S1039 Establish sleep breathing mode M and chest respiratory electrical impedance signal and abdominal respiratory power
  • the M value in each phase relationship segmentation signal is calculated, and the breathing mode of the respiratory signal in each phase relationship segmentation signal is determined according to the range of the M value.
  • the calculation of the G value can directly utilize the G value obtained in step S1033, and the difference of the M value corresponds to different breathing modes, and the M value range corresponding to the breathing mode can be obtained according to experimental experience; specifically, when the M value is 10 ⁇ At 20 o'clock, the sleep breathing pattern identifying the segment is chest breathing; when the M value is 2-6, the sleep breathing pattern identifying the segment is cis abdominal breathing; when the M value is greater than 20, the score is recognized.
  • the sleep breathing mode of the segment is reverse abdominal breathing; when the M value is 7-10, the sleep breathing pattern identifying the segment is central apnea; when the M value is less than 2, the sleep breathing pattern of the segment is identified. For obstructive apnea.
  • the present invention also provides a bioelectrical impedance-based sleep breathing pattern recognition apparatus, and the following is a bioelectrical impedance-based sleep breathing pattern recognition method apparatus of the present invention.
  • a bioelectrical impedance-based sleep breathing pattern recognition apparatus of the present invention. The specific examples are described in detail.
  • the bioelectrical impedance-based sleep breathing pattern recognition apparatus of the present embodiment includes: the electrode and the monitor electrode include an excitation electrode and a detection electrode.
  • the excitation electrode is worn on the chest and abdomen of the human body to provide an excitation current to the human body;
  • the detection electrode is worn on the chest and abdomen of the human body for receiving a voltage signal of the chest and abdomen of the human body;
  • the hardware part of the monitor includes:
  • An excitation current module for providing a stable current excitation to the excitation electrode
  • a multi-channel switch module connected to the electrode, the excitation current module and the impedance calculation module, for supplying current excitation to the excitation electrode of different parts, receiving the voltage signal of the detection electrode and receiving the voltage signal of the detection electrode to the impedance calculation module;
  • the impedance calculation module is connected to the multi-channel switch module, and calculates a chest respiratory electrical impedance signal and an abdominal respiratory electrical impedance signal according to the voltage signal provided by the detecting electrode;
  • the main control module is connected to the impedance calculation module for performing analog-to-digital conversion on the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal, and analyzing and processing the signal, and then classifying and identifying the breathing mode of the human body;
  • An alarm module connected to the main control module, for alerting the timeout apnea; the power module provides analog voltage and digital voltage for each of the above modules,
  • the monitor generates excitation current to the multi-channel switch module by exciting the current module, and the multi-channel switch module selects to conduct different excitation paths to transmit the excitation current to the excitation electrode, the excitation electrode injects the excitation current into the human body, and the detection electrode collects the chest and the abdomen
  • the multi-channel switch module sends the received voltage signal to the impedance calculation module, and the impedance calculation module calculates the chest respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal according to the voltage signal, and finally the chest
  • the respiratory electrical impedance signal and the abdominal respiratory electrical impedance signal are sent to the main control module to be converted into a digital signal, and then the signal is analyzed and processed to determine the breathing pattern of the human body.
  • the test electrode is attached to the human body before the test, and the electrical impedance of the human body can be measured by the quadrupole method.
  • the two pairs of excitation electrodes Ip and In and the two pairs of detection electrodes Vp and Vn are respectively fixed on the chest and The corresponding position of the abdomen.
  • the chest excitation electrode positive electrode Ip is fixed to the left side of the right chest nipple, and the excitation electrode negative electrode In is fixed at the projection position of the excitation electrode Ip at the back;
  • the chest detecting electrode Vp is fixed to the right side of the right chest nipple, and the detecting electrode Vn is fixed at the detecting electrode Vp at the back Projection position;
  • the abdomen excitation electrode positive electrode Ip is fixed on the left side of the navel, the excitation electrode negative electrode In is fixed at the projection position of the excitation electrode Ip at the back;
  • the abdominal detection electrode Vp is fixed on the right side of the navel, and the detection electrode Vn is fixed on the detection electrode Vp at the back
  • the monitor can be started normally, the chest respiratory impedance signal and the abdominal respiratory impedance signal are obtained, and the signal is analyzed and processed, and then the segmentation identification determines the breathing mode of the human body;
  • the device of the invention can provide a frequency range of 10 ⁇ 100KHz by the excitation current module, and the amplitude range is 0.5-5 mA.
  • the stable excitation current is input into the human body, and the voltage signal of the human body is obtained by the detection electrode, and the impedance signal of the human body is obtained by the impedance calculation module. Then, it is sent to the main control module to perform preliminary filtering and analog digital conversion, and then analyze and process the signal, and then segmentally identify and determine the breathing mode of the human body.
  • the monitor can send the converted digital signal to a computer for signal analysis and segmentation identification.

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Abstract

一种基于生物电阻抗的睡眠呼吸模式识别方法及装置。所述方法包括：采集人体的胸部和腹部呼吸电阻抗信号；对已为数字信号数据的胸部呼吸电阻抗信号根据幅值变化进行分段，分析各分段内对应的胸部和腹部呼吸电阻抗信号之间的幅值变化关系、频谱差异关系和信号相位关系并建立参数模型，根据参数模型对人体的睡眠呼吸信号进行分类识别确定呼吸模式。该方法抗干扰效果好，能实时准确识别出人体睡眠的呼吸模式。

Description

基于生物电阻抗的睡眠呼吸模式识别方法及 ^ 技术领域
本发明涉及医疗监测领域， 尤其涉及一种基于生物电阻抗的睡眠呼吸模 式识别方法及装置。
背景技术
目前临床多采用多导睡眠监测仪 （PSG) 对患者睡眠情况进行监测， 该 设备笨重， 操作复杂， 需要患者在睡眠监护中心进行检查， 不方便随身携带。 对简单监测患者睡眠呼吸暂停的方法多采用口鼻气流传感器进行检测， 该方 法能够有效地监测睡眠呼吸的暂停次数， 但却无法有效地分类识别睡眠呼吸 暂停为阻塞型暂停、 中枢型暂停还是混合型暂停。 而对于睡眠呼吸暂停的患 者来说， 需要有效地监测及记录患者睡眠呼吸中的呼吸模式， 帮助医生诊断 识别睡眠呼吸中呼吸暂停的类型， 为患者制定有效的治疗方案以及评估治疗 方案的有效性。 但是目前的睡眠呼吸监测方法及装置都无法满足上述要求。
生物电阻抗技术利用了生物组织及器官的电特性提取人体生理与病理信 息的无创监测技术。 人体组织与器官具有独特的电特性， 组织与器官的状态 或功能变化将伴随相应的电特性改变。 比如现有技术中有利用膈肌疲劳程度 的变化与胸部呼吸电阻抗信号及腹部呼吸电阻抗信号的波峰的同步程度建立 对应关系， 根据胸部呼吸电阻抗信号及腹部呼吸电阻抗信号的波峰的同步程 度的差异将膈肌疲劳程度分为不同的类型。 这样的应用使得生物电阻抗技术 在临床医学方面体现出了它无创无损、 便于长时间监护及低成本的优势， 使 得生物电阻抗技术应用于临床医学具有很大的潜力与价值。 发明内容
本发明解决的技术问题是克服现有技术的不足， 提供一种基于生物电阻 抗技术实时准确识别患者睡眠呼吸模式的方法。
本发明还提供一种基于生物电阻抗实时准确识别患者睡眠呼吸模式的识 别装置。
为解决上述技术问题， 本发明第一个发明目的的技术方案如下： 一种基于生物电阻抗的睡眠呼吸模式识别方法， 包括如下步骤：
( a) 采集人体的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号；
( b )对胸部呼吸电阻抗信号和腹部呼吸电阻抗信号分别进行数据处理转 换成数字信号数据；
( c )根据胸部呼吸电阻抗信号和腹部呼吸电阻抗信号之间的关系对人体 的睡眠呼吸信号进行分段识别确定人体的呼吸模式。
作为一种优选方案， 所述步骤 （a) 的具体步骤为：
通过固定在人体胸前乳头左侧 （或右侧） 区域及对应的背部投影位置的 一对激励电极和固定在人体肚脐左侧 （或右侧） 区域及对应的背部投影位置 的另一对激励电极同步输入电流激励；
通过固定在人体胸前乳头右侧 （或左侧） 区域及对应的背部投影位置的 一对信号检测电极和固定在人体肚脐右侧 （或左侧） 区域及对应的背部投影 位置的另一对信号检测电极同步采集人体胸部和腹部的电压幅值数据， 经计 算后得到胸部呼吸电阻抗信号和腹部呼吸电阻抗信号。
作为一种优选方案， 所述步骤 （b ) 的具体步骤为： 对胸部呼吸电阻抗信 号和腹部呼吸电阻抗信号分别依次进行滤波和模数转换处理获得数字信号类 型的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号。
作为一种优选方案， 所述步骤 （C ) 的具体步骤为：
(cl ) 根据已为数字信号数据的胸部呼吸电阻抗信号和腹部呼吸电阻抗 信号之间的幅值变化关系及相位关系进行一一对应的幅值变化分段及相位关 系分段。
(c2) 分析获取各相位关系分段信号内的胸部呼吸电阻抗信号和腹部呼 吸电阻抗信号之间的频谱变化关系。 根据幅值变化关系、 相位关系和频谱变 化关系对胸部呼吸电阻抗信号和腹部呼吸电阻抗信号进行分类识别确定人体 的睡眠呼吸模式。
作为一种优选方案， 所述步骤 （cl ) 的具体步骤为：
(cll ) 设定极大值点变化阈值 A1 ;
(cl2)利用滑动窗函数对已为数字信号数据的胸部呼吸电阻抗信号进行 处理， 提取滑动窗函数内的呼吸信号极大值点的平均值 Y,， 将相邻滑动窗函 数内平均值的差的绝对值 ΙΥ1+1-Υ」与极大值点变化阈值 进行比较，如果该绝 对值 IY1+1-Y」大于极大值点变化阈值 则后一个滑动窗函数内的数据起点为 一个幅值变化分段点；
(cl3) 以第一个幅值变化分段点为起点， 提取相邻的幅值变化分段点之 间的胸部呼吸电阻抗信号作为幅值变化分段信号， 依据同样的幅值变化分段 点对腹部呼吸电阻抗信号进行分段获取相对应的幅值变化分段信号；
(cl4)对胸部呼吸电阻抗信号和腹部呼吸电阻抗信号相对应的幅值变化 分段信号内的呼吸电阻抗信号加上滑动窗函数， 分别提取滑动窗函数内的胸 部呼吸电阻抗信号和腹部呼吸电阻抗信号的信号波峰出现的一系列时刻 t„和 tai;
(cl5 )计算获取滑动窗函数内的胸部呼吸电阻抗信号和腹部呼吸电阻抗
Figure imgf000005_0001
信号的相位关系 = ；
(cl6) 设定相位阈值 A2， 将相位关系 G与相位阈值 A2进行比较， 如果 G值大于相位阈值 A2， 则该滑动窗函数的数据起点为一个相位关系分段点；
(cl7) 以每段幅值变化分段信号的起点和终点分别为该段信号第一个和 最后一个相位关系分段点， 提取相邻的相位关系分段点之间的胸部呼吸电阻 抗信号和腹部呼吸电阻抗信号为相位关系分段信号；
作为更进一步的优选方案， 所述步骤 （c2) 的具体步骤为：
(c21 )分别提取各相位关系分段信号内的胸部呼吸电阻抗信号和腹部呼 吸电阻抗信号的极大值点 ^和 Pai ;
(c22)计算各相位关系分段信号内的胸部呼吸电阻抗信号和腹部呼吸电
∑ ci ∑
阻抗信号的极大值点的平均值 Fe= ^^和 Fa=^~；
(c23 )利用 = 计算获取各相位关系分段信号内的胸部呼吸电阻抗 信号和腹部呼吸电阻抗信号之间的幅值变化关系；
(c24)分别计算各相位关系分段信号内的胸部呼吸电阻抗信号和腹部呼 吸电阻抗信号的频谱；
(c25 )计算各相位关系分段信号内的胸部呼吸电阻抗信号和腹部呼吸电
Figure imgf000006_0001
(c26) 利用 1= / 计算获取各相位关系分段信号内的胸部呼吸电阻抗 a 信号和腹部呼吸电阻抗信号之间的频谱变化关系；
(c27) 建立睡眠呼吸模式 M和胸部呼吸电阻抗信号与腹部呼吸电阻抗 信号之间的相位关系 G、 幅值变化关系 F及频谱变化关系 I的参数模型关系 式 M=G*F*I。 计算各相位关系分段信号内的 M值， 根据 M值的范围判断各 相位关系分段信号的呼吸模式。
本发明第二个发明目的的技术方案如下：
一种基于生物电阻抗的睡眠呼吸模式识别装置， 包括电极和监测仪， 其 中，
电极包括激励电极和检测电极。 激励电极， 佩戴在人体胸部和腹部位置， 用于提供激励电流至人体组织； 检测电极， 佩戴在人体胸部和腹部位置， 用 于接收人体胸部和腹部的电压信号；
所述监测仪的硬件部分包括：
激励电流模块， 用于为激励电极提供稳定的电流激励；
多通道开关模块， 与电极、 激励电流模块和阻抗计算模块连接， 用于向 不同部位的激励电极提供电流激励、 接收检测电极的电压信号和接收检测电 极的电压信号传输给阻抗计算模块；
阻抗计算模块， 与多通道开关模块连接， 根据检测电极提供的电压信号 计算出胸部呼吸电阻抗信号和腹部呼吸电阻抗信号； 主控模块， 与阻抗计算模块连接， 用于对胸部呼吸电阻抗信号和腹部呼 吸电阻抗信号进行模数转换并对信号进行分析处理， 然后分类识别确定人体 的呼吸模式；
报警模块， 与主控模块连接， 用于对超时的呼吸暂停进行报警提醒； 电源模块， 为以上各个模块提供模拟电压和数字电压；
监测仪， 通过激励电流模块产生激励电流提供给多通道开关模块， 多通 道开关模块选择导通不同通路后将激励电流传输至激励电极， 激励电极将激 励电流注入人体， 同时检测电极采集胸部和腹部的电压信号后将其发送至多 通道开关模块， 多通道开关模块将接收的电压信号发送至阻抗计算模块， 阻 抗计算模块根据电压信号计算出胸部呼吸电阻抗信号和腹部呼吸电阻抗信 号， 最后将胸部呼吸电阻抗信号和腹部呼吸电阻抗信号送入主控模块转化为 数字信号后进行信号的分析处理并分段识别确定人体的呼吸模式。
与现有技术相比， 本发明技术方案的有益效果是：
本发明能将不同呼吸模式的测量数据进行统一处理， 有效排除生理活动 的干扰、 工频干扰等影响， 抗干扰效果好， 能清晰准确显示测量数据， 其操 作更安全方便， 能够快速准确识别出睡眠呼吸中不同时刻的呼吸模式， 能够 为医护人员对患者疾病的检测、 诊断及治疗提供辅助参考信息。
附图说明
图 1 为本发明中基于生物电阻抗的睡眠呼吸模式识别方法具体实施例的流程 图；
图 2为本发明中电极在人体上的测试图；
图 3为本发明中通过滤波处理后的胸部呼吸电阻抗信号和腹部呼吸电阻抗信 号的示意图；
图 4为本发明中利用滑动窗函数对胸部呼吸电阻抗信号和腹部呼吸电阻抗信 号进行处理的示意图；
图 5为本发明中利用滑动窗函数对幅值变化分段信号内的胸部呼吸电阻抗信 号和腹部呼吸电阻抗信号的相位关系图；
图 6为本发明的基于生物电阻抗的睡眠呼吸模式识别装置具体实施例的结构 示意图；
具体实施方式
下面结合附图和实施例对本发明的技术方案做进一步的说明。
在实验过程中得到， 胸部和腹部的电阻抗幅值变化与呼吸信号变化具有 很强的相关性， 因此， 本发明通过采集胸部及腹部呼吸电阻抗信号， 结合实 验中获得的胸部呼吸电阻抗信号及腹部呼吸电阻抗信号的参数模型关系， 采 用分段进行相位、 幅值及频谱的比较， 将不同呼吸信号分类识别为不同的呼 吸模式。
如图 1所示， 为本发明中基于生物电阻抗的睡眠呼吸模式识别方法具体 实施例的流程图， 本具体实施例的基于生物电阻抗的睡眠呼吸模式识别方法 的具体步骤包括：
步骤 S101 : 采集人体的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号； 步骤 S102: 对胸部呼吸电阻抗信号和腹部呼吸电阻抗信号分别进行数据 处理转换成数字信号数据；
步骤 S103 : 根据胸部呼吸电阻抗信号和腹部呼吸电阻抗信号之间的关系 对胸部呼吸电阻抗信号和腹部呼吸电阻抗信号进行分段、 识别并确定人体的 呼吸模式。
在具体实施过程中，步骤 S101采用四极法测量患者胸部和腹部的人体电 阻抗值。 具体为， 如图 2所示， 两对激励电极 Ip、 In及两对检测电极 Vp、 Vn分别固定在胸部及腹部相应位置。通过固定在人体胸前乳头左侧区域及对 应的背部投影位置的一对激励电极 Ip、 In和固定在人体肚脐左侧区域及对应 的背部投影位置的另一对激励电极 Ip、 In输入电流激励； 通过固定在人体胸 前乳头右侧区域及对应的背部投影位置的一对信号检测电极 Vp、 Vn和固定 在人体肚脐右侧区域及对应的背部投影位置的另一对信号检测电极 Vp、 Vn 采集人体胸部和腹部的电压信号， 经计算后得到胸部呼吸电阻抗信号和腹部 呼吸电阻抗信号； 作为一种备选方案， 胸部和腹部位置的激励电极与检测电 极可以交换粘帖位置。
此外， 本发明采用多通道开关技术实现实时采集胸部及腹部的生物电阻 抗信号， 在保证采集胸部呼吸电阻抗信号及腹部呼吸电阻抗信号之间的时间 差远小于呼吸信号周期的情况下简化了硬件电路的设计。
在步骤 S102中， 由于各电极贴在人体胸部和腹部， 会受到其他生理信号 的干扰， 同时可能伴有偶尔的电极接触问题产生的阻抗过载现象， 因此， 对 于采集到的电阻抗信号， 需要进行数据处理， 具体地， 对胸部呼吸电阻抗信 号和腹部呼吸电阻抗信号分别进行滤波处理， 然后将其通过模数转换处理转 变成数字信号类型的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号。 滤波可以 采用带通滤波器的处理方法实现， 带通滤波器可采用切比雪夫带通滤波器， 阶数为 4阶， 通带频率为 0.1Hz-4Hz。 同时将高于 100欧姆的数据均取其前一 时刻的数据来代替， 目的是减少干扰， 使采用曲线更平滑。 图 3为经过滤波 后的胸部呼吸电阻抗信号及腹部呼吸电阻抗信号数据。
在步骤 S103中，本具体实施例通过如下步骤对胸部呼吸电阻抗信号和腹 部呼吸电阻抗信号进行分段：
步骤 S1031 : 设定极大值点变化阈值 A1 ;
步骤 S1032:利用滑动窗函数对已为数字信号数据的胸部呼吸电阻抗信号 进行处理， 提取滑动窗函数内的呼吸信号极大值点的平均值 Y,， 将相邻滑动 窗函数内平均值的差的绝对值 ΙΥ1+1-Υ」和极大值点变化阈值 进行比较，如果 绝对值 IY1+1-Y」大于 则后一个滑动窗函数的数据起点为一个幅值变化分段 点， 以第一个幅值变化分段点为起点， 提取相邻的幅值变化分段点之间的胸 部呼吸电阻抗信号作为幅值变化分段信号， 依据同样的幅值变化分段点对腹 部呼吸电阻抗信号进行分段获取相对应的幅值变化分段信号；
具体地， 如图 4所示， 对已为数字信号类型的胸部呼吸电阻抗信号加一 定窗宽 的滑动窗函数， 提取窗函数内的胸部呼吸电阻抗信号的极大值点， 然后计算窗函数内的极大值点的平均值。 将窗函数按给定的步长 在胸部呼 吸电阻抗信号上滑动， 依次计算出窗函数内的极大值点 PM， 然后计算该窗函
数内的极大值点的平均值 ¥C1=^^~。对平均值 FM的变化设定极大值点变化 阈值 当相邻平均值 Fa的变化 IFM- 1+1)I超过极大值点变化阈值八1时， 则 产生此平均值 Fa+1的窗函数的起点为胸部呼吸电阻抗信号的一个幅值变化分 段点， 同时对相应位置的腹部呼吸电阻抗信号进行分段， 胸部呼吸电阻抗信 号和腹部呼吸电阻抗信号的分段一一对应。滑动窗函数的宽度 可以根据不 同的采样率 f设定不同值， 一般设定窗函数的宽度点数为 30*f， 即采用 30秒 的呼吸数据作为滑动窗函数的宽度。 同时可以设定滑动窗函数的步长 为窗 函数的宽度 滑动窗函数的宽度 及步长 均可根据需要进行调整。一 般设定极大值点变化阈值 ^为 0.1， 也可以根据胸部呼吸电阻抗信号的变化 程度调整为其他值。
由于各个人在静息状态下呼吸阻抗信号的幅值变化程度具有个体性差 异， 所以本具体实施例在监测过程中可以增加一个计算呼吸阻抗幅值变化平 均值的步骤， 该步骤获取各个人在静息状态下的呼吸阻抗幅值变化的平均值。 该平均值作为判断胸部呼吸阻抗及腹部呼吸阻抗变化程度的参考标准， 能够 有效的保证整个算法的有效性， 排除异常变化的干扰。
步骤 S1033:对胸部呼吸电阻抗信号和腹部呼吸电阻抗信号相对应的幅值 变化分段信号内的呼吸电阻抗信号加上滑动窗函数， 分别提取滑动窗函数内 的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号的信号波峰出现的一系列时刻 tai和 tbl; 计算获取滑动窗函数内的胸部呼吸电阻抗信号和腹部呼吸电阻抗信
Figure imgf000011_0001
号的相位关系 = ~~ ；
步骤 S1034: 设定相位阈值 A2， 将相位关系 G与相位阈值 A2进行比较， 如果 G值大于相位阈值 A2， 则该滑动窗函数的数据起点为一个相位关系分段 点， 以每段幅值变化分段信号的起点和终点分别为该段信号第一个和最后一 个相位关系分段点， 提取相邻的相位关系分段点之间的胸部呼吸电阻抗信号 和腹部呼吸电阻抗信号为相位关系分段信号；
具体地， 如图 5所示， 对胸部呼吸电阻抗信号和腹部呼吸电阻抗信号相 对应分段内的呼吸电阻抗信号同时加上窗宽为 W2， 步长为 L2的滑动窗函数， 然后分别提取窗函数内的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号的信号
波峰出现的一系列时刻
Figure imgf000012_0001
tai和 tbl， 最后采用公式 = 计算窗函数
n
内的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号的相位关系； 接着对胸部呼 吸电阻抗信号和腹部呼吸电阻抗信号的相位关系设定相位阈值 A2， 当表示胸 部呼吸电阻抗信号和腹部呼吸电阻抗信号相位关系的 G值小于阈值 A2时，则 使 G=l ; 当表示胸部呼吸电阻抗信号和腹部呼吸电阻抗信号相位关系的 G值 大于阈值八2时， 则使 G=2， 同时以产生此 G值的窗函数内的数据起点为胸部 呼吸电阻抗信号和腹部呼吸电阻抗信号的一个相位关系分段点， 以每段幅值 变化分段信号的起点和终点分别为该段信号第一个和最后一个相位关系分段 点， 提取相邻的相位关系分段点之间的胸部呼吸电阻抗信号和腹部呼吸电阻 抗信号为相位关系分段信号， 且胸部呼吸电阻抗信号和腹部呼吸电阻抗信号 的相位关系分段信号一一对应。滑动窗函数的宽度 \¥2可以根据不同的采样率 f设定不同的值， 一般设定窗函数的宽度点数为 30*f， 即采用 30秒的呼吸数 据作为滑动窗函数的宽度。 同时设定滑动窗函数的步长 1^2为窗函数的宽度 W2。 滑动窗函数的宽度 W2及步长 L2均可根据需要进行调整。 一般根据呼吸 频率设定胸部呼吸电阻抗信号和腹部呼吸电阻抗信号的相位关系的变化阈值 A2为 1， 也可以根据呼吸频率的变化调整为其他值。 步骤 S1035 :分别提取各相位关系分段信号内的胸部呼吸电阻抗信号和腹 部呼吸电阻抗信号的极大值点 Pe^P Pai ;计算各相位关系分段信号内的胸部呼 n y p ci. 吸电阻抗信号和腹部呼吸电阻抗信号的极大值点的平均值 Fe= ^ ^和 n
y p ai .
Fa= n ；
步骤 S1036: 利用 = 计算获取各相位关系分段信号内的胸部呼吸 电阻抗信号和腹部呼吸电阻抗信号之间的幅值变化关系；
步骤 S1037:计算各相位关系分段信号内的胸部呼吸电阻抗信号和腹部呼 吸电阻抗信号的频谱； 然后计算各相位关系分段信号内的胸部呼吸电阻抗信 号 和 l -
Figure imgf000013_0001
步骤 S1038: 利用 1= 计算获取各相位关系分段信号内的胸部呼吸电阻 抗信号和腹部呼吸电阻抗信号之间的频谱变化关系； 各分段数据的频谱变化 关系包括各分段信号内的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号的频谱 成分的差异及各自的功率谱的特征。 由于带通滤波器的作用， 有效的频谱成 分范围为 0.2-0.45HZ, 其他频谱成分被有效的滤除。 不同的呼吸模式状态下， 胸部呼吸电阻抗信号的频谱与腹部呼吸电阻抗信号的频谱具有显著差异， 相 同的频率成分在胸部呼吸电阻抗信号频谱及腹部呼吸电阻抗信号频谱中占有 不同的比重， 这些数据都可以通过实验获取， 从而建立不同呼吸模式下胸部 呼吸电阻抗信号的频谱成分与腹部呼吸电阻抗信号的频谱成分的关系。
步骤 S1039: 建立睡眠呼吸模式 M和胸部呼吸电阻抗信号与腹部呼吸电 阻抗信号之间的相位关系 G、 幅值变化关系 F及频谱变化关系 I的参数模型 关系式 M=G*F*I。 计算各相位关系分段信号内的 M值， 根据 M值的范围判 断各相位关系分段信号内呼吸信号的呼吸模式。 其中 G值的计算可以直接利 用步骤 S1033中求取的 G值， M值的不同对应于不同的呼吸模式， 呼吸模式 对应的 M值范围可以根据实验经验获取； 具体地， 当 M值为 10~20时， 识别 该分段的睡眠呼吸模式为胸式呼吸； 当 M值为 2~6时， 识别该分段的睡眠呼 吸模式为顺式腹式呼吸； 当 M值大于 20时， 识别该分段的睡眠呼吸模式为 逆式腹式呼吸； 当 M值为 7~10时， 识别该分段的睡眠呼吸模式为中枢型呼 吸暂停； 当 M值小于 2时， 识别该分段的睡眠呼吸模式为阻塞型呼吸暂停。
根据上述发明的基于生物电阻抗的睡眠呼吸模式识别方法,本发明还提供 了一种基于生物电阻抗的睡眠呼吸模式识别装置， 以下就本发明的基于生物 电阻抗的睡眠呼吸模式识别方法装置的具体示例进行详细说明。
本实施例的基于生物电阻抗的睡眠呼吸模式识别装置， 包括： 电极和监 电极包括激励电极和检测电极。 激励电极， 佩戴在人体胸部和腹部位置， 用于提供激励电流至人体组织； 检测电极， 佩戴在人体胸部和腹部位置， 用 于接收人体胸部和腹部的电压信号；
如图 6所示， 所述监测仪的硬件部分包括：
激励电流模块， 用于为激励电极提供稳定的电流激励；
多通道开关模块， 与电极、 激励电流模块和阻抗计算模块连接， 用于向 不同部位的激励电极提供电流激励、 接收检测电极的电压信号和接收检测电 极的电压信号传输给阻抗计算模块； 阻抗计算模块， 与多通道开关模块连接， 根据检测电极提供的电压信号 计算出胸部呼吸电阻抗信号和腹部呼吸电阻抗信号；
主控模块， 与阻抗计算模块连接， 用于对胸部呼吸电阻抗信号和腹部呼 吸电阻抗信号进行模数转换并对信号进行分析处理， 然后分类识别确定人体 的呼吸模式；
报警模块， 与主控模块连接， 用于对超时的呼吸暂停进行报警提醒； 电源模块， 为以上各个模块提供模拟电压和数字电压，
监测仪， 通过激励电流模块产生激励电流提供给多通道开关模块， 多通 道开关模块选择导通不同通路后将激励电流传输至激励电极， 激励电极将激 励电流注入人体， 同时检测电极采集胸部和腹部的电压信号后将其发送至多 通道开关模块， 多通道开关模块将接收的电压信号发送至阻抗计算模块， 阻 抗计算模块根据电压信号计算出胸部呼吸电阻抗信号和腹部呼吸电阻抗信 号， 最后将胸部呼吸电阻抗信号和腹部呼吸电阻抗信号送入主控模块转化为 数字信号后进行信号的分析处理并分段识别确定人体的呼吸模式。
如图 2所示， 测试前先将测试电极贴于人体， 可以采用四极法测量人体 电阻抗， 具体地， 将两对激励电极 Ip、 In及两对检测电极 Vp、 Vn分别固定 在胸部及腹部相应位置。 胸部激励电极正极 Ip固定在右胸乳头左侧， 激励电 极负极 In固定在激励电极 Ip在背部的投影位置； 胸部检测电极 Vp固定在右 胸乳头右侧， 检测电极 Vn固定在检测电极 Vp在背部的投影位置； 腹部激励 电极正极 Ip固定在肚脐左侧， 激励电极负极 In固定在激励电极 Ip在背部的 投影位置； 腹部检测电极 Vp固定在肚脐右侧， 检测电极 Vn固定在检测电极 Vp在背部的投影位置。 放置好检测电极后， 启动监测仪即可正常工作， 获取胸部呼吸电阻抗信 号和腹部呼吸电阻抗信号后进行信号的分析处理， 然后分段识别确定人体的 呼吸模式；
本发明的装置由激励电流模块可提供频率范围 10~100KHz， 幅值范围为 0.5-5mA 稳定的激励电流输入人体， 由检测电极获取人体的电压信号经阻抗 计算模块后得到人体的电阻抗信号， 接着送入主控模块完成初步滤波及模拟 数字转换后进行信号的分析处理， 然后分段识别确定人体的呼吸模式。 作为 一种补充方式， 监测仪可将转换后的数字信号发送至电脑进行信号的分析处 理及分段识别。

Claims

1. 一种基于生物电阻抗的睡眠呼吸模式识别方法， 其特征在于， 包括如下步骤：
( a) 采集人体的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号；
( b ) 对胸部呼吸电阻抗信号和腹部呼吸电阻抗信号分别进行数据处理转换成数字信号数 据；
( c) 根据胸部呼吸电阻抗信号和腹部呼吸电阻抗信号之间的关系对人体的睡眠呼吸信号进 行分段识别确定人体的呼吸模式。
2. 根据权利要求 1 所述的基于生物电阻抗的睡眠呼吸模式识别方法， 其特征在于， 所述步 骤 （a) 的具体步骤为：
通过固定在人体胸前乳头左侧或右侧区域及对应的背部投影位置的一对激励电极和固定在人 体肚脐左侧或右侧区域及对应的背部投影位置的另一对激励电极同步输入电流激励； 通过固定在人体胸前乳头右侧或左侧区域及对应的背部投影位置的一对信号检测电极和固定 在人体肚脐右侧或左侧区域及对应的背部投影位置的另一对信号检测电极同步采集人体胸部 和腹部的电压幅值数据， 经计算后得到胸部呼吸电阻抗信号和腹部呼吸电阻抗信号。
3. 根据权利要求 1 所述的基于生物电阻抗的睡眠呼吸模式识别方法， 其特征在于， 所述步 骤 （c) 的具体步骤为：
(cl ) 根据已为数字信号数据的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号之间的幅值变化 关系及相位关系进行一一对应的幅值变化分段及相位关系分段；
(c2) 分析获取各相位关系分段内胸部呼吸电阻抗信号和腹部呼吸电阻抗信号之间的频谱变 化关系； 根据各相位关系分段信号的幅值变化关系、 相位关系和频谱变化关系对胸部呼吸电 阻抗信号和腹部呼吸阻抗信号进行分类识别并确定人体的睡眠呼吸模式。
4. 根据权利要求 3 所述的基于生物电阻抗的睡眠呼吸模式识别方法， 其特征在于， 所述步 骤 （cl ) 的具体步骤为：
(cll ) 设定极大值点变化阈值；
( cl2) 利用滑动窗函数对已为数字信号数据的胸部呼吸电阻抗信号进行处理， 提取相邻滑 动窗函数内的呼吸信号极大值点的平均值， 将相邻滑动窗函数内的平均值的差的绝对值与极 大值点变化阈值进行比较， 如果该绝对值大于极大值点变化阈值， 则后一个滑动窗函数内的 数据起点为一个幅值变化分段点；
( cl3 ) 以第一个幅值变化分段点为起点， 提取相邻的幅值变化分段点之间的胸部呼吸电阻 抗信号作为幅值变化分段信号， 依据同样的幅值变化分段点对腹部呼吸电阻抗信号进行分段 获取相对应的幅值变化分段信号； (cl4) 对幅值变化分段信号内的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号加上滑动窗函 数， 分别提取滑动窗函数内的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号的信号波峰出现的 一系列时刻；
(cl5 ) 根据提取的一系列时刻计算滑动窗函数内的胸部呼吸电阻抗信号和腹部呼吸电阻抗 信号之间的相位关系 G;
(cl6) 设定相位阈值， 将相位关系 G与相位阈值进行比较， 如果 G值大于相位阈值， 则该 滑动窗函数的数据起点为一个相位关系分段点；
(cl7 ) 以每段幅值变化分段信号的起点和终点分别为该段信号第一个和最后一个相位关系 分段点， 提取相邻的相位关系分段点之间的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号为相 位关系分段信号。
5. 根据权利要求 3 所述的基于生物电阻抗的睡眠呼吸模式识别方法， 其特征在于， 所述步 骤 （c2) 的具体步骤为：
(c21 ) 分别提取各相位关系分段信号内的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号的极 大值点；
(c22) 根据极大值点计算各相位关系分段信号内的胸部呼吸电阻抗信号和腹部呼吸电阻抗 信号的极大值点的平均值；
(c23 ) 计算各相位关系分段信号内的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号的极大值 点的平均值的比值， 获得信号之间的幅值变化关系 F;
(c24) 计算各相位关系分段信号内的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号的频谱；
(c25 ) 计算各相位关系分段信号内的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号在设定频 率范围内的频谱积分；
(c26) 计算各相位关系分段信号内的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号的频谱积 分的比值， 获得信号之间的频谱变化关系 I；
(c27) 建立睡眠呼吸模式 M和胸部呼吸电阻抗信号与腹部呼吸电阻抗信号之间的相位关系 G、 幅值变化关系 F及频谱变化关系 I的参数模型关系式 M=GWI; 计算各相位关系分段信 号内的胸部呼吸电阻抗信号和腹部呼吸电阻抗信号的 M值， 根据 M值的范围判断各段呼吸 信号的呼吸模式。
6. 一种基于生物电阻抗的睡眠呼吸模式识别装置， 其特征在于， 包括电极和监测仪， 其 中，
电极包括激励电极和检测电极； 激励电极， 佩戴在人体胸部和腹部位置， 用于提供激励电流 至人体组织； 检测电极， 佩戴在人体胸部和腹部位置， 用于接收人体胸部和腹部的电压信 号；
所述监测仪的硬件部分包括：
激励电流模块， 用于为激励电极提供电流激励；
多通道开关模块， 与电极、 激励电流模块和阻抗计算模块连接， 用于向不同部位的激励电极 提供电流激励、 接收检测电极的电压信号和接收检测电极的电压信号传输给阻抗计算模块； 阻抗计算模块， 与多通道开关模块连接， 根据检测电极提供的电压信号计算出胸部呼吸电阻 抗信号和腹部呼吸电阻抗信号；
主控模块， 与阻抗计算模块连接， 用于对胸部呼吸电阻抗信号和腹部呼吸电阻抗信号进行模 数转换并对信号进行分析处理， 然后分类识别确定人体的呼吸模式；
报警模块， 与主控模块连接， 用于对超时的呼吸暂停进行报警提醒；
电源模块， 为以上各个模块提供模拟电压和数字电压；
监测仪， 通过激励电流模块产生激励电流提供给多通道开关模块， 多通道开关模块选择导通 不同通路后将激励电流传输至激励电极， 激励电极将激励电流注入人体， 同时检测电极采集 胸部和腹部的电压信号后将其发送至多通道开关模块， 多通道开关模块将接收的电压信号发 送至阻抗计算模块， 阻抗计算模块根据电压信号计算出胸部呼吸电阻抗信号和腹部呼吸电阻 抗信号， 最后将胸部呼吸电阻抗信号和腹部呼吸电阻抗信号送入主控模块转化为数字信号后 进行信号的分析处理并分段识别确定人体的呼吸模式。
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