JP2020146140A - Heart abnormality detection method and detection device - Google Patents
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Abstract
Description
近年、健康志向や精神衛生に対する意識の高まりからヘルスケアや、メンタルケア、遠隔医療といった医療・福祉関連サービスが注目されている。こうしたサービスの拡充には、心身の状態を反映する脈波や心拍、血圧といった生体信号を、身近に活用できる環境の整備が不可欠であろう。 In recent years, medical and welfare-related services such as health care, mental care, and remote medical care have been attracting attention due to heightened awareness of health consciousness and mental health. In order to expand such services, it will be essential to create an environment in which biological signals such as pulse waves, heartbeats, and blood pressure that reflect the physical and mental conditions can be used in our daily lives.
心臓は、全身に酸素を含んだ血液を送るポンプの働きをする臓器である。心臓の中はいくつかの部屋に分かれており、そこを血液が1方向に流れる。血液の流れを制御するために弁構造があり、心臓の周りには心臓自身を栄養する血管（冠状動脈）が走っている。心臓は特殊な筋肉でできており、刺激伝導系という組織が、脈拍を調節している。心臓病とはこれらの心臓の働きがなんらかの原因で悪化したものであり、先天的、遺伝的な原因などにより起こることが多いものであるが、大人になってから起こるものもある。 The heart is an organ that acts as a pump that pumps oxygenated blood throughout the body. The heart is divided into several chambers through which blood flows in one direction. There is a valve structure to control the flow of blood, and blood vessels (coronary arteries) that feed the heart itself run around the heart. The heart is made up of special muscles, and a tissue called the conduction system regulates the pulse. Heart disease is a deterioration of these heart functions for some reason, and is often caused by congenital or genetic causes, but some of them occur after adulthood.
心臓を収縮させる刺激が一定でない場合をまとめて不整脈といい、脈が時々飛ぶタイプ、脈が極端に遅くなるタイプや、脈がばらばらになるタイプなどさまざまな種類の不整脈があるため、正確な診断が必要である。特に脈がばらばらになるタイプの不整脈は心房細動といい、この心房細動は、心房が細かく、速くかつ不規則的に動く頻発性の不整脈である。日本では70才以上高齢者の数％を占める130万人の患者のいることが知られており、高齢になるほど増加するため、心臓の筋肉の老化により生じると考えられている。心房細動で心拍が高い状態が続くと、心臓の収縮機能低下による心不全や、または血液が心房の中で固まりやすく血栓ができやすい状態となり、できた血栓により脳の血管が詰まってしまう脳梗塞の原因となることも多く、注意が必要である。また、心房細動の不整脈により、心臓の働きが弱る心不全の原因にもなりやすい。さらに、心房細動は、高血圧症や弁膜症、心臓肥大などの特定な原因疾患がなくても生じると言われており、精神的・肉体的ストレス、不眠、不安なども誘発原因になっているため、予防のためには日々のチェックが重要になっている。 The case where the stimulus that contracts the heart is not constant is collectively called arrhythmia, and there are various types of arrhythmia such as the type in which the pulse sometimes flies, the type in which the pulse becomes extremely slow, and the type in which the pulse becomes disjointed. is necessary. In particular, the type of arrhythmia in which the pulse is separated is called atrial fibrillation, and this atrial fibrillation is a frequent arrhythmia in which the atrium moves finely, quickly and irregularly. It is known that there are 1.3 million patients in Japan, who account for a few percent of the elderly aged 70 and over, and the number increases with age, which is thought to be caused by aging of the heart muscle. If the heartbeat continues to be high due to atrial fibrillation, heart failure due to a decrease in the contractile function of the heart, or a state in which blood tends to clot in the atrium and blood clots are likely to form, and the blood vessels in the brain are clogged by the blood clots. It is often the cause of this, so caution is required. In addition, arrhythmia of atrial fibrillation tends to cause heart failure, which weakens the function of the heart. Furthermore, atrial fibrillation is said to occur without specific causative diseases such as hypertension, valvular disease, and cardiac hypertrophy, and mental and physical stress, insomnia, and anxiety are also triggering causes. Therefore, daily checks are important for prevention.
心臓疾患に関する医療分野において、心房細動を判定する技術がある。例えば、特許文献１には、信号取得部により取得された検出波形信号において、体動ノイズの影響が含まれていても、その信号から心房細動を判定する、心房細動判定装置を開示している。そして、特許文献２には、脳血管疾患発症の危険度を予測することのできる発症危険度予測システムを開示している。 In the medical field related to heart disease, there is a technique for determining atrial fibrillation. For example, Patent Document 1 discloses an atrial fibrillation determination device that determines atrial fibrillation from a detection waveform signal acquired by a signal acquisition unit even if it includes the influence of body motion noise. ing. Further, Patent Document 2 discloses an onset risk prediction system capable of predicting the risk of developing a cerebrovascular disease.
以上述べた従来の脈波信号による心臓状態の監視システムにおいては、内部構成部品に複雑で精密な部品からなる高価な装置であった。
本発明は、このような従来の構成が有していた問題を解決しようとするものであり、心臓の異常状態を簡便に、検出可能な装置を提供することを目的とする。
In the conventional heart condition monitoring system using the pulse wave signal described above, it is an expensive device consisting of complicated and precise internal components.
The present invention is intended to solve the problem of such a conventional configuration, and an object of the present invention is to provide a device capable of easily detecting an abnormal state of the heart.
そこで、発明者は、通常の血圧測定に利用する脈圧波のデータを収集して、これら脈圧波データの周波数分析により得られた周波数スペクトルの振幅変化データの統計解析を行い（図１（ｂ）参照）、心房細動などの心臓の異常状態を簡便に検出できる特徴量を導いた。 Therefore, the inventor collects pulse pressure wave data used for normal blood pressure measurement, and statistically analyzes the frequency spectrum amplitude change data obtained by frequency analysis of these pulse pressure wave data (FIG. 1 (b)). (See), and derived feature quantities that can easily detect abnormal conditions of the heart such as atrial fibrillation.
そして、通常の血圧測定に利用する脈圧波のデータを用いて、心臓の何らかの異常により変化する脈圧波形のかたちの違いに着目し、波形が平均値を中心に如何にばらついているか、波形が平均値を中心に如何に歪んでいるか、さらに、波形が如何に尖っているかを示す無次元統計量である波形率（Shape factor）、歪み度(Skewness)及び尖り度(Kurtosis)を用いて異常特徴量の検討を行った。 Then, using the pulse pressure wave data used for normal blood pressure measurement, paying attention to the difference in the shape of the pulse pressure waveform that changes due to some abnormality in the heart, how the waveform varies around the average value, the waveform is Abnormality using the waveform factor (Shape factor), skewness (Skewness), and kurtosis, which are dimensional statistics showing how the waveform is distorted around the average value and how sharp the waveform is. The feature quantity was examined.
その結果、心臓の脈圧波形を高速フーリエ変換などの信号処理とその周波数スペクトルの振幅波形データの統計解析を行い、導出した異常特徴量により心筋梗塞や脳卒中の主な原因となる心房細動などの心臓の異常状態を簡便に検出する方法を発明するに至ったのである。 As a result, signal processing such as fast Fourier transform of the pulse pressure waveform of the heart and statistical analysis of the amplitude waveform data of the frequency spectrum are performed, and atrial fibrillation, which is the main cause of myocardial infarction and stroke, is performed based on the derived abnormal features. He has invented a method for easily detecting an abnormal state of the heart.
本発明において、上記目的を達成するための第１の発明は、血圧測定に利用する脈圧波を用いて、心臓の何らかの異常により変化する脈圧波形のかたちの違いに着目し、その脈圧波形を高速フーリエ変換などの信号処理とその周波数スペクトルの振幅波形データの統計解析を行い、導出した異常特徴量である波形率、歪み度及び尖り度により、心筋梗塞や脳卒中の主な原因となる心房細動などの心臓の異常状態を検出する方法である。
また本発明の第２の発明は、血圧測定部（圧力式脈圧波測定器）により取得した脈圧波形データをデータ処理用マイコンにより、分析し表示装置に表示する一連の動作に基づく、所定のアルゴリズムで心房細動などの心臓疾患の有無を判断する前記請求項１に記載する心臓の異常状態を検出する方法を用いた心臓異常検出装置である。
In the present invention, the first invention for achieving the above object focuses on the difference in the shape of the pulse pressure waveform that changes due to some abnormality in the heart by using the pulse pressure wave used for blood pressure measurement, and the pulse pressure waveform. Signal processing such as high-speed Fourier transformation and statistical analysis of the amplitude waveform data of the frequency spectrum are performed, and the waveform rate, distortion degree, and sharpness, which are the derived abnormal feature quantities, are used to determine the atrial heart, which is the main cause of myocardial infarction and stroke. It is a method of detecting abnormal conditions of the heart such as fibrillation.
The second invention of the present invention is a predetermined invention based on a series of operations of analyzing pulse pressure waveform data acquired by a blood pressure measuring unit (pressure type pulse pressure wave measuring device) by a data processing microcomputer and displaying it on a display device. It is a cardiac abnormality detection device using the method for detecting an abnormal state of the heart according to claim 1, wherein the presence or absence of a heart disease such as atrial fibrillation is determined by an algorithm.
心臓の各症状における脈圧波の周波数分析により得られた周波数スペクトルの振幅変化データの統計解析を行い心房細動などの心臓の異常状態を簡便に検出できる特徴量を導き、特に、各症状における周波数スペクトルの振幅波形形態のかたちの違いに着目し、波形が平均値に対して如何にばらついているか、平均値を中心に如何に歪んでいるか、さらに波形が如何に尖っているかを示す無次元統計量である波形率、歪み度および尖り度を用いて、この三つの統計量の値は標準状態における結果値を基準にして正規化することにより、脈圧波の周波数スペクトルの振幅波形データの統計量である波形率、歪み度および尖り度を監視することで、心房細動や心室性期外収縮などの心臓異常が検出できる。 Statistical analysis of the amplitude change data of the frequency spectrum obtained by frequency analysis of the pulse pressure wave in each symptom of the heart is performed to derive a feature amount that can easily detect an abnormal state of the heart such as atrial fibrillation. In particular, the frequency in each symptom. Focusing on the difference in the shape of the amplitude waveform of the spectrum, non-dimensional statistics showing how the waveform varies with respect to the average value, how it is distorted around the average value, and how sharp the waveform is. The statistic of the amplitude waveform data of the frequency spectrum of the pulse pressure wave by normalizing the values of these three statistics with respect to the result value in the standard state using the quantities of waveform rate, distortion degree and sharpness. By monitoring the waveform rate, the degree of strain, and the degree of sharpness, cardiac abnormalities such as atrial fibrillation and ventricular extrasystole can be detected.
つぎに、本発明に係る心臓異常を検出する実施例について、図面を参照して具体的に説明する。 Next, an example of detecting a cardiac abnormality according to the present invention will be specifically described with reference to the drawings.
（１）心臓の脈圧波形データの時間領域と周波数領域の分析
不整脈の波形と周期の変化をシミュレーションできる装置（BP-PUMP:BIO-TEK Instruments製）と本発明者が製作した圧力式脈圧波測定器を用いて（表１）の4種類の脈圧波形データについて時間領域と周波数領域で分析を行った。
(1) Analysis of time domain and frequency domain of cardiac pulse pressure waveform data A device (BP-PUMP: manufactured by BIO-TEK Instruments) capable of simulating changes in arrhythmia waveform and period, and a pressure pulse pressure wave manufactured by the present inventor. Using a measuring instrument, the four types of pulse pressure waveform data (Table 1) were analyzed in the time domain and frequency domain.
脈圧波形データの分析には0.00512秒間隔でサンプリングした4096点数が使用された（サンプリング周波数は195.31Hz）。特に、波形分析の際には、測定機器や測定環境の違い、または利用者の個人差の影響をなくすために、前処理としてすべての測定データは平均値0、標準偏差1になるように標準化を行った。その後、4種類の脈圧波形データを用いて時間領域と周波数領域で信号処理を行い、各状態における分析結果を比較、検討した。 4096 points sampled at 0.00512 second intervals were used for the analysis of pulse pressure waveform data (sampling frequency 195.31 Hz). In particular, during waveform analysis, all measurement data are standardized so that the average value is 0 and the standard deviation is 1 as preprocessing in order to eliminate the influence of differences in measuring equipment and measurement environment, or individual differences of users. Was done. After that, signal processing was performed in the time domain and frequency domain using four types of pulse pressure waveform data, and the analysis results in each state were compared and examined.
図１(a)、(b)に測定した各状態別の脈圧波形データを時間領域と周波数領域で分析した結果を示す。心房性期外収縮、心房細動及び心室性期外収縮状態の場合、時間領域と周波数領域の両者において標準状態（健常者）と異なる振幅特性が認められた。また、心臓疾患状態と標準状態における脈圧波形分析の結果を見ると、時間領域の分析と比べて、周波数領域分析の方が、脈圧波形変化特性の違いがより明確になっている。これは、脈圧波形の周波数分析の方が、より高精度に心房細動などの心臓疾患の状態が検出できることを示唆する。 Figures 1 (a) and 1 (b) show the results of analysis of the pulse pressure waveform data for each state measured in the time domain and the frequency domain. In the case of atrial extrasystole, atrial fibrillation and ventricular extrasystole, amplitude characteristics different from those in the standard state (healthy subjects) were observed in both the time domain and the frequency domain. Moreover, looking at the results of the pulse pressure waveform analysis in the heart disease state and the standard state, the difference in the pulse pressure waveform change characteristics is clearer in the frequency domain analysis than in the time domain analysis. This suggests that frequency analysis of pulse pressure waveforms can detect the state of heart disease such as atrial fibrillation with higher accuracy.
ここでは、前述の各状態における脈圧波の周波数分析により得られた周波数スペクトルの振幅変化データの統計解析を行い、心房細動などの心臓の異常を素早く高精度に検出できる特徴量を導く。特に、本解析では、各状態における周波数スペクトルの振幅波形のかたちの違いに着目し、波形が平均値に対して如何にばらついているか、平均値を中心に如何に歪んでいるか、さらに、波形が如何に尖っているかを示す無次元統計量である波形率、歪み度及び尖り度を用いて異常特徴量の検討を行った。 Here, we perform statistical analysis of the amplitude change data of the frequency spectrum obtained by frequency analysis of the pulse pressure wave in each of the above-mentioned states, and derive a feature amount capable of detecting cardiac abnormalities such as atrial fibrillation quickly and with high accuracy. In particular, in this analysis, we focus on the difference in the shape of the amplitude waveform of the frequency spectrum in each state, how the waveform varies with respect to the average value, how it is distorted around the average value, and the waveform. Anomalous features were examined using the waveform rate, the degree of distortion, and the degree of sharpness, which are non-dimensional statistics showing how sharp the edges are.
図２に各状態に対して求めた周波数スペクトルの振幅データの統計量を比較した結果を示す。この図で示された三つの統計量の値は標準状態（正常）における結果値を基準にして標準化したものである。この図から、No-２の心室性期外収縮症状の場合が、すべての統計量において正常の標準状態との差が一番大きかった。また、統計量の中では、尖り度が標準の正常状態の値と一番差が大きかった。以上の結果から、脈圧波の周波数スペクトルの振幅波形データの統計量である波形率、歪み度及び尖り度を監視することで、心房細動や心室性期外収縮などの心臓異常が検出できるといえる。 FIG. 2 shows the results of comparing the statistics of the amplitude data of the frequency spectrum obtained for each state. The values of the three statistics shown in this figure are standardized based on the result values in the standard state (normal). From this figure, in the case of No. 2 premature ventricular contraction, the difference from the normal standard state was the largest in all statistics. In addition, among the statistics, the sharpness was the largest difference from the standard normal state value. From the above results, it is possible to detect cardiac abnormalities such as atrial fibrillation and premature ventricular contraction by monitoring the waveform rate, strain, and sharpness, which are statistics of the amplitude waveform data of the frequency spectrum of the pulse pressure wave. I can say.
ここでは、臨床現場において、健常者１人と心房細動などの心臓疾患者１０人を含めて１１人の被験者から測定した脈圧波形データに対して、本願発明で提案した異常検出手法を適用し、その有効性を評価する。脈圧測定は試作した圧力式脈圧波測定器を使用して行った。特に、被験者中の心臓疾患者に対しては、同一な条件で3回測定を行った。また、これら測定データは、健常者はNo.０、疾患者はNo.１-１、No.１-２、No.１-３からNo.１０-１、No.１０-２、No.１０-３のように番号を付けて区別ができるようにした。測定した脈圧データの評価方法は、以下の流れで行われた。 Here, the abnormality detection method proposed in the present invention is applied to pulse pressure waveform data measured from 11 subjects including 1 healthy person and 10 heart disease persons such as atrial fibrillation in a clinical setting. And evaluate its effectiveness. The pulse pressure was measured using a prototype pressure type pulse pressure wave measuring device. In particular, for those with heart disease among the subjects, measurements were performed three times under the same conditions. In addition, these measurement data are No. 0 for healthy people, No. 1-1, No. 1-2, No. 1-3 to No. 10-1, No. 10-2, No. 10 for diseased people. I made it possible to distinguish by numbering like -3. The evaluation method of the measured pulse pressure data was performed according to the following flow.
（２）臨床現場の被験者の脈圧波形に対する本発明の心臓異常検出方法の有効性評価
本発明の実施例で得られた脈圧データは、図３に示すように最初の加圧過程とその後、最大圧力に到達してから減圧する過程の２段階の波形データになっているが、波形データの評価に用いる有意なデータとしては、最大脈圧からの減圧過程区間のデータであり、本願発明の実施例では最大脈圧からの減圧過程区間で、0.00512秒間隔でサンプリングした7168点の連続データ（約37秒間）を用いて脈圧波形データの分析と評価を行った。図３に測定した脈圧波形データの一例を示す。
(2) Evaluation of Effectiveness of the Cardiac Abnormality Detection Method of the Present Invention on the Pulse Pressure Waveform of a Subject in a Clinical Field The pulse pressure data obtained in the examples of the present invention are the first pressurization process and thereafter as shown in FIG. , It is a two-stage waveform data of the process of depressurizing after reaching the maximum pressure, but the significant data used for evaluation of the waveform data is the data of the decompression process section from the maximum pulse pressure, and the present invention In this example, pulse pressure waveform data was analyzed and evaluated using 7168 continuous data (about 37 seconds) sampled at 0.00512 second intervals in the decompression process section from the maximum pulse pressure. FIG. 3 shows an example of the measured pulse pressure waveform data.
次に、前処理として、時間に伴う下降傾向を示している脈圧波形データの非定常性を取り除くために、用いた7168点の測定値に対して１次階差をとった。図４に１次階差を施した脈圧の時系列波形データを示す。 Next, as a pretreatment, in order to remove the non-stationarity of the pulse pressure waveform data showing a downward tendency with time, a first-order difference was taken from the measured values of 7168 points used. FIG. 4 shows time-series waveform data of pulse pressure with a first-order difference.
さらに、この前処理を施した波形データを平均値０、標準偏差１になるように標準化を行った。これは、前述のシミュレータのデータ処理の場合と同様に、利用者の個人差の影響をなくすためである。 Further, the waveform data subjected to this preprocessing was standardized so that the average value was 0 and the standard deviation was 1. This is to eliminate the influence of individual differences of users, as in the case of the above-mentioned simulator data processing.
最後に、標準化処理を行った脈圧波形データに対して周波数分析を行った。図５に求めた周波数スペクトルの一例を示す。ここでは、疾患の有無による周波数スペクトルの振幅波形のかたちの違いに注目し、振幅波形の形状変化が見られる周波数0.05Hzまでの周波数スペクトルの振幅データを用いて（359データ点で、周波数０のデータは除く）統計解析を行い、本願発明で提案した異常特徴量の歪み度及び尖り度が心房細動などの心臓疾患の状態を高精度に検出できる指標として有効であるかを検証した。このような0.05Hz以下の低周波数帯の振幅波形の変動は、血圧や心拍などの生理学的な挙動と密接な関連があることが報告されている。 Finally, frequency analysis was performed on the pulse pressure waveform data that had been standardized. An example of the frequency spectrum obtained in FIG. 5 is shown. Here, paying attention to the difference in the shape of the amplitude waveform of the frequency spectrum depending on the presence or absence of a disease, the amplitude data of the frequency spectrum up to the frequency of 0.05 Hz in which the shape change of the amplitude waveform is observed is used (at 359 data points, the frequency is 0). (Data excluded) Statistical analysis was performed to verify whether the degree of distortion and sharpness of the abnormal feature amount proposed in the present invention is effective as an index capable of detecting the state of heart disease such as atrial fibrillation with high accuracy. It has been reported that such fluctuations in the amplitude waveform in the low frequency band of 0.05 Hz or less are closely related to physiological behavior such as blood pressure and heartbeat.
（３）臨床現場の被験者の脈圧波形に対して歪み度、尖り度を適用した結果
ここでは、臨床試験現場で測定した健常者１人と心房細動などの心臓疾患者１０人を含めて１１人の被験者の脈圧波形データに対して、本願発明で提案した心臓異常の特徴量として歪み度及び尖り度を適用した実施例の結果を述べる。
図６と７は、健常者の歪み度及び尖り度を基準値１にしたとき、１０人の被験者に対して得られた異常特徴量の歪み度及び尖り度をそれぞれ示したものである。この図から、健常者と比べて、何らかの心臓疾患を持っているすべての被験者の場合、歪み度及び尖り度の値が小さい値を示した。また、この二つの特徴量の比較では、尖り度の方が、健常者の値と比べてより差が顕著に表れており、例えば、健常者の値が１の場合、心臓疾患者の１０人すべてにおいて0.3以下で大きな差を示した。したがって、心臓異常の検出精度から見ると、異常検出指標（異常特徴量）として尖り度を用いた方がより検出精度が高いといえる。
(3) Results of applying the degree of strain and sharpness to the pulse pressure waveforms of subjects in clinical trials Here, including one healthy subject and 10 patients with heart disease such as atrial fibrillation measured at clinical trial sites. The results of an example in which the degree of strain and the degree of sharpness are applied as the feature quantities of the cardiac abnormality proposed in the present invention to the pulse pressure waveform data of 11 subjects will be described.
FIGS. 6 and 7 show the strain and sharpness of the abnormal features obtained for 10 subjects, respectively, when the strain and sharpness of healthy subjects are set to the reference value 1. From this figure, all the subjects having some kind of heart disease showed smaller values of strain and sharpness than those of healthy subjects. In addition, in the comparison of these two features, the sharpness is more remarkable than the value of the healthy subject. For example, when the value of the healthy subject is 1, 10 persons with heart disease A large difference was shown at 0.3 or less in all cases. Therefore, from the viewpoint of the detection accuracy of cardiac abnormalities, it can be said that the detection accuracy is higher when the sharpness is used as the abnormality detection index (abnormal feature amount).
以上のように、臨床試験で測定した脈圧波データを対象として本願発明で提案した異常検出手法の適用性を評価した結果、脈圧波を高速フーリエ変換などの信号処理と統計解析により導いた歪み度及び尖り度の異常特徴量を利用することにより、心筋梗塞や脳卒中の主な原因となる心房細動などの心臓の異常状態を精度よく検出できることを確認した。 As described above, as a result of evaluating the applicability of the anomaly detection method proposed in the present invention on the pulse pressure wave data measured in the clinical test, the distortion degree derived from the pulse pressure wave by signal processing such as fast Fourier transform and statistical analysis. It was confirmed that abnormal conditions of the heart such as atrial fibrillation, which is the main cause of myocardial infarction and stroke, can be accurately detected by using the abnormal feature amount of sharpness.
（４）本発明の心臓異常状態検出装置の説明
（４−１）血圧測定のブロック図
図８は、本発明に係る、脈圧波形を用いた心房細動などの心臓異常状態を検出する装置のブロック図である。
(4) Description of Cardiac Abnormal State Detection Device of the Present Invention (4-1) Block Diagram of Blood Pressure Measurement FIG. 8 is a device for detecting a cardiac abnormal state such as atrial fibrillation using a pulse pressure waveform according to the present invention. It is a block diagram of.
（４−２）心臓異常状態検出装置の構成
図８を参照して、心臓異常状態検出装置１の構成を説明する。心臓異常状態検出装置１は、血圧測定部３と、光電脈波測定部１４と、データ処理部２２とから構成されている。前記血圧測定部３は、血圧測定を制御する血圧測定用マイコン４と、電磁弁５と、医療用の圧電ポンプ６と、圧力センサ７と、被検者の腕に巻き付けて使用する腕帯（カフともいう）８と、そして、前記腕帯（カフともいう）８と、前記電磁弁５と、前記圧電ポンプ６と、前記圧力センサ７とをエア配管９により連通させ、血圧測定用操作スイッチ１０の操作により、前記血圧測定用マイコン４に内蔵のソフトウエアの指示に従い、前記圧電ポンプ６の作動により加圧する時に前記電磁弁５を閉じ、前記腕帯（カフともいう）８が所定の圧力になったら、前記圧電ポンプ６の作動を停止して、血圧を測定するときに前記電磁弁５を徐々に開ける。血圧測定中の圧力や血圧測定結果の圧力を、液晶により血圧表示器１１に表示し、補助記憶装置のメモリ１２に血圧測定結果の圧力を記憶する。
(4-2) Configuration of Cardiac Abnormal State Detecting Device The configuration of the cardiac abnormal state detecting device 1 will be described with reference to FIG. The cardiac abnormality state detecting device 1 is composed of a blood pressure measuring unit 3, a photoelectric pulse wave measuring unit 14, and a data processing unit 22. The blood pressure measuring unit 3 includes a blood pressure measuring microcomputer 4 for controlling blood pressure measurement, an electromagnetic valve 5, a medical piezoelectric pump 6, a pressure sensor 7, and an arm band (used by wrapping around the subject's arm). (Also referred to as a cuff) 8, the arm band (also referred to as a cuff) 8, the electromagnetic valve 5, the piezoelectric pump 6, and the pressure sensor 7 are communicated with each other by an air pipe 9, and an operation switch for blood pressure measurement. By the operation of 10, the electromagnetic valve 5 is closed when pressurizing by the operation of the piezoelectric pump 6 according to the instruction of the software built in the blood pressure measuring microcomputer 4, and the arm band (also referred to as a cuff) 8 has a predetermined pressure. When becomes, the operation of the piezoelectric pump 6 is stopped, and the electromagnetic valve 5 is gradually opened when measuring the blood pressure. The pressure during blood pressure measurement and the pressure of the blood pressure measurement result are displayed on the blood pressure display 11 by a liquid crystal display, and the pressure of the blood pressure measurement result is stored in the memory 12 of the auxiliary storage device.
（４−３）光電脈波測定部１４
前記光電脈波測定部１４は、光電脈波計である指ホルダ１５と、前記光電脈波計である指ホルダ１５からのノイズを除去するノイズフィルタ１６と、前記光電脈波計である指ホルダ１５の微弱な光電脈波の信号を大きくするゲインアンプ（信号増幅器）１７とを含む構成となっている。
(4-3) Photoelectric pulse wave measuring unit 14
The photoelectric pulse wave measuring unit 14 includes a finger holder 15 which is a photoelectric pulse wave meter, a noise filter 16 which removes noise from the finger holder 15 which is a photoelectric pulse wave meter, and a finger holder which is the photoelectric pulse wave meter. The configuration includes a gain amplifier (signal amplifier) 17 for increasing the signal of the weak photoelectric pulse wave of 15.
（４−４）圧力センサ７とゲインアンプ１７
前記血圧測定部３の前記圧力センサ７と、前記光電脈波測定部１４の前記ゲインアンプ（信号増幅器）１７と、後述するデータ処理部２２のデータ処理用マイコン２３に各データ信号を送る構成となっている。
(4-4) Pressure sensor 7 and gain amplifier 17
A configuration in which each data signal is sent to the pressure sensor 7 of the blood pressure measuring unit 3, the gain amplifier (signal amplifier) 17 of the photoelectric pulse wave measuring unit 14, and the data processing microcomputer 23 of the data processing unit 22, which will be described later. It has become.
（４−５）データ処理部２２
データ処理部２２を説明する。このデータ処理部２２は、データ処理用マイコン２３と、モニターを操作するモニター操作用スイッチ２４と、液晶による異常状態表示２５と、処理したデータを記憶する補助記憶装置であるメモリ２６と、処理したデータを転送収納するＳＤカード２７と、前記データ処理用マイコン２３のソフトウエアを書き込んであるmicro-ＵＳＢ記憶装置２８とを含む構成であり、更に、前記したように、前記血圧測定部３の前記圧力センサ７と、前記光電脈波測定部１４の前記ゲインアンプ（信号増幅器）１７は、前記データ処理用マイコン２３に接続して、各データ信号を前記データ処理用マイコン２３に送る構成となっている。
(4-5) Data processing unit 22
The data processing unit 22 will be described. The data processing unit 22 processed the data processing microcomputer 23, the monitor operation switch 24 for operating the monitor, the abnormal state display 25 by the liquid crystal, and the memory 26 which is an auxiliary storage device for storing the processed data. The configuration includes an SD card 27 for transferring and storing data and a micro-USB storage device 28 in which the software of the data processing microcomputer 23 is written. Further, as described above, the blood pressure measuring unit 3 said. The pressure sensor 7 and the gain amplifier (signal amplifier) 17 of the photoelectric pulse wave measuring unit 14 are connected to the data processing microcomputer 23, and each data signal is sent to the data processing microcomputer 23. There is.
本発明は、上述のように、通常の血圧測定に利用する脈圧波のデータを収集して、これら各症状における脈圧波データにより、各症状における脈圧波の周波数分析により得られた周波数スペクトルの振幅変化データの統計解析を行い、心房細動などの心臓の異常状態を簡便に検出できる特徴量を導く方法である。 As described above, the present invention collects pulse pressure wave data used for normal blood pressure measurement, and based on the pulse pressure wave data in each of these symptoms, the amplitude of the frequency spectrum obtained by frequency analysis of the pulse pressure wave in each symptom. This is a method of deriving a feature amount that can easily detect abnormal conditions of the heart such as atrial fibrillation by performing statistical analysis of change data.
そして、通常の血圧測定に利用する脈圧波のデータを用いて、心臓の何らかの異常により変化する脈圧波形のかたちの違いに着目し、波形が平均値を中心に如何にばらついているか、波形が平均値を中心に如何に歪んでいるか、さらに、波形が如何に尖っているかを示す無次元統計量である波形率,歪み度及び尖り度を用いて異常特徴量の検討を行い、これらを監視することで、心房細動や心室性期外収縮などの心臓異常が検出できる。 Then, using the pulse pressure wave data used for normal blood pressure measurement, paying attention to the difference in the shape of the pulse pressure waveform that changes due to some abnormality in the heart, how the waveform varies around the average value, the waveform is Abnormal feature quantities are examined and monitored using the waveform rate, distortion degree, and sharpness, which are dimensionless statistics showing how the waveform is distorted around the average value and how sharp the waveform is. By doing so, cardiac abnormalities such as atrial fibrillation and premature ventricular contraction can be detected.
（４−６）心臓疾患の有無を判断するアルゴリズム
心房細動などの心臓疾患の有無を判断するアルゴリズムを以下に述べる。
・ステップ1：加圧中（加圧時に脈圧を測定する場合）または、減圧中（減圧時に脈圧を測定する場合）で0.00512秒間隔（サンプリング周波数：195.31Hz)で30秒〜1分間の脈圧データを測定する。本願発明では、減圧時の脈圧データを対象とする。
(4-6) Algorithm for determining the presence or absence of heart disease An algorithm for determining the presence or absence of heart disease such as atrial fibrillation will be described below.
・Step 1 : Pressurizing (when measuring pulse pressure during pressurization) or depressurizing (when measuring pulse pressure during depressurization) at 0.00512 second intervals (sampling frequency: 195.31Hz) for 30 seconds to 1 minute Measure pulse pressure data. In the present invention, pulse pressure data at the time of decompression is targeted.
・ステップ2：測定したデータ中、最大圧力から減圧する区間で7168点の連続データを採集し、前処理として、この7168点データに対して１次階差（差分）をとる。これは、時間に伴う下降傾向を表す脈圧波形データの非正常性を取り除くためである。１次階差データは以下のように求める。元の脈圧時系列データを{yt:t=1,2,・・・・・,n}とすると、その１次階差時系列{xt:t=1,2,・・・・・,n-1}は、次のように表される。 -Step 2 : From the measured data, 7168 points of continuous data are collected in the section where the pressure is reduced from the maximum pressure, and as a preprocessing, the primary difference (difference) is taken from this 7168 point data. This is to remove the abnormalities of the pulse pressure waveform data showing the downward tendency with time. The primary floor difference data is calculated as follows. If the original pulse pressure time series data is {y t : t = 1,2, ..., n}, the first-order difference time series {x t : t = 1,2, ...・, N-1} is expressed as follows.
（数１）
xt=yt+1-yt (1)
(Number 1)
x t = y t + 1 -y t (1)
・ステップ3：前述の1次階差データxtを以下のように平均値0、標準偏差1になるように標準化を行う。 -Step 3 : Standardize the above-mentioned primary difference data x t so that the average value is 0 and the standard deviation is 1 as shown below.
（数２）
Xt=(xt-μ)/σ (2)
ここで、Xtは標準化した1次階差データ、μは1次階差データ平均値、σは1次階差データの標準偏差である。
(Number 2)
X t = (x t -μ) / σ (2)
Here, X t is the standardized first-order difference data, μ is the average value of the first-order difference data, and σ is the standard deviation of the first-order difference data.
・ステップ4：標準化した7168点の階差データXtに対して、Cooley-Tukeyの高速フーリエ変換（FFT:Fast Fourier Transform）アルゴリズムを採用し周波数分析を行う。 -Step 4 : Frequency analysis is performed on the standardized 7168 point difference data X t by adopting the Cooley-Tukey Fast Fourier Transform (FFT) algorithm.
・ステップ5：次に、求めた周波数スペクトルの振幅波形データの中、周波数0.05Hzまでのスペクトル振幅データを取り出して統計解析を行う。用いたデータは359点データで周波数０のデータを除いた359点データである。異常検出に使用する特徴量の波形率及び尖り度を算出する。 -Step 5 : Next, from the obtained amplitude waveform data of the frequency spectrum, the spectrum amplitude data up to a frequency of 0.05 Hz is taken out and statistical analysis is performed. The data used is 359 point data excluding the frequency 0 data. Calculate the waveform rate and sharpness of the feature amount used for abnormality detection.
・ステップ６：次に、正常状態を基準値1にした場合、算出したひずみ度及び尖り度の両者の値が1より小さいと、心房細動や心室性外期収縮などの心臓疾患を有すると判断する。 -Step 6 : Next, when the normal state is set to the reference value 1, if both the calculated strain and sharpness values are smaller than 1, it means that the patient has heart disease such as atrial fibrillation or ventricular external contraction. to decide.
以上のことから、本願発明は、通常の血圧測定に利用する脈圧波のデータを収集して、これ等のデータにより、各症状における脈圧波の周波数分析により得られた周波数スペクトルの振幅変化データの統計解析を行い（図１（ｂ）参照）、心房細動などの心臓の異常状態を簡便に検出できる特徴量を導く方法である。 Based on the above, the present invention collects pulse pressure wave data used for normal blood pressure measurement, and uses these data to obtain amplitude change data of the frequency spectrum obtained by frequency analysis of the pulse pressure wave in each symptom. This is a method of performing statistical analysis (see FIG. 1B) to derive a feature amount that can easily detect an abnormal state of the heart such as atrial fibrillation.
以上、本発明の実施例を説明したが、本発明の範囲は、これに限定されるものではなく、本発明の要旨を逸脱しない範囲において種々変更を加え得ることは勿論である。 Although the examples of the present invention have been described above, the scope of the present invention is not limited to this, and it goes without saying that various modifications can be made without departing from the gist of the present invention.
病院や老人福祉施設、学校、職場、家庭などでも心臓疾患に素早く、対応することに貢献できる。 It can contribute to the quick response to heart disease in hospitals, welfare facilities for the elderly, schools, workplaces, and homes.
１‥‥心臓異常状態検出装置
３‥‥血圧測定部
４‥‥血圧測定用マイコン
５‥‥電磁弁
６‥‥圧電ポンプ
７‥‥圧力センサ
８‥‥腕帯（カフ）
９‥‥エア配管
１０‥‥血圧測定用操作スイッチ
１１‥‥液晶血圧表示器
１２‥‥補助記憶装置のメモリ
１４‥‥光電脈波測定部
１５‥‥指ホルダ（光電脈波計）
１６‥‥ノイズフィルタ
１７‥‥ゲインアンプ（信号増幅器）
２２‥‥データ処理部
２３‥‥データ処理用マイコン
２４‥‥モニター操作用スイッチ
２５‥‥異常状態表示器
２６‥‥メモリ
２７‥‥ＳＤカード
２８‥‥micro-ＵＳＢ記憶装置
1 ‥‥‥ Heart abnormality state detection device 3 ‥‥ Blood pressure measurement unit 4 ‥‥‥ Microcomputer for blood pressure measurement 5 ‥‥‥ Electromagnetic valve 6 ‥‥ Piezoelectric pump 7 ‥‥ Pressure sensor 8 ‥‥ Arm band (cuff)
9 ‥‥ Air pipe 10 ‥‥‥ Operation switch for blood pressure measurement 11 ‥‥‥ LCD blood pressure display 12 ‥‥‥ Memory of auxiliary storage device 14 ‥‥‥ Photopulse wave measurement unit 15 ‥‥ Finger holder (photoelectric pulse wave meter)
16 ... Noise filter 17 ... Gain amplifier (signal amplifier)
22 ‥‥ Data processing unit 23 ‥‥‥ Data processing microcomputer 24 ‥‥‥ Monitor operation switch 25 ‥‥‥ Abnormal state indicator 26 ‥‥‥ Memory 27 ‥‥ SD card 28 ‥‥ micro-USB storage device
Claims (2)
The pulse pressure waveform data acquired by the pressure type pulse pressure wave measuring unit, which is a blood pressure measuring unit, and the photoelectric pulse wave meter, which is a photoelectric pulse wave measuring unit, is stored in the data processing unit and analyzed by a data processing microcomputer to display an abnormal state. A device for detecting cardiac abnormalities including atrial fibrillation using the method for detecting an abnormal state of the heart according to claim 1, wherein the presence or absence of a heart disease is determined by a predetermined algorithm based on a series of actions displayed in.
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