JPH07231880A - Stress evaluation method and device therefor - Google Patents
Stress evaluation method and device thereforInfo
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- JPH07231880A JPH07231880A JP6026584A JP2658494A JPH07231880A JP H07231880 A JPH07231880 A JP H07231880A JP 6026584 A JP6026584 A JP 6026584A JP 2658494 A JP2658494 A JP 2658494A JP H07231880 A JPH07231880 A JP H07231880A
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Abstract
Description
【０００１】[0001]
【産業上の利用分野】本発明は、心電図計測によって得
られるＲ−Ｒ間隔に基づいて、ストレスの度合いを評価
するストレス評価方法及び装置に関するものである。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a stress evaluation method and apparatus for evaluating the degree of stress based on the RR interval obtained by electrocardiogram measurement.
【０００２】[0002]
【従来の技術】心臓は交感神経と副交感神経の両方の自
律神経の支配を受けており、交感神経は心臓の活動を促
進し、副交感神経はこれを抑制する。この自律神経の活
動は更に、視床下部や大脳辺縁計や大脳皮質の制御を受
けているため、精神活動との関わりも深いことが明らか
にされている。BACKGROUND OF THE INVENTION The heart is under the control of both sympathetic and parasympathetic autonomic nerves. The sympathetic nerve promotes cardiac activity and the parasympathetic nerve suppresses it. Further, it has been clarified that this autonomic nerve activity is closely related to mental activity because it is controlled by the hypothalamus, limbic meter and cerebral cortex.
【０００３】そこで、図３の如く心電図計測によって得
られる心電図波形から、左右の心房全体が興奮する過程
で発生するＲ波の間隔、即ちＲ−Ｒ間隔の時系列データ
を検出し、該時系列データにＦＦＴ等のスペクトル分析
や統計解析を施して、人のストレスの度合いを定量的に
評価せんとする試みが行なわれている。Ｒ−Ｒ間隔はス
トレスと密接な関係があり、一般にストレス状態におい
てはＲ−Ｒ間隔が短くなると共に、Ｒ−Ｒ間隔の分散値
が小さくなる(ＴＶ学会技術報告,VV178-6(1986)等)。Therefore, as shown in FIG. 3, from the electrocardiogram waveform obtained by electrocardiogram measurement, time series data of R wave intervals generated in the process of exciting the entire left and right atrium, that is, RR intervals, is detected, and the time series data is detected. Attempts have been made to quantitatively evaluate the degree of human stress by subjecting data to spectral analysis such as FFT or statistical analysis. The R-R interval is closely related to stress. Generally, in a stressed state, the R-R interval becomes shorter and the variance of the R-R interval becomes smaller (TV Society Technical Report, VV178-6 (1986), etc. ).
【０００４】この様な技術背景の下、心電図計測によっ
て得られる時系列データに周期解析を施して、心周期の
変動性に基づいて、ストレスを評価する装置が提案され
ている(特開平4-54940〔A61B5/16〕)。Under such a technical background, there has been proposed an apparatus which evaluates stress based on the variability of the cardiac cycle by performing cycle analysis on time series data obtained by electrocardiogram measurement (Japanese Patent Laid-Open No. Hei 4- 54940 (A61B5 / 16)).
【０００５】[0005]
【発明が解決しようとする課題】ところで、心臓を支配
している前述の２つの自律神経は、体温、血圧、呼吸、
外界の環境等が変化することによって、その活動状態が
変化し、心臓の働きは複雑にゆらぐことになる。そし
て、この現象は、その背後に存在する非線形力学系に基
づいて発生しているものと推定される。By the way, the above-mentioned two autonomic nerves that control the heart are the body temperature, blood pressure, respiration,
As the external environment changes, the activity state changes, and the function of the heart fluctuates in a complicated manner. Then, it is presumed that this phenomenon occurs based on the nonlinear dynamical system existing behind it.
【０００６】しかしながら、従来のストレス評価に用い
られているスペクトル分析や統計解析は、線形力学系の
解析には有効であるが、非線形力学系の解析には不十分
であるため、従来のストレス評価方法では高い信頼性の
評価が困難な問題があった。この点については、雑誌
「自律神経」25巻3号(1988年)第334頁乃至第342頁に
も、Ｒ−Ｒ間隔の分散値を平均値で除したＣＶ−ＲＲの
値によっては副交感神経機能の変化を適確に評価出来な
い旨の研究報告が為されており、Ｒ−Ｒ間隔の平均値や
分散値のみによる評価の問題点が指摘されている。However, the spectral analysis and the statistical analysis used for the conventional stress evaluation are effective for the analysis of the linear dynamical system, but are insufficient for the analysis of the non-linear dynamical system. The method had a problem that it was difficult to evaluate high reliability. Regarding this point, in the magazine "Autonomic nerve" Vol. 25, No. 3 (1988), pages 334 to 342, depending on the value of CV-RR obtained by dividing the variance value of the RR interval by the average value, the parasympathetic nerve may be used. A research report has been made to the effect that the change in function cannot be accurately evaluated, and it has been pointed out that there are problems in evaluation based only on the average value or variance value of the RR interval.
【０００７】そこで、本発明の目的は、非線形なゆらぎ
の解析に有効とされているカオス解析を導入することに
よって、信頼性の高いストレス評価を実現することであ
る。Therefore, an object of the present invention is to realize a highly reliable stress evaluation by introducing a chaos analysis which is considered effective for the analysis of nonlinear fluctuations.
【０００８】[0008]
【課題を解決する為の手段】本発明に係るストレス評価
方法は、特定人の心電図データに基づいてＲ−Ｒ間隔を
検出するＲ−Ｒ間隔検出ステップと、これによって得ら
れたＲ−Ｒ間隔の時系列データに統計処理を施して、少
なくともＲ−Ｒ間隔の平均値或いは分散値を含む統計解
析パラメータを算出すると共に、Ｒ−Ｒ間隔の時系列デ
ータにカオス解析を施して、少なくとも最大リアプノフ
指数を含むカオス解析パラメータを算出する解析ステッ
プと、これによって算出された統計解析パラメータ及び
カオス解析パラメータの内、最大リアプノフ指数を含む
複数種類のパラメータに基づいて、１或いは複数種類の
ストレス評価データを算出する評価ステップと、これに
よって得られたストレス評価データを表示する表示ステ
ップとを有している。A stress evaluation method according to the present invention comprises an RR interval detecting step of detecting an RR interval based on electrocardiogram data of a specific person, and an RR interval obtained thereby. Is subjected to statistical processing to calculate statistical analysis parameters including at least an average value or a variance value of the R-R intervals, and chaotic analysis is performed on the time-series data of the R-R intervals to obtain at least the maximum Lyapunov. An analysis step of calculating a chaos analysis parameter including an index, and one or more kinds of stress evaluation data based on a plurality of kinds of parameters including the maximum Lyapunov index among the statistical analysis parameters and the chaos analysis parameters calculated by the analysis step. It has an evaluation step to calculate and a display step to display the stress evaluation data obtained by this. .
【０００９】又本発明に係るストレス評価装置は、特定
人の心電図計測を行なう心電図計測手段と、該手段から
得られる心電図データに基づいてＲ−Ｒ間隔を検出する
Ｒ−Ｒ間隔検出手段と、該手段から得られるＲ−Ｒ間隔
の時系列データに統計処理を施して、少なくともＲ−Ｒ
間隔の平均値或いは分散値を含む統計解析パラメータを
算出すると共に、Ｒ−Ｒ間隔の時系列データにカオス解
析を施して、少なくとも最大リアプノフ指数を含むカオ
ス解析パラメータを算出するデータ解析手段と、該手段
によって算出された統計解析パラメータ及びカオス解析
パラメータの内、最大リアプノフ指数を含む複数種類の
パラメータに基づいて、１或いは複数種類のストレス評
価データを算出する評価手段と、該手段によって得られ
たストレス評価データを表示する表示手段とを具えてい
る。Further, the stress evaluation apparatus according to the present invention comprises an electrocardiogram measuring means for measuring an electrocardiogram of a specific person, and an RR interval detecting means for detecting an RR interval based on electrocardiographic data obtained from the means. At least RR is performed by statistically processing the time series data of RR intervals obtained from the means.
A data analysis means for calculating a statistical analysis parameter including an average value or a variance value of the intervals and performing a chaos analysis on the time series data of the RR interval to calculate a chaos analysis parameter including at least the maximum Lyapunov exponent; Evaluation means for calculating one or more kinds of stress evaluation data based on a plurality of kinds of parameters including the maximum Lyapunov index among the statistical analysis parameters and the chaotic analysis parameters calculated by the means, and the stress obtained by the means Display means for displaying the evaluation data.
【００１０】Ｒ−Ｒ間隔の統計解析パラメータとして
は、平均値及び分散値の他、低周波数のパワー値、高周
波数のパワー値等が採用出来る。又、カオス解析パラメ
ータとしては、最大リアプノフ指数の他、相関次元数、
ＫＳエントロピー、リアプノフ次元などが採用出来る。As the statistical analysis parameter of the RR interval, a low frequency power value, a high frequency power value, etc. can be adopted in addition to the average value and the variance value. The chaotic analysis parameters include the maximum Lyapunov exponent, the number of correlation dimensions,
KS entropy, Lyapunov dimension, etc. can be adopted.
【００１１】ストレス評価方法及び装置の具体的構成に
おいては、データ解析は、Ｒ−Ｒ間隔の平均値、分散値
及び最大リアプノフ指数を算出するものであり、ストレ
ス評価は、データ解析によって得られるこれら３種類の
パラメータに多変量解析を施して、ストレス評価データ
を算出するものである。In a specific configuration of the stress evaluation method and apparatus, the data analysis is to calculate the average value, variance value and maximum Lyapunov index of the RR interval, and the stress evaluation is obtained by data analysis. The stress evaluation data is calculated by performing multivariate analysis on the three types of parameters.
【００１２】[0012]
【作用】上記ストレス評価方法及び装置においては、Ｒ
−Ｒ間隔の平均値、分散値等の統計解析パラメータと、
最大リアプノフ指数等を含むカオス解析パラメータとが
組み合わされて、評価データが算出される。ここで、Ｒ
−Ｒ間隔の平均値及び分散値は共に、リラックス状態で
は大きく、ストレス状態では小さくなる傾向を示すが、
測定時の体温、血圧、呼吸、外界の環境等が変化するこ
とによって、Ｒ−Ｒ間隔が不安定となり、必ずしもこの
大小関係が維持されるとは限らない。一方、最大リアプ
ノフ指数は、リラックス状態では大きな値、ストレス状
態では小さな値を示し、Ｒ−Ｒ間隔の不安定性を適確に
表わす。従って、上記ストレス評価方法及び装置によれ
ば、統計解析パラメータとカオス解析パラメータとが相
互に補完して、ストレスの度合いに応じた適切な評価デ
ータが得られるのである。In the above stress evaluation method and apparatus, R
-Statistical analysis parameters such as average value and variance value of R interval,
Evaluation data is calculated by combining with chaotic analysis parameters including the maximum Lyapunov exponent. Where R
Both the mean value and the variance value of the −R interval tend to be large in the relaxed state and small in the stressed state,
The RR interval becomes unstable due to changes in body temperature, blood pressure, respiration, external environment, etc. at the time of measurement, and this magnitude relationship is not always maintained. On the other hand, the maximum Lyapunov exponent shows a large value in a relaxed state and a small value in a stressed state, and accurately represents the instability of the RR interval. Therefore, according to the above stress evaluation method and apparatus, the statistical analysis parameter and the chaotic analysis parameter complement each other to obtain appropriate evaluation data according to the degree of stress.
【００１３】[0013]
【発明の効果】本発明に係るストレス評価方法及び装置
によれば、信頼性の高いストレス評価が実現される。According to the stress evaluation method and apparatus of the present invention, highly reliable stress evaluation can be realized.
【００１４】[0014]
【実施例】以下、本発明の一実施例につき、図面に沿っ
て詳述する。図１に示す如く、本発明のストレス評価装
置は、心電図計測を行なう心電図計測器(１)、ストレス
評価回路(２)、及びディスプレイなどの表示装置(６)を
具え、ストレス評価回路(２)を構成するＲ−Ｒ間隔検出
回路(３)は、心電図波形からＲ−Ｒ間隔を検出する。デ
ータ解析回路(４)は、Ｒ−Ｒ間隔検出回路(３)から得ら
れるＲ−Ｒ間隔の時系列データに統計処理を施して、Ｒ
−Ｒ間隔の平均値、分散値、低周波数のパワー値、及び
高周波数のパワー値を算出すると共に、Ｒ−Ｒ間隔の時
系列データにカオスリアプノフスペクトラム解析及び相
関次元解析(「カオス入門」培風館発行、長島、馬場共
著、第９９頁乃至第１１０頁参照)を施して、最大リア
プノフ指数、相関次元数、ＫＳエントロピー、及びリア
プノフ次元を算出するものである。又、多変量解析回路
(５)は、データ解析回路(４)によって算出されたＲ−Ｒ
間隔の平均値などのパラメータに多変量解析を施して、
ストレス度、リラックス度、緊張度、集中度、疲労度、
覚醒度、快適度などの感覚量や、交感神経及び副交感神
経の活動度を算出するものである。DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described in detail below with reference to the drawings. As shown in FIG. 1, the stress evaluation device of the present invention comprises an electrocardiogram measuring device (1) for performing electrocardiogram measurement, a stress evaluation circuit (2), and a display device (6) such as a display, and the stress evaluation circuit (2). The R-R interval detection circuit (3) constituting the circuit detects the R-R interval from the electrocardiogram waveform. The data analysis circuit (4) statistically processes the R-R interval time series data obtained from the R-R interval detection circuit (3),
-Calculate the average value, variance value, low frequency power value, and high frequency power value of the R interval, and use the chaos Lyapunov spectrum analysis and the correlation dimension analysis ("Chaos Primer" Baifukan) in the time series data of the RR interval. Issued, co-authored by Nagashima and Baba, pp. 99 to 110) to calculate maximum Lyapunov exponent, correlation dimension number, KS entropy, and Lyapunov dimension. Also, a multivariate analysis circuit
(5) is RR calculated by the data analysis circuit (4)
By performing multivariate analysis on parameters such as the average value of intervals,
Stress level, relaxation level, tension level, concentration level, fatigue level,
It calculates sensory quantities such as arousal level and comfort level, and activity levels of sympathetic nerve and parasympathetic nerve.
【００１５】尚、Ｒ−Ｒ間隔の低周波数のパワー値及び
高周波数のパワー値は、図４に示す様に、Ｒ−Ｒ間隔の
時系列データにＦＦＴによる周波数分析を施して得られ
る低周波数領域のピークレベルと高周波数領域のピーク
レべルである。高周波数のパワー値は副交感神経の活動
を反映し、低周波数のパワー値は副交感神経と交感神経
の両方の活動を反映するものであって、ストレス状態で
は交感神経が活性化することにより、低周波数のパワー
値が増大する傾向にある。The low-frequency power value and the high-frequency power value in the RR interval are low-frequency power values obtained by performing frequency analysis by FFT on time series data in the RR interval, as shown in FIG. It is the peak level of the region and the peak level of the high frequency region. High frequency power values reflect parasympathetic activity, and low frequency power values reflect both parasympathetic and sympathetic nerve activity. The power value of the frequency tends to increase.
【００１６】図２は、ストレス評価回路(２)によって実
行される評価手続きを表わしており、先ずステップＳ１
にて、心電図計測器(１)によって心電図計測が行なわ
れ、ステップＳ２にてＲ−Ｒ間隔検出回路(３)によるＲ
−Ｒ間隔の検出が行なわれる。次にステップＳ３では、
データ解析回路(４)によって、Ｒ−Ｒ間隔の平均値、分
散値、最大リアプノフ指数などが算出された後、ステッ
プＳ４にて、多変量解析回路(５)によってストレス度な
どのストレス評価データが算出される。算出された評価
値は表示装置(６)に数値データとして表示される。FIG. 2 shows an evaluation procedure executed by the stress evaluation circuit (2). First, step S1.
At, the electrocardiogram is measured by the electrocardiogram measuring device (1), and at step S2, the R-R interval detection circuit (3) performs R
-R intervals are detected. Next, in step S3,
After the average value, variance value, maximum Lyapunov exponent, etc. of the RR interval are calculated by the data analysis circuit (4), the stress evaluation data such as the stress level is calculated by the multivariate analysis circuit (5) in step S4. It is calculated. The calculated evaluation value is displayed as numerical data on the display device (6).
【００１７】多変量解析回路(５)によるストレス評価デ
ータの算出に際しては、下記数１に示す重回帰式に基づ
く多変量解析が行なわれる。ここで、ｘ1、ｘ2、…ｘn
は、Ｒ−Ｒ間隔の平均値、分散値、低周波数のパワー
値、高周波数のパワー値、カオス最大リアプノフ指数、
カオス相関次元数、ＫＳエントロピー、リアプノフ次元
などのパラメータである。又、ｙ1、ｙ2、…ｙmは、ス
トレス度、リラックス度、緊張度、集中度、疲労度、覚
醒度、快適度、交感神経及び副交感神経の活動度などの
複数の評価データである。When the stress evaluation data is calculated by the multivariate analysis circuit (5), the multivariate analysis based on the multiple regression equation shown in the following equation 1 is performed. Where x 1 , x 2 , ... x n
Is the mean value, variance value, low frequency power value, high frequency power value, chaos maximum Lyapunov exponent,
Parameters such as the number of chaos correlation dimensions, KS entropy, and Lyapunov dimension. Further, y 1 , y 2 , ... Y m are a plurality of evaluation data such as stress level, relaxation level, tension level, concentration level, fatigue level, arousal level, comfort level, sympathetic nerve and parasympathetic nerve activity level. .
【００１８】[0018]
【数１】 ｙ1＝ａ1＋ｂ1ｘ1＋……＋ｚ1ｘn ｙ2＝ａ2＋ｂ2ｘ1＋……＋ｚ2ｘn ｙ3＝ａ3＋ｂ3ｘ1＋……＋ｚ3ｘn ・ ・ ｙm＝ａm＋ｂmｘ1＋……＋ｚmｘn Y 1 = a 1 + b 1 x 1 + ... + z 1 xn y 2 = a 2 + b 2 x 1 + ... + z 2 xn y 3 = a 3 + b 3 x 1 + ... + z 3 x n · · y m = a m + b m x 1 + ...... + z m x n
【００１９】下記表１及び表２は、健康な男性４名をリ
ラックス状態とストレス状態において、１時間半にわた
る心電図計測を行ない、これによって得られた心電図波
形からＲ−Ｒ間隔を検出して、本発明のストレス評価を
行なったものである。ここで、リラックス状態は、仰臥
位で快適な音楽を流すことによって実現し、ストレス状
態は、座位でＴＶゲームを行なうことによって実現し
た。Tables 1 and 2 below show that four healthy males were subjected to electrocardiographic measurement for one and a half hours in relaxed and stressed states, and the RR interval was detected from the electrocardiographic waveform thus obtained, The stress evaluation of the present invention was performed. Here, the relaxed state was realized by playing comfortable music in the supine position, and the stressed state was realized by playing a TV game in the sitting position.
【００２０】[0020]
【表１】 [Table 1]
【００２１】[0021]
【表２】 [Table 2]
【００２２】本実施例では、下記数２の如くストレス度
ｙを推定する重回帰式と、下記数３の如くリラックス度
ｙ′を推定する重回帰式を作成し、上記の表１及び表２
のデータに基づいて、これらの重回帰式の係数を算出し
た。In this embodiment, a multiple regression equation for estimating the stress level y as shown in the following equation 2 and a multiple regression equation for estimating the relaxation degree y'as shown in the following equation 3 are prepared, and the above table 1 and table 2 are prepared.
The coefficients of these multiple regression equations were calculated based on the data of.
【００２３】[0023]
【数２】ｙ＝ａ＋ｂｘ1＋ｃｘ2＋ｄｘ3 ここで、ａ＝１２８.１ ｂ＝−０.０１５１９１ ｃ＝−０.２０８１４２ ｄ＝−９４８６.８０７Y = a + bx 1 + cx 2 + dx 3 where a = 128.1 b = −0.015191 c = −0.208142 d = −9486.807
【数３】ｙ′＝ａ′＋ｂ′ｘ1＋ｃ′ｘ2＋ｄ′ｘ3 ここで、ａ′＝−４６.９５２９ ｂ′＝０.０３０１７１ ｃ′＝０.２７７０６１ ｄ′＝９５０２.１３４９## EQU3 ## y '= a' + b'x 1 + c'x 2 + d'x 3 where a '=-46.9529 b' = 0.030171 c '= 0.2777061 d' = 95021349
【００２４】本実施例では更に、上記４名の被験者にア
ンケート調査を行なって、下記表３及び表４に示す如
く、自己申告によるストレス度ｙとリラックス度ｙ′
(夫々０以上、１００以下の値)を得た。In the present example, further, a questionnaire survey was conducted on the above four subjects, and as shown in Tables 3 and 4 below, the self-reported stress level y and relaxation level y '
(Values of 0 or more and 100 or less, respectively) were obtained.
【００２５】[0025]
【表３】 [Table 3]
【００２６】[0026]
【表４】 [Table 4]
【００２７】そして、上記数１及び数２に基づくストレ
ス度ｙ及びリラックス度ｙの計算値と自己申告値の重相
関係数Ｒを算出したところ、ストレス度についてはＲ＝
０.９９４４の値が得られ、リラックス度についてはＲ
＝０.９８７の値が得られた。 一般に、重相関係数Ｒ
が０.８を越えたとき、その重回帰式は精度が十分に高
いとされることから、Ｒ−Ｒ間隔の平均値ｘ1、分散ｘ2
及び最大リアプノフ指数ｘ3の３つのパラメータに基づ
く本発明のストレス度及びリラックス度の算出方式は有
意義なものであると言える。Then, the multiple correlation coefficient R between the calculated values of the stress level y and the relaxation level y and the self-reported value based on the above equations 1 and 2 was calculated.
A value of 0.9944 is obtained, and the degree of relaxation is R
A value of = 0.987 was obtained. Generally, the multiple correlation coefficient R
When the value exceeds 0.8, the accuracy of the multiple regression equation is considered to be sufficiently high, so the average value x 1 of the RR interval and the variance x 2
It can be said that the calculation method of the stress level and the relaxation level of the present invention based on the three parameters of the maximum Lyapunov index x 3 and the maximum Lyapunov index x 3 is significant.
【００２８】又、平均値ｘ1、分散ｘ2及び最大リアプノ
フ指数ｘ3が夫々、ストレス度ｙ及びリラックス度ｙ′
に及ぼす影響度を表わす偏相関係数Ｒ1y、Ｒ2y、Ｒ3yを
算出したところ、下記の結果が得られた。The mean value x 1 , the variance x 2 and the maximum Lyapunov index x 3 are the stress level y and the relaxation level y ', respectively.
The partial correlation coefficients R 1y , R 2y , and R 3y , which represent the degree of influence on, were calculated, and the following results were obtained.
【００２９】ストレス度について Ｒ1y＝−０.４５８５１ Ｒ2y＝−０.５６９８９ Ｒ3y＝−０.９９０４９７リラックス度について Ｒ1y＝０.５１５５９ Ｒ2y＝０.５０７２ Ｒ3y＝０.９７５８ For stress level R 1y = -0.45851 R 2y = -0.56989 R 3y = -0.9990497 For relax level R 1y = 0.51559 R 2y = 0.5072 R 3y = 0.9758
【００３０】ストレス度、リラックス度のいずれについ
ても、最大リアプノフ指数ｘ3に関する偏相関係数Ｒ3y
が、平均値ｘ1や分散ｘ2に関する偏相関係数Ｒ1y、Ｒ2y
に比べて非常に大きくなっており、最大リアプノフ指数
ｘ3の影響度が大きいことが明らかである。The partial correlation coefficient R 3y related to the maximum Lyapunov exponent x 3 for both stress and relaxation
Are partial correlation coefficients R 1y and R 2y with respect to the mean value x 1 and the variance x 2.
The maximum Lyapunov exponent x 3 has a great influence.
【００３１】但し、平均値ｘ1や分散ｘ2に関する偏相関
係数Ｒ1y、Ｒ2yも約０.５と無視出来ない大きさとなっ
ていることから、最大リアプノフ指数のみによる評価で
は不十分と言える。However, since the partial correlation coefficients R 1y and R 2y related to the average value x 1 and the variance x 2 are about 0.5, which cannot be ignored, evaluation using only the maximum Lyapunov index is not sufficient. I can say.
【００３２】上述の如く本発明のストレス評価方法によ
れば、Ｒ−Ｒ間隔の平均値や分散などの統計解析パラメ
ータと、最大リアプノフ指数などのカオス解析パラメー
タとが相互に補完して、ストレスの度合いに応じた適切
な評価データが得られる。As described above, according to the stress evaluation method of the present invention, the statistical analysis parameters such as the average value and variance of the RR interval and the chaotic analysis parameters such as the maximum Lyapunov exponent complement each other, and the stress Appropriate evaluation data can be obtained according to the degree.
【００３３】上記実施例の説明は、本発明を説明するた
めのものであって、特許請求の範囲に記載の発明を限定
し、或は範囲を減縮する様に解すべきではない。又、本
発明の各部構成は上記実施例に限らず、特許請求の範囲
に記載の技術的範囲内で種々の変形が可能であることは
勿論である。The above description of the embodiments is for explaining the present invention, and should not be construed as limiting the invention described in the claims or limiting the scope. The configuration of each part of the present invention is not limited to the above-mentioned embodiment, and it goes without saying that various modifications can be made within the technical scope described in the claims.
【００３４】例えば上記実施例では、ストレス評価デー
タの算出に多変量解析を用いたが、これに限らず、ニュ
ーラルネットワークなどの手法を採用することも可能で
ある。又、統計解析パラメータとカオス解析パラメータ
の組合せは、Ｒ−Ｒ間隔の平均値と最大リアプノフ指数
や、Ｒ−Ｒ間隔の分散と最大リアプノフ指数など、種々
の形態が採用可能である。For example, in the above embodiment, the multivariate analysis was used to calculate the stress evaluation data, but the present invention is not limited to this, and a method such as a neural network may be adopted. Further, the combination of the statistical analysis parameter and the chaotic analysis parameter can adopt various forms such as the average value of the RR interval and the maximum Lyapunov index, the variance of the RR interval and the maximum Lyapunov index, and the like.
【図１】ストレス評価装置の構成を示すブロック図であ
る。FIG. 1 is a block diagram showing a configuration of a stress evaluation device.
【図２】ストレス評価方法の手順を示すフローチャート
である。FIG. 2 is a flowchart showing a procedure of a stress evaluation method.
【図３】心電図波形を示す図である。FIG. 3 is a diagram showing an electrocardiogram waveform.
【図４】Ｒ−Ｒ間隔の周波数分析の結果を示すグラフで
ある。FIG. 4 is a graph showing the results of frequency analysis of RR intervals.
(１) 心電図計測器 (２) ストレス評価回路 (３) Ｒ−Ｒ間隔検出回路 (４) データ解析回路 (５) 多変量解析回路 (６) 表示装置 (1) ECG measuring instrument (2) Stress evaluation circuit (3) RR interval detection circuit (4) Data analysis circuit (5) Multivariate analysis circuit (6) Display device
Claims (4)
出するＲ−Ｒ間隔検出ステップと、これによって得られ
たＲ−Ｒ間隔の時系列データに統計処理を施して、少な
くともＲ−Ｒ間隔の平均値或いは分散値を含む統計解析
パラメータを算出すると共に、Ｒ−Ｒ間隔の時系列デー
タにカオス解析を施して、少なくとも最大リアプノフ指
数を含むカオス解析パラメータを算出する解析ステップ
と、これによって算出された統計解析パラメータ及びカ
オス解析パラメータの内、最大リアプノフ指数を含む複
数種類のパラメータに基づいて、１或いは複数種類のス
トレス評価データを算出する評価ステップと、これによ
って得られたストレス評価データを表示する表示ステッ
プとを有するストレス評価方法。1. An RR interval detecting step of detecting an RR interval based on electrocardiogram data, and statistically processing the time series data of the RR interval obtained thereby, and at least the RR interval. And a statistical analysis parameter including an average value or a variance value of the same, and a chaotic analysis on the time series data of the RR interval to calculate a chaotic analysis parameter including at least the maximum Lyapunov exponent; An evaluation step of calculating one or more kinds of stress evaluation data based on a plurality of kinds of parameters including the maximum Lyapunov index among the statistical analysis parameters and the chaotic analysis parameters, and the stress evaluation data thus obtained are displayed. And a stress evaluation method having a display step.
値、分散値及び最大リアプノフ指数を算出し、評価ステ
ップでは、これら３種類のパラメータに多変量解析を施
して、ストレス評価データを算出する請求項１に記載の
ストレス評価方法。2. The analysis step calculates the average value, the variance value, and the maximum Lyapunov exponent of the RR interval, and the evaluation step performs a multivariate analysis on these three types of parameters to calculate stress evaluation data. The stress evaluation method according to claim 1.
該手段から得られる心電図データに基づいてＲ−Ｒ間隔
を検出するＲ−Ｒ間隔検出手段と、該手段から得られる
Ｒ−Ｒ間隔の時系列データに統計処理を施して、少なく
ともＲ−Ｒ間隔の平均値或いは分散値を含む統計解析パ
ラメータを算出すると共に、Ｒ−Ｒ間隔の時系列データ
にカオス解析を施して、少なくとも最大リアプノフ指数
を含むカオス解析パラメータを算出するデータ解析手段
と、該手段によって算出された統計解析パラメータ及び
カオス解析パラメータの内、最大リアプノフ指数を含む
複数種類のパラメータに基づいて、１或いは複数種類の
ストレス評価データを算出する評価手段と、該手段によ
って得られたストレス評価データを表示する表示手段と
を具えたストレス評価装置。3. An electrocardiogram measuring means for performing electrocardiogram measurement,
RR interval detecting means for detecting the RR interval based on electrocardiogram data obtained from said means, and at least RR interval by statistically processing the time series data of the RR interval obtained from said means And a data analysis means for calculating a chaos analysis parameter including at least a maximum Lyapunov exponent, while calculating a statistical analysis parameter including an average value or a variance value of the above and performing chaos analysis on the time series data of the RR interval. Of the statistical analysis parameters and the chaotic analysis parameters calculated by the above, based on a plurality of types of parameters including the maximum Lyapunov exponent, an evaluation means for calculating one or a plurality of types of stress evaluation data, and a stress evaluation obtained by the means. A stress evaluation device comprising display means for displaying data.
値、分散値及び最大リアプノフ指数を算出するデータ解
析回路(４)から構成され、評価手段は、データ解析回路
から得られる３種類のパラメータに多変量解析を施し
て、ストレス評価データを算出する多変量解析回路(５)
から構成される請求項３に記載のストレス評価装置。4. The data analysis means comprises a data analysis circuit (4) for calculating an average value, a variance value and a maximum Lyapunov exponent of the RR interval, and the evaluation means comprises three types obtained from the data analysis circuit. Multivariate analysis circuit that calculates stress evaluation data by performing multivariate analysis on parameters (5)
The stress evaluation device according to claim 3, which comprises
Priority Applications (1)
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|---|---|---|---|
| JP6026584A JPH07231880A (en) | 1994-02-24 | 1994-02-24 | Stress evaluation method and device therefor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP6026584A JPH07231880A (en) | 1994-02-24 | 1994-02-24 | Stress evaluation method and device therefor |
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| Publication Number | Publication Date |
|---|---|
| JPH07231880A true JPH07231880A (en) | 1995-09-05 |
Family
ID=12197602
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP6026584A Pending JPH07231880A (en) | 1994-02-24 | 1994-02-24 | Stress evaluation method and device therefor |
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| Country | Link |
|---|---|
| JP (1) | JPH07231880A (en) |
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