JP6315576B2 - Sleep breathing sound analysis apparatus and method - Google Patents
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Description
この発明は、測定された寝息呼吸音を解析して、睡眠状態や睡眠時における無呼吸及び低呼吸状態を判定する寝息呼吸音解析装置及び方法に関するものである。 The present invention relates to a sleep breathing sound analysis apparatus and method for analyzing a sleep breathing sound measured to determine a sleep state or apnea and hypopnea state during sleep.
従来の睡眠計測装置では身体にセンサー等を装着したり、被検者に麻酔をかけたりしなければならなかったため、手間がかかるだけでなく睡眠の妨げとなることもあった。
また、市販の簡易な睡眠計では装置の動きや寝相により測定結果に大きなずれが生じるなど、精度が高いとは言いがたかった。
さらに、センサーマットを、使用する寝具の下に敷くだけで測定できるものもあるが、値段が高いことや出張先などでの使用が難しいなどの問題がある。
In the conventional sleep measuring device, it has been necessary to wear a sensor or the like on the body or to anesthetize the subject, which is not only troublesome but also disturbs sleep.
In addition, it is difficult to say that a commercially available simple sleep meter has high accuracy such as a large deviation in measurement results due to the movement of the device or the sleep phase.
Furthermore, some sensors can be measured simply by laying them under the bedding used, but there are problems such as high price and difficulty in use at business trip destinations.
例えば、特許文献１（特開２０１４−６４６７５号公報）には、呼気音発生器を被検者の鼻及び／又は口を覆うように装着して呼吸音を高周波音に変換し、その音を受信して解析を行うことで、非拘束かつ低侵襲で無呼吸を検知することが可能な無呼吸検知システムが記載されている。
また、特許文献２（特開２００６−３２０６４１号公報）には、被検者に麻酔による人工的短時間睡眠を起こさせ、この被検者の睡眠中の呼吸音を解析することによって睡眠時無呼吸症の有無又は軽重を定量的に判定するシステムが記載されている。
For example, in Patent Document 1 (Japanese Patent Application Laid-Open No. 2014-64675), an exhalation sound generator is attached so as to cover a subject's nose and / or mouth, and a breathing sound is converted into a high-frequency sound. An apnea detection system capable of detecting apnea in a non-constrained and minimally invasive manner by receiving and analyzing is described.
Patent Document 2 (Japanese Patent Application Laid-Open No. 2006-320641) discloses that a subject is caused to sleep artificially for a short time by anesthesia, and the sleep sound of the subject is analyzed by analyzing the breathing sound during sleep. A system for quantitatively determining the presence or absence or severity of respiratory disease is described.
この発明は、従来の各種システムにおける様々な問題点を解消するため、自然な状態で眠っている人の寝息呼吸音を集音マイクや顔の近くに設置したマイクで検出し、検出した寝息呼吸音のデータを解析することによって、精度良く睡眠状態や睡眠時における無呼吸及び低呼吸を判定し、酸素摂取状態を推定できる寝息呼吸音解析装置及び方法の提供を目的としてなされたものである。 In order to solve various problems in various conventional systems, the present invention detects the sleep breathing sound of a person sleeping in a natural state with a sound collecting microphone or a microphone installed near the face, and detects the detected sleep breathing. The object of the present invention is to provide a sleep breathing sound analysis apparatus and method that can accurately determine sleep state and apnea and hypopnea during sleep and estimate the oxygen uptake state by analyzing sound data.
請求項１に係る発明は、被験者の寝息呼吸音を検出する寝息呼吸音検出手段と、該寝息呼吸音検出手段で検出された寝息信号に基づいて、呼吸特徴波形を得る呼吸特徴波形取得手段と、前記呼吸特徴波形において吸気波形と呼気波形を判別する呼吸波形判別手段と、前記呼吸特徴波形における吸気波形の時間間隔、呼気波形の時間間隔、吸気波形から呼気波形の時間間隔、呼気波形から吸気波形の時間間隔、吸気波形の時間幅、呼気波形の時間幅、吸気波形の振幅値及び呼気波形の振幅値から選択した一又は複数の特徴値を抽出する特徴値抽出手段と、所定期間における前記一又は複数の特徴値に基づいて、前記所定期間における前記被験者の睡眠状態の判定、無呼吸状態若しくは低呼吸状態の判定又は酸素摂取状態の推定を行う状態判定手段を備える寝息呼吸音解析装置において、
前記特徴値抽出手段は、少なくとも前記吸気波形の振幅値及び前記呼気波形の振幅値又は少なくとも前記吸気波形から呼気波形の時間間隔及び前記呼気波形から吸気波形の時間間を抽出し、
前記状態判定手段は、前記吸気波形の振幅値及び前記呼気波形の振幅値が一定期間毎に設定した閾値を超えた部分の時間幅の総和が前記一定期間に占める割合の変化、一定期間毎の前記吸気波形から呼気波形の時間間隔又は前記呼気波形から吸気波形の時間間隔が閾値を超えた時間間隔の抽出回数若しくは合計値に基づいて、前記被験者の無呼吸状態若しくは低呼吸状態の判定又は酸素摂取状態の推定を行うことを特徴とする。
The invention according to claim 1 is a sleep breathing sound detection unit that detects a sleep breathing sound of a subject, and a breathing feature waveform acquisition unit that obtains a breathing feature waveform based on the sleep signal detected by the sleep breathing sound detection unit. A breathing waveform discriminating means for discriminating an inspiratory waveform and an expiratory waveform in the respiratory characteristic waveform; a time interval of the inspiratory waveform in the respiratory characteristic waveform; a time interval of the expiratory waveform; a time interval of the expiratory waveform from the inspiratory waveform; A feature value extracting means for extracting one or a plurality of feature values selected from the time interval of the waveform, the time width of the inspiratory waveform, the time width of the expiratory waveform, the amplitude value of the inspiratory waveform, and the amplitude value of the expiratory waveform; A state determination hand that performs determination of the sleep state of the subject, determination of an apnea state or hypopnea state, or estimation of an oxygen intake state based on one or more feature values. In sleeper's breathing breathing sound analyzer comprising,
The feature value extracting means extracts at least the amplitude value of the inspiratory waveform and the amplitude value of the expiratory waveform or at least the time interval of the expiratory waveform from the inspiratory waveform and the time period of the inspiratory waveform from the expiratory waveform,
The state determination means includes a change in a ratio of a sum of time widths of portions where the amplitude value of the inspiratory waveform and the amplitude value of the expiratory waveform exceed a threshold set for each predetermined period in the predetermined period, Based on the number of extractions or the total value of the time interval from the inspiratory waveform to the expiratory waveform or the time interval in which the time interval from the expiratory waveform to the inspiratory waveform exceeds the threshold, the determination of the subject's apnea or hypopnea state or oxygen The intake state is estimated.
請求項２に係る発明は、請求項１に記載の寝息呼吸音解析装置において、前記特徴値抽出手段は、少なくとも前記吸気波形の時間間隔又は前記呼気波形の時間間隔を抽出し、前記状態判定手段は、前記所定期間における前記吸気波形の時間間隔又は前記呼気波形の時間間隔の変化度合に基づいて、前記被験者の睡眠状態がレム睡眠であるかノンレム睡眠であるかの判定を行うことを特徴とする。 The invention according to claim 2, in sleeper's breathing breathing sound analysis apparatus according to claim 1, wherein the feature value extraction unit extracts a time interval of the time interval or the expiratory waveform of at least the intake waveform, the state determining means Determining whether the sleep state of the subject is REM sleep or non-REM sleep based on the degree of change of the time interval of the inspiratory waveform or the time interval of the expiratory waveform in the predetermined period. To do.
請求項３に係る発明は、請求項１又は２に記載の寝息呼吸音解析装置において、前記特徴値抽出手段は、少なくとも前記吸気波形の時間幅及び前記呼気波形の時間幅又は少なくとも前記吸気波形の振幅値及び前記呼気波形の振幅値を抽出し、前記状態判定手段は、前記所定期間における前記吸気波形の時間幅と前記呼気波形の時間幅の長さの関係又は前記吸気波形の振幅値と前記呼気波形の振幅値の大きさの関係に基づいて、前記被験者が低酸素状態か否かの判定を行うことを特徴とする。 According to a third aspect of the present invention, in the sleep breathing sound analysis device according to the first or second aspect, the feature value extracting means includes at least a time width of the inspiratory waveform and a time width of the expiratory waveform or at least the inspiratory waveform. An amplitude value and an amplitude value of the exhalation waveform are extracted, and the state determination unit is configured to determine a relationship between a time width of the inspiration waveform and a time width of the exhalation waveform in the predetermined period or an amplitude value of the inspiration waveform and the A determination is made as to whether or not the subject is in a hypoxic state based on the relationship between the amplitude values of the expiratory waveform.
請求項１に係る発明の寝息呼吸音解析装置によれば、寝息呼吸音検出手段で検出された寝息信号に基づいて、呼吸特徴波形を得る呼吸特徴波形取得手段と、呼吸特徴波形において吸気波形と呼気波形を判別する呼吸波形判別手段を備えているので、特徴値抽出手段によって、吸気波形の時間間隔、呼気波形の時間間隔、吸気波形から呼気波形の時間間隔、呼気波形から吸気波形の時間間隔、吸気波形の時間幅、呼気波形の時間幅、吸気波形の振幅値及び呼気波形の振幅値から選択した一又は複数の特徴値を抽出することができる。
そして、状態判定手段により、抽出した特徴値の所定期間における合計値、平均値又は変化度合に基づいて、所定期間における被験者の睡眠状態の判定又は酸素摂取状態の推定を行えば、寝息呼吸音を非拘束で直接検出するシステムであるにもかかわらず、従来の同様のシステムに比べ、判定又は推定精度が格段に向上するという効果が得られる。
また、身体にセンサー等を装着したり、被検者に麻酔をかけたりする必要がないので、手間がかからず被験者の睡眠を妨げることもない。
そのため、睡眠障害の早期発見、電車、バス、タクシーの運転手等、居眠りが事故をもたらす危険性のある仕事に携わっている人の睡眠状態チェックなどへの利用性が高い。
さらに、吸気波形の振幅値及び呼気波形の振幅値が一定期間毎に設定した閾値を超えた部分の時間幅の総和が一定期間に占める割合の変化、一定期間毎の吸気波形から呼気波形の時間間隔又は呼気波形から吸気波形の時間間隔が閾値を超えた時間間隔の抽出回数若しくは合計値に基づいて、被験者の無呼吸状態若しくは低呼吸状態の判定又は酸素摂取状態の推定を精度良く行うことができるという効果が得られる。
According to the sleep breathing sound analysis apparatus of the first aspect of the present invention, the breathing feature waveform acquisition means for obtaining the breathing feature waveform based on the sleepiness signal detected by the sleep breathing sound detection means, and the inspiration waveform in the breathing feature waveform Since the respiratory waveform discriminating means for discriminating the expiratory waveform is provided, the characteristic value extracting means allows the inspiratory waveform time interval, the expiratory waveform time interval, the inspiratory waveform to expiratory waveform time interval, and the expiratory waveform to inspiratory waveform time interval. One or a plurality of feature values selected from the time width of the inspiratory waveform, the time width of the expiratory waveform, the amplitude value of the inspiratory waveform, and the amplitude value of the expiratory waveform can be extracted.
Then, if the state determination means determines the sleep state of the subject in the predetermined period or estimates the oxygen intake state based on the total value, average value, or degree of change of the extracted feature values in the predetermined period, the sleep breathing sound is obtained. In spite of the non-constrained direct detection system, the determination or estimation accuracy can be greatly improved as compared with the conventional similar system.
Further, since it is not necessary to wear a sensor or the like on the body or to anesthetize the subject, it does not take time and does not disturb the sleep of the subject.
Therefore, it is highly useful for early detection of sleep disorders, checking the sleep state of people engaged in work where there is a risk that falling asleep will cause accidents, such as train, bus and taxi drivers.
In addition, the change in the proportion of the sum of the time widths of the portions where the amplitude value of the inspiratory waveform and the amplitude value of the expiratory waveform exceed the threshold set for each predetermined period, the time of the expiratory waveform from the inspiratory waveform for each predetermined period It is possible to accurately determine the apnea state or hypopnea state of the subject or estimate the oxygen intake state based on the number of extractions or the total value of the time intervals when the time interval of the inspiratory waveform exceeds the threshold value from the interval or the expiratory waveform. The effect that it can be obtained.
請求項２に係る発明の寝息呼吸音解析装置によれば、請求項１に係る発明の効果に加え、所定期間における吸気波形の時間間隔又は呼気波形の時間間隔の変化度合に基づいて、被験者の睡眠状態がレム睡眠であるかノンレム睡眠であるかの判定を精度良く行うことができるという効果が得られる。 According to the sleep breathing sound analysis apparatus of the invention of claim 2 , in addition to the effect of the invention of claim 1 , based on the degree of change in the time interval of the inspiratory waveform or the time interval of the expiratory waveform in a predetermined period, An effect is obtained that it is possible to accurately determine whether the sleep state is REM sleep or non-REM sleep.
請求項３に係る発明の寝息呼吸音解析装置によれば、請求項１又は２に係る発明の効果に加え、所定期間における吸気波形の時間幅と前記呼気波形の時間幅の長さの関係又は前記吸気波形の振幅値と前記呼気波形の振幅値の大きさの関係に基づいて、被験者が低酸素状態か否かの判定を精度良く行うことができるという効果が得られる。 According to the sleep breathing sound analysis apparatus of the invention of claim 3 , in addition to the effect of the invention of claim 1 or 2 , the relationship between the time width of the inspiratory waveform and the time width of the expiratory waveform in a predetermined period or Based on the relationship between the amplitude value of the inspiratory waveform and the amplitude value of the expiratory waveform, it is possible to accurately determine whether or not the subject is in a hypoxic state.
実施例を説明する前に、本発明の寝息呼吸音解析装置を含む寝息計測・解析システムの概要について説明する。
図１に示すように、個人宅・介護施設等１で就寝中の被験者２の鼻部周囲に寝息呼吸音を検出する寝息呼吸音検出手段３（集音マイク等）を設置する。
寝息呼吸音検出手段３で検出された寝息信号（音声データ）は、寝息呼吸音検出手段３が備えるBluetooth（登録商標）等の無線通信手段４を介して、被験者が所有する携帯端末５（タブレット型端末やスマートフォン）に一旦送られ、その携帯端末からインターネット等の無線通信回線を経由してデータサーバー機能を有する寝息呼吸音解析装置６に送信される。
寝息呼吸音解析装置６は、内蔵する記憶装置やクラウドメモリに受信した寝息信号を記憶させ、各種の信号処理を行ってデータ解析を行う。そして、その解析結果は携帯端末５や被験者２が通院している病院・診療所等７に設置されている端末８（ＰＣ等）に送られ、被験者２や主治医等９がその解析結果を確認できるようになっている。
そして、本発明はこのような寝息計測・解析システムにおける寝息呼吸音解析装置及び方法に関する。
以下、実施例によって本発明の実施形態を説明する。
Before describing the embodiment, an outline of a sleep measurement / analysis system including the sleep breathing sound analysis apparatus of the present invention will be described.
As shown in FIG. 1, a sleep breathing sound detection means 3 (sound collecting microphone or the like) that detects a sleep breathing sound around a nose of a subject 2 sleeping at a private home / care facility 1 or the like is installed.
The sleep signal (voice data) detected by the sleep breathing sound detection means 3 is transmitted to the portable terminal 5 (tablet) owned by the subject via the wireless communication means 4 such as Bluetooth (registered trademark) provided in the sleep breathing sound detection means 3. Is transmitted to the sleep breathing sound analysis apparatus 6 having a data server function via a wireless communication line such as the Internet.
The sleep breathing sound analysis device 6 stores the received sleep signal in a built-in storage device or cloud memory, performs various signal processing, and performs data analysis. And the analysis result is sent to the terminal 8 (PC etc.) installed in the portable terminal 5 or the hospital / clinic clinic 7 where the subject 2 visits, and the subject 2 or the attending physician 9 confirms the analysis result. It can be done.
The present invention relates to a sleep breathing sound analysis apparatus and method in such a sleep measurement / analysis system.
Hereinafter, embodiments of the present invention will be described by way of examples.
図２は、寝息信号の実例及びその寝息信号を処理した結果のグラフを示す図であり、これらのグラフを用いて、実施例に係る寝息呼吸音解析装置が寝息信号をどのように処理するかについて説明する。
寝息呼吸音解析装置は、まず寝息信号から所定長さ（例えば１０秒）ごとについての閾値Ｈｖを設定し、寝息信号がＨｖを超えた部分の時間δｉ（ｉ＝１〜ｎ）の総和を所定長さで割った値、すなわち、閾値Ｈｖを超えたデータの総数を所定長さにおけるデータの総数（例えばサンプリング周波数が11025Ｈｚの場合、１０秒間のデータの総数は110250）で割った値の変化を呼吸の変動とみなし、無呼吸判別用パラメータとして使用する。
FIG. 2 is a diagram illustrating an example of a sleep signal and a graph of the result of processing the sleep signal. Using these graphs, how the sleep breathing sound analyzer according to the embodiment processes the sleep signal Will be described.
The sleep breathing sound analysis apparatus first sets a threshold value Hv for each predetermined length (for example, 10 seconds) from a sleep signal, and determines a total sum of times δi (i = 1 to n) of a portion where the sleep signal exceeds Hv. A change of a value obtained by dividing the value divided by the length, that is, the total number of data exceeding the threshold value Hv by the total number of data in a predetermined length (for example, the total number of data for 10 seconds is 110250 when the sampling frequency is 11025 Hz). It is regarded as a change in respiration and used as an apnea discrimination parameter.
次に、寝息信号に対して吸気又は呼気のうちどちらか値の小さい信号が顕著に現れるように増幅処理を行った後、５０〜２０００Ｈｚのバンドパスフィルター処理と正規化を行い、処理後信号を得る（図２の処理後信号のグラフ）。
そして、処理後信号の包絡線を求め呼吸特徴波形を得る（図２の呼吸特徴波形のグラフ）。
この処理は、所定長さデータごとに同様に行われる。
その後、適宜選択した所定期間で得られた呼吸特徴波形に対して、様々な処理を施し、１又は複数の特徴値を抽出し、その所定期間における被験者２の睡眠状態の判定、無呼吸状態若しくは低呼吸状態の判定又は酸素摂取状態の推定を行う。処理の一例としては、吸気波形の時間間隔、すなわち呼吸周期Ｔｄを求める処理、吸気波形から呼気波形の時間間隔Ｔｅを抽出する処理、吸気波形の時間幅Ｔ１を抽出する処理、呼気波形の時間幅Ｔ２を抽出する処理、吸気と呼気間の呼吸停止時間間隔Ｔｓを抽出する処理が挙げられる。
また、適宜設定した期間中における呼吸特徴波形に周波数解析を施し、図２のスペクトルのグラフに見られるように、第１ピークに対応する周波数ｆｄ（０．２５Ｈｚ）又は第２ピークに対応する周波数ｆｅ（０．５Ｈｚ）を求め、Ｔ≒１／ｆの関係からＴｄの平均値（≒４秒）又はＴｅの平均値（≒２秒）を導出すると、簡便な処理によって呼吸周期Ｔｄや吸気波形から呼気波形の時間間隔Ｔｅの平均値を求めることができる。
Next, after performing an amplification process so that a signal with a smaller value of either inspiration or expiration appears significantly with respect to the sleep signal, bandpass filter processing of 50 to 2000 Hz and normalization are performed, and the processed signal is Obtained (graph of processed signal in FIG. 2).
Then, an envelope of the processed signal is obtained to obtain a respiratory feature waveform (graph of the respiratory feature waveform in FIG. 2).
This process is similarly performed for each predetermined length data.
After that, various processes are performed on the respiratory characteristic waveform obtained in a predetermined period selected as appropriate, and one or a plurality of characteristic values are extracted, and the sleep state of the subject 2 in the predetermined period is determined. Determine hypopnea or estimate oxygen uptake. As an example of processing, processing for obtaining the time interval of the inspiratory waveform, that is, the respiration cycle Td, processing for extracting the time interval Te of the expiratory waveform from the inspiratory waveform, processing for extracting the time width T1 of the inspiratory waveform, time width of the expiratory waveform A process of extracting T2 and a process of extracting a breathing stop time interval Ts between inspiration and expiration are exemplified.
In addition, frequency analysis is performed on the respiratory feature waveform during an appropriately set period, and the frequency fd (0.25 Hz) corresponding to the first peak or the frequency corresponding to the second peak as seen in the spectrum graph of FIG. When fe (0.5 Hz) is obtained and the average value of Td (≈4 seconds) or the average value of Te (≈2 seconds) is derived from the relationship of T≈1 / f, the respiratory cycle Td and the inspiratory waveform are obtained by simple processing. From this, the average value of the time interval Te of the expiration waveform can be obtained.
図３は、無呼吸状態若しくは低呼吸状態か否かを判定する第１の方法についての説明図であり、これらのグラフを用いて、被験者２が無呼吸状態若しくは低呼吸状態か否かを判定する方法について説明する。
呼吸変動のグラフは、寝息信号に基づいて求めた上述の無呼吸判別用パラメータの変動を示すものである。
平滑化呼吸変動のグラフは、所定期間の呼吸変動データに平滑化処理を施して得られる時系列データを示しており、無呼吸のグラフは、呼吸変動データとその移動平均値の差を求め、その差の変化が所定の閾値より小さい場合をＬｏｗ、大きい場合をＨｉｇｈとしたものである。
無呼吸のグラフにおいて、Ｌｏｗになっている部分が無呼吸若しくは低呼吸状態となっていることを示し、５０分過ぎの４回のＬｏｗ部分のうち最後のＬｏｗ部分を精査したところ、５７分７秒〜５７分４２秒にかけて約３５秒間にわたって無呼吸状態が継続していた。
FIG. 3 is an explanatory diagram of a first method for determining whether or not an apnea state or a hypopnea state. Using these graphs, it is determined whether or not the subject 2 is an apnea state or a hypopnea state. How to do will be described.
The breathing fluctuation graph shows the fluctuation of the apnea discrimination parameter obtained based on the sleep signal.
The smoothed breathing fluctuation graph shows time series data obtained by smoothing the breathing fluctuation data for a predetermined period, and the apnea graph calculates the difference between the breathing fluctuation data and its moving average value, When the change in the difference is smaller than a predetermined threshold, it is Low, and when it is larger, it is High.
In the apnea graph, the low part indicates apnea or hypopnea, and when the last low part of the four low parts over 50 minutes was examined, 57 minutes 7 From the second to 57 minutes 42 seconds, the apnea state continued for about 35 seconds.
図４は、無呼吸状態若しくは低呼吸状態か否かを判定する第２の方法についての説明図であり、このグラフを用いて、被験者２が無呼吸状態若しくは低呼吸状態か否かを判定する方法について説明する。
所定期間の呼吸特徴波形において、吸気と呼気間の呼吸停止時間間隔Ｔｓを抽出する処理を施した後、Ｔｓが閾値（例えば１０秒）を超えている部分Ｔｎを抽出する。Ｔｎの抽出回数ならびに間隔長さが睡眠時無呼吸状態若しくは低呼吸状態を示すパラメータである。また、所定閾値を超えた時間間隔Ｔｎの変化を酸素摂取状態の推測に用いる。
FIG. 4 is an explanatory diagram of a second method for determining whether or not the patient is in an apnea state or a hypopnea state. Using this graph, it is determined whether or not the subject 2 is in an apnea state or a hypopnea state. A method will be described.
After performing a process of extracting a breathing stop time interval Ts between inspiration and expiration in a breathing characteristic waveform of a predetermined period, a portion Tn where Ts exceeds a threshold value (for example, 10 seconds) is extracted. The number of extractions of Tn and the interval length are parameters indicating a sleep apnea state or a hypopnea state. Moreover, the change of the time interval Tn exceeding the predetermined threshold is used for estimating the oxygen uptake state.
図５は、レム睡眠であるかノンレム睡眠であるかを判定する方法についての説明図であり、このグラフを用いて、被験者２がレム睡眠状態かノンレム睡眠状態かを判定する方法について説明する。
呼吸周期のグラフは、呼吸特徴波形から求めた呼吸周期Ｔｄが変動する様子を示しており、Ｔｄが激しく変動し呼吸周期のばらつきが大きいところでは、呼吸が不規則なレム睡眠状態となっており、Ｔｄが安定し呼吸周期のばらつきが小さいところでは、呼吸が規則的なノンレム睡眠状態となっている。
移動平均のグラフは、測定対象期間の呼吸周期データに移動平均処理を施して得られる時系列データを示しており、周期の短いところでは、レム睡眠状態となっており、周期の長いところではノンレム睡眠状態となっている。
睡眠状態のグラフは、呼吸周期データとその移動平均値の差を求め、その差の変化が所定の閾値より大きい場合をＬｏｗ、小さい場合をＨｉｇｈとしたものである。
睡眠状態のグラフにおいて、Ｌｏｗになっている部分が覚醒状態又はレム睡眠状態となっていることを示し、Ｈｉｇｈになっている部分がノンレム睡眠状態となっていることを示している。
FIG. 5 is an explanatory diagram of a method for determining whether it is REM sleep or non-REM sleep, and a method for determining whether the subject 2 is in a REM sleep state or a non-REM sleep state will be described using this graph.
The respiration cycle graph shows how the respiration cycle Td obtained from the respiration feature waveform fluctuates. Where the Td fluctuates significantly and the respiration cycle varies greatly, the respiration is in an irregular REM sleep state. , Where Td is stable and the variation in respiratory cycle is small, breathing is in a regular non-REM sleep state.
The moving average graph shows time-series data obtained by applying moving average processing to the respiratory cycle data of the measurement target period, where the REM sleep state is in a short cycle and non-REM in a long cycle. Sleeping.
In the sleep state graph, the difference between the respiratory cycle data and the moving average value is obtained, and when the change of the difference is larger than a predetermined threshold, it is Low, and when the change is small, it is High.
In the graph of the sleep state, the portion that is Low indicates that it is an awake state or a REM sleep state, and the portion that is High indicates that it is a non-REM sleep state.
図６は、低酸素状態か否かを判定する方法についての説明図であり、このグラフと図２の呼吸特徴波形のグラフを用いて、被験者２が低酸素状態か否かを判定する方法について説明する。
図２の呼吸特徴波形のグラフにおいて、吸気波形の時間幅Ｔ１が呼気波形の時間幅Ｔ２より長い場合、気道が狭くなり吸気困難によって、Ｔ１が長くなっていることが考えられ、これは低酸素の状態と判定される。
また、吸気波形の振幅値が呼気波形の振幅値より大きい場合も、同様に低酸素の状態と判定される。
すなわち、図６の呼吸特徴波形のグラフにおいて、吸気波形の振幅値が直後の呼気波形の振幅値より大きく、かつ、その吸気波形の時間幅が直後の呼気波形の時間幅より大きくなっているところ（四角で囲った部分）は、個人差はあるものの、被験者２が十分に空気を吸い込めなかった可能性から血中酸素濃度が低下している疑いがある。
FIG. 6 is an explanatory diagram of a method for determining whether or not the subject is in a hypoxic state, and a method for determining whether or not the subject 2 is in a hypoxic state using this graph and the graph of the respiratory feature waveform in FIG. explain.
In the graph of the respiratory characteristic waveform of FIG. 2, when the time width T1 of the inspiratory waveform is longer than the time width T2 of the expiratory waveform, it is considered that T1 becomes longer due to narrowing of the airway and difficulty in inhalation. It is determined that
Similarly, when the amplitude value of the inspiratory waveform is larger than the amplitude value of the expiratory waveform, it is similarly determined as a hypoxic state.
That is, in the graph of the respiratory characteristic waveform of FIG. 6, the amplitude value of the inspiratory waveform is larger than the amplitude value of the immediately exhaled waveform, and the time width of the inspiratory waveform is larger than the time width of the immediately exhaled waveform. There is a suspicion that the blood oxygen concentration is lowered because there is a possibility that the subject 2 has not sufficiently inhaled air, although there are individual differences.
実施例が適用される寝息計測・解析システム並びに実施例の寝息呼吸音解析装置及び方法に関する変形例を列記する。
（１）実施例が適用される寝息計測・解析システムにおいては、寝息呼吸音検出手段３（集音マイク等）を就寝中の被験者２の鼻部周囲に設置したが、被験者２の枕元、上方（天井等）、被験者２の頭の近くにある柵若しくは照明器具等、又は被験者２が着用しているパジャマ等に設置しても良い。
（２）実施例が適用される寝息計測・解析システムにおいては、寝息呼吸音検出手段３で検出された寝息信号は、被験者が所有する携帯端末５に一旦送られるが、パーソナルコンピュータや専用端末に送られるようにしても良く、寝息呼吸音検出手段３自体に送信機能を持たせて直接寝息呼吸音解析装置６に送信できるようにしても良い。
（３）実施例においては、被験者２の睡眠状態の判定、無呼吸状態若しくは低呼吸状態の判定又は酸素摂取状態の推定を行う点しか説明していないが、いずれの場合も、判定や推定した結果について分かり易く説明する図、絵又は文言等を携帯端末５や端末８に表示し、被験者２や主治医等９が睡眠状態、無呼吸状態、低呼吸状態又は酸素摂取状態を把握し易くした方が良い。
（４）実施例の図２及び図５についての説明では、吸気波形の時間間隔を呼吸周期とし、呼吸周期のばらつきの大小によって、被験者２がレム睡眠状態かノンレム睡眠状態かを判定しているが、呼気波形の時間間隔を呼吸周期とし、同様にして被験者２がレム睡眠状態かノンレム睡眠状態かを判定するようにしても良い。
Modified examples of the sleep measurement / analysis system to which the embodiment is applied and the sleep breathing sound analysis apparatus and method of the embodiment are listed.
(1) In the sleep measurement / analysis system to which the embodiment is applied, the sleep breathing sound detection means 3 (sound collecting microphone, etc.) is installed around the nose of the subject 2 who is sleeping. (Ceiling etc.), a fence near the head of the subject 2, a lighting fixture, etc., or a pajamas worn by the subject 2 may be installed.
(2) In the sleep measurement / analysis system to which the embodiment is applied, the sleep signal detected by the sleep breath sound detection means 3 is once sent to the portable terminal 5 owned by the subject, but is sent to a personal computer or a dedicated terminal. Alternatively, the sleep breathing sound detection means 3 itself may have a transmission function so that it can be transmitted directly to the sleep breathing sound analysis device 6.
(3) In the embodiment, only the point of determining the sleep state of the subject 2, the determination of the apnea state or the hypopnea state, or the estimation of the oxygen intake state has been described. Easy-to-understand illustrations, pictures, or words displayed on the portable terminal 5 or terminal 8 to make it easier for the subject 2 or the attending physician 9 to understand the sleep state, apnea state, hypopnea state, or oxygen intake state Is good.
(4) In the description of FIG. 2 and FIG. 5 in the embodiment, the time interval of the inspiratory waveform is the respiratory cycle, and it is determined whether the subject 2 is in the REM sleep state or the non-REM sleep state based on the variation in the respiratory cycle. However, the time interval of the expiratory waveform may be set as the respiratory cycle, and it may be similarly determined whether the subject 2 is in the REM sleep state or the non-REM sleep state.
１ 個人宅・介護施設等 ２ 被験者 ３ 寝息呼吸音検出手段
４ 無線通信手段 ５ 携帯端末 ６ 寝息呼吸音解析装置
７ 病院・診療所等 ８ 端末 ９ 主治医等
DESCRIPTION OF SYMBOLS 1 Personal home, care facility, etc. 2 Subject 3 Sleeping breath sound detection means 4 Wireless communication means 5 Mobile terminal 6 Sleep breathing sound analysis device 7 Hospital / clinic etc. 8 Terminal 9 Doctor
Claims (3)
該寝息呼吸音検出手段で検出された寝息信号に基づいて、呼吸特徴波形を得る呼吸特徴波形取得手段と、
前記呼吸特徴波形において吸気波形と呼気波形を判別する呼吸波形判別手段と、
前記呼吸特徴波形における吸気波形の時間間隔、呼気波形の時間間隔、吸気波形から呼気波形の時間間隔、呼気波形から吸気波形の時間間隔、吸気波形の時間幅、呼気波形の時間幅、吸気波形の振幅値及び呼気波形の振幅値から選択した一又は複数の特徴値を抽出する特徴値抽出手段と、
所定期間における前記一又は複数の特徴値に基づいて、前記所定期間における前記被験者の睡眠状態の判定、無呼吸状態若しくは低呼吸状態の判定又は酸素摂取状態の推定を行う状態判定手段を備え、
前記特徴値抽出手段は、少なくとも前記吸気波形の振幅値及び前記呼気波形の振幅値又は少なくとも前記吸気波形から呼気波形の時間間隔及び前記呼気波形から吸気波形の時間間隔を抽出し、
前記状態判定手段は、前記吸気波形の振幅値及び前記呼気波形の振幅値が一定期間毎に設定した閾値を超えた部分の時間幅の総和が前記一定期間に占める割合の変化、一定期間毎の前記吸気波形から呼気波形の時間間隔又は前記呼気波形から吸気波形の時間間隔が閾値を超えた時間間隔の抽出回数若しくは合計値に基づいて、前記被験者の無呼吸状態若しくは低呼吸状態の判定又は酸素摂取状態の推定を行う
ことを特徴とする寝息呼吸音解析装置。 A sleep breathing sound detection means for detecting the sleep breathing sound of the subject;
Respiration feature waveform acquisition means for obtaining a respiration feature waveform based on the sleep signal detected by the sleep breathing sound detection means;
Breathing waveform discriminating means for discriminating an inspiratory waveform and an expiratory waveform in the respiratory characteristic waveform;
Inhalation waveform time interval, expiration waveform time interval, inspiration waveform to expiration waveform time interval, expiration waveform to inspiration waveform time interval, inspiration waveform time width, expiration waveform time width, inspiration waveform time interval Feature value extraction means for extracting one or more feature values selected from the amplitude value and the amplitude value of the expiration waveform;
Based on the one or a plurality of feature values in a predetermined period, the state determination means for determining the sleep state of the subject in the predetermined period, determining the apnea state or hypopnea state, or estimating the oxygen intake state ,
The feature value extraction means extracts at least the amplitude value of the inspiratory waveform and the amplitude value of the expiratory waveform or at least the time interval of the expiratory waveform from the inspiratory waveform and the time interval of the inspiratory waveform from the expiratory waveform,
The state determination means includes a change in a ratio of a sum of time widths of portions where the amplitude value of the inspiratory waveform and the amplitude value of the expiratory waveform exceed a threshold set for each predetermined period in the predetermined period, Based on the number of extractions or the total value of the time interval from the inspiratory waveform to the expiratory waveform or the time interval in which the time interval from the expiratory waveform to the inspiratory waveform exceeds the threshold, the determination of the subject's apnea or hypopnea state or oxygen Estimate intake status
A sleep breathing sound analyzer characterized by the above .
前記状態判定手段は、前記所定期間における前記吸気波形の時間間隔又は前記呼気波形の時間間隔の変化度合に基づいて、前記被験者の睡眠状態がレム睡眠であるかノンレム睡眠であるかの判定を行う
ことを特徴とする請求項１に記載の寝息呼吸音解析装置。 The feature value extracting means extracts at least a time interval of the inspiration waveform or a time interval of the expiration waveform,
The state determination means determines whether the sleep state of the subject is REM sleep or non-REM sleep based on the degree of change of the time interval of the inspiration waveform or the time interval of the expiration waveform during the predetermined period. The sleep breathing sound analysis apparatus according to claim 1 .
前記状態判定手段は、前記所定期間における前記吸気波形の時間幅と前記呼気波形の時間幅の長さの関係又は前記吸気波形の振幅値と前記呼気波形の振幅値の大きさの関係に基づいて、前記被験者が低酸素状態か否かの判定を行う
ことを特徴とする請求項１又は２に記載の寝息呼吸音解析装置。 The feature value extracting means extracts at least a time width of the inspiratory waveform and a time width of the expiratory waveform or at least an amplitude value of the inspiratory waveform and an amplitude value of the expiratory waveform,
The state determination means is based on a relationship between a time width of the inspiration waveform and a time width of the expiration waveform or a relationship between the amplitude value of the inspiration waveform and the amplitude value of the expiration waveform in the predetermined period. , sleeper's breathing breathing sound analysis apparatus according to claim 1 or 2, characterized in that for determining said subject whether hypoxia.
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| WO2015194833A1 (en) | 2015-12-23 |
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