中村 知道

We describe a method for constructing networks for multivariate nonlinear time series. We approach the interaction between the various scalar time series from a deterministic dynamical system perspective and provide a generic and algorithmic test for whether the interaction between two measured time series is statistically significant. The method can be applied even when the data exhibit no obvious qualitative similarity: a situation in which the naive method utilizing the cross correlation function directly cannot correctly identify connectivity. To establish the connectivity between nodes we apply the previously proposed small-shuffle surrogate (SSS) method, which can investigate whether there are correlation structures in short-term variabilities (irregular fluctuations) between two data sets from the viewpoint of deterministic dynamical systems. The procedure to construct networks based on this idea is composed of three steps: (i) each time series is considered as a basic node of a network, (ii) the SSS method is applied to verify the connectivity between each pair of time series taken from the whole multivariate time series, and (iii) the pair of nodes is connected with an undirected edge when the null hypothesis cannot be rejected. The network constructed by the proposed method indicates the intrinsic (essential) connectivity of the elements included in the system or the underlying (assumed) system. The method is demonstrated for numerical data sets generated by known systems and applied to several experimental time series.

We investigate possible origins of the trends in financial markets, where trend we refer to as is a relatively long-term fluctuation observed in price change (price movement), using a simple deterministic threshold model that contains no external driving force term to generate trends forcibly. We find that the trend can be generated by this simple model without any external driving force. Furthermore, from thorough numerical simulations, we obtain two following results: (i) a trend of monotonic increase or decrease can be generated only by dealers' minuscule price updates for the next deal trying to follow an expected forthcoming direction of price change, (ii) non-monotonic trends spontaneously emerge when dealers cannot obtain accurate information about the number of dealers participating in the next deal. We conclude from these results that the emergence of trends is not necessarily generated by an external driving force but by a natural outcome of the accumulation of minuscule price updates of individual dealers with insufficient information about the next deal. (C) 2014 Elsevier B.V. All rights reserved.

We employ a recent technique for building complex networks from time series data to construct a directed network embodying time structure to collate the predictive properties of individual granular sensors in a series of biaxial compression tests. For each grain, we reconstruct a static predictive model. This combines a subset selection algorithm and an information theory fitting criterion that selects which other grains in the assembly are best placed to predict a given grain's local stress throughout loading history. The local stress of a grain at each time step is summarized by the magnitude of its particle load vector. A directed network is constructed by representing each grain as a node, and assigning an in-link to a grain from another grain if the latter is selected within the best predictive model of the first grain. The grains with atypically large out-degree are thus the most responsible for predicting the stress history of the other grains: These turn out to be only a few grains which reside inside shear bands. Moreover, these "smart grains" prove to be strongly linked to the mechanism of force chain buckling and intermittent rattler events. That only a small number of grain sensors situated in the shear band are required to accurately capture the rheological response of all other grains in the assembly underlines the crucial importance of nonlocal interactions, espoused by extended continuum theories which posit nonlocal evolution laws. Findings here cast the spotlight on two specific mechanisms as being key to the formulation of robust evolution laws in deforming granular materials under compression and shear: the long held mechanism for energy dissipation of force chain buckling and the sudden switch in roles that a rattler plays as it enters in and out of force chains. © 2013 American Physical Society.

We employ a recent technique for building complex networks from time series data to construct a directed network embodying time structure to collate the predictive properties of individual granular sensors in a series of biaxial compression tests. For each grain, we reconstruct a static predictive model. This combines a subset selection algorithm and an information theory fitting criterion that selects which other grains in the assembly are best placed to predict a given grain's local stress throughout loading history. The local stress of a grain at each time step is summarized by the magnitude of its particle load vector. A directed network is constructed by representing each grain as a node, and assigning an in-link to a grain from another grain if the latter is selected within the best predictive model of the first grain. The grains with atypically large out-degree are thus the most responsible for predicting the stress history of the other grains: These turn out to be only a few grains which reside inside shear bands. Moreover, these "smart grains" prove to be strongly linked to the mechanism of force chain buckling and intermittent rattler events. That only a small number of grain sensors situated in the shear band are required to accurately capture the rheological response of all other grains in the assembly underlines the crucial importance of nonlocal interactions, espoused by extended continuum theories which posit nonlocal evolution laws. Findings here cast the spotlight on two specific mechanisms as being key to the formulation of robust evolution laws in deforming granular materials under compression and shear: the long held mechanism for energy dissipation of force chain buckling and the sudden switch in roles that a rattler plays as it enters in and out of force chains. DOI: 10.1103/PhysRevE.87.032203

We propose a method of constructing a network, in which its time structure is directly incorporated, based on a deterministic model from a time series. To construct such a network, we transform a linear model containing terms with different time delays into network topology. The terms in the model are translated into temporal nodes of the network. On each link connecting these nodes, we assign a positive real number representing the strength of relationship, or the "distance," between nodes specified by the parameters of the model. The method is demonstrated by a known system and applied to two actual time series. (C) 2012 Elsevier B.V. All rights reserved.

For the analysis of nonstationary stochastic time series we introduce a formulation to estimate the underlying time-dependent parameters. This method is designed for random events with small numbers that are out of the applicability range of the normal distribution. The method is demonstrated for numerical data generated by a known system, and applied to time series of traffic accidents, batting average of a baseball player and sales volume of home electronics.

Recently a new framework has been proposed to explore the dynamics of pseudoperiodic time series by constructing a complex network [J. Zhang, M. Small, Phys. Rev. Lett. 96 (2006) 238701]. Essentially, this is a transformation from the time domain to the network domain, which allows for the dynamics of the time series to be studied via organization of the network. In this paper, we focus on the deterministic chaotic Rossler time series and stochastic noisy periodic data that yield substantially different structures of networks. In particular, we test an extensive range of network topology statistics, which have not been discussed in previous work, but which are capable of providing a comprehensive statistical characterization of the dynamics from different angles. Our goal is to find out how they reflect and quantify different aspects of specific dynamics, and how they can be used to distinguish different dynamical regimes. For example, we find that the joint degree distribution appears to fundamentally characterize spatial organizations of cycles in phase space, and this is quantified via an assortativity coefficient. We applied network statistics to electrocardiograms of a healthy individual and an arrythmia patient. Such time series are typically pseudoperiodic, but are noisy and nonstationary and degrade traditional phase-space based methods. These time series are, however, better differentiated by our network-based statistics. (C) 2008 Elsevier B.V. All rights reserved.

Financial data usually show irregular fluctuations and some trends. We investigate whether there are correlation structures in short-term variabilities (irregular fluctuations) among financial data from the viewpoint of deterministic dynamical systems. Our method is based on the small-shuffle surrogate method. The data we use are daily closing price of Standard & Poor's 500 and the volume, and daily foreign exchange rates, Euro/US Dollar (USD), British Pound/USD and Japanese Yen/USD. We found that these data are not independent. (c) 2007 Elsevier B.V. All rights reserved.

Time-frequency analysis is performed for chaotic flow with a power spectrum estimator based on the phase-space neighborhood. The relation between the reference phase point and its nearest neighbors is demonstrated. The nearest neighbors, representing the state recurrences in the phase space reconstructed by time delay embedding, actually cover data segments with similar wave forms and thus possess redundant information, but recur with no obvious temporal regularity. To utilize this redundant recurrence information, a neighborhood-based spectrum estimator is devised. Then time-frequency analysis with this estimator is performed for the Lorenz time series, the Rossler time series, experimental laser data, and colored noise. Features revealed by the spectrogram can be used to distinguish noisy chaotic flow from colored noise.

In this paper we consider the problem of estimating the parameters of a nonlinear dynamical system given a finite time series of observations that are contaminated by observational noise. The least squares method is a standard method for parameter estimation, but for nonlinear dynamical systems it is well known that the least squares method can result in biased estimates, especially when the noise is significant relative to the nonlinearity. In this paper, it is demonstrated that by combining nonlinear noise reduction and least squares parameter fitting it is possible to obtain more accurate parameter estimates.

We propose a method from the viewpoint of deterministic dynamical systems to investigate whether observed data follow a random walk (RW) and apply the method to several financial data. Our method is based on the previously proposed small-shuffle surrogate method. Hence, our method does not depend on the specific data distribution, although previously proposed methods depend on properties of the data distribution. The data we use are stock market (Standard & Poor's 500 in US market and Nikkei225 in Japanese market), exchange rate (British Pound/US dollar and Japanese Yen/ US dollar), and commodity market (gold price and crude oil price). We found that these financial data are RW whose first differences are independently distributed random variables or time-varying random variables. (c) 2006 Elsevier B.V. All rights reserved.

We describe a method for identifying random walks. This method is based on the previously proposed small shuffle surrogate method. Hence, our method does not depend on the specific data distribution, although previously proposed methods depend on properties of the data distribution. The method is demonstrated for numerical data generated by known systems, and applied to several actual time series of special interest. (c) 2006 Elsevier B.V. All rights reserved.

Recently there has been much attention devoted to exploring the complicated possibly chaotic dynamics in pseudoperiodic time series. Two methods [Zhang et al., Phys. Rev. E 73, 016216 (2006); Zhang and Small, Phys. Rev. Lett. 96, 238701 (2006)] have been forwarded to reveal the chaotic temporal and spatial correlations, respectively, among the cycles in the time series. Both these methods treat the cycle as the basic unit and design specific statistics that indicate the presence of chaotic dynamics. In this paper, we verify the validity of these statistics to capture the chaotic correlation among cycles by using the surrogate data method. In particular, the statistics computed for the original time series are compared with those from its surrogates. The surrogate data we generate is pseudoperiodic type (PPS), which preserves the inherent periodic components while destroying the subtle nonlinear (chaotic) structure. Since the inherent chaotic correlations among cycles, either spatial or temporal (which are suitably characterized by the proposed statistics), are eliminated through the surrogate generation process, we expect the statistics from the surrogate to take significantly different values than those from the original time series. Hence the ability of the statistics to capture the chaotic correlation in the time series can be validated. Application of this procedure to both chaotic time series and real world data clearly demonstrates the effectiveness of the statistics. We have found clear evidence of chaotic correlations among cycles in human electrocardiogram and vowel time series. Furthermore, we show that this framework is more sensitive to examine the subtle changes in the dynamics of the time series due to the match between PPS surrogate and the statistics adopted. It offers a more reliable tool to reveal the possible correlations among cycles intrinsic to the chaotic nature of the pseudoperiodic time series.

Recently, a new surrogate method, the Small-Shuffle (SS) surrogate method, has been proposed to investigate whether there is some kind of dynamics in irregular fluctuations, even if they are modulated by long term trends or periodicities. This situation is theoretically incompatible with the assumption underlying previously proposed surrogate methods. We apply the SS surrogate method to a variety of simulated data with known dynamics and actual time series with unknown dynamics.

In this paper we consider the problem of whether a nonlinear system has dynamic noise and then estimate the level of dynamic noise to add to any model we build. The method we propose relies on a nonlinear model and an improved least squares method recently proposed on the assumption that observational noise is not large. We do not need any a priori knowledge for systems to be considered and we can apply the method to both maps and flows. We demonstrate with applications to artificial and experimental data. The results indicate that applying the proposed method can detect presence or absence of dynamic noise from scalar time series and give a reliable level of dynamic noise to add to the model built in some cases. (c) 2006 Elsevier B.V. All rights reserved.

We describe a method for identifying correlation structures in irregular fluctuations (short-term variabilities) of multivariate time series, even if they exhibit long-term trends. This method is based on the previously proposed small shuffle surrogate method. The null hypothesis addressed by this method is that there is no short-term correlation structure among data or that the irregular fluctuations are independent. The method is demonstrated for numerical data generated by known systems and applied to several experimental time series.

There are a number of good techniques for finding, in some sense, the best model of a deterministic system given a time series of observations. We examine a problem called model degeneracy, which has the consequence that even when a perfect model of a system exists, one does not find it using the best techniques currently available. The problem is illustrated using global polynomial models and the theory of Grobner bases. (c) 2006 American Institute of Physics.

We describe a method for investigating nonlinearity in irregular fluctuations (short-term variability) of time series even if the data exhibit long-term trends (periodicities). Such situations are theoretically incompatible with the assumption of previously proposed methods. The null hypothesis addressed by our algorithm is that irregular fluctuations are generated by a stationary linear system. The method is demonstrated for numerical data generated by known systems and applied to several actual time series.

To build good models, we need to know the appropriate model size. To handle this problem, a variety of information criteria have already been proposed, each with a different background. In this paper, we consider the problem of model selection and investigate the performance of a number of proposed information criteria and whether the assumption to obtain the formulae that fitting errors are normally distributed hold or not in some conditions (different data points and noise levels). The results show that although the application of information criteria prevents over-fitting and under-fitting in most cases, there are cases where we cannot avoid even involving many data points and low noise levels in ideal situations. The results also show that the distribution of the fitting errors is not always normally distributed, although the observational noise is Gaussian, which contradicts an assumption of the information criteria.

Price changes in financial data fluctuate irregularly or stochastically. This paper investigates whether the irregular fluctuations are random or have some kind of dynamics by applying a recently developed method, the small-shuffle surrogate method. The data used are daily gold price, daily Japanese Yen/US dollar exchange rate and tick-wise data of the Swiss Francs/US dollar exchange rate. The results indicate that irregular fluctuations in these data are not random but have some kind of dynamics. (c) 2005 Elsevier B.V. All rights reserved.

We improve the least squares (LS) method for building models of a nonlinear dynamical system given finite time series which are contaminated by observational noise. When the noise level is low. the LS method gives good estimates for the parameters, however. the models selected as the best by information criteria often tend to be over-parameterized or even degenerate. We observe that the correct model is riot selected as the best model despite belonging to the chosen model class. To overcome this, we propose a simple but very effective idea to use the LS method more appropriately. We apply the method for model selection. Numerical studies indicate that the method can be used to apply information criteria more effectively, and generally avoid over-fitting and model degeneracy.

A method to test for nonlinearity in time series, without the need to apply the Fourier transform, is proposed. This method therefore avoids the drawbacks of previously proposed surrogate techniques associated with the estimation of the signal's power spectrum. The test addressed by this algorithm is that the data are generated by a stationary linear system. To achieve this, the algorithm takes advantage of the fundamentally different structure of linear and nonlinear systems.

We describe a method for identifying dynamics in irregular time series (short term variability). The method we propose focuses attention on the flow of information in the data. We can apply the method even for irregular fluctuations which exhibit long term trends (periodicities): situations in which previously proposed surrogate methods would give erroneous results. The null hypothesis addressed by our algorithm is that irregular fluctuations are independently distributed random variables (in other words, there is no short term dynamics). The method is demonstrated for numerical data generated by known systems, and applied to several actual time series.

In this paper a different algorithm is proposed to produce surrogates for pseudoperiodic time series. By imposing a few constraints on the noise components of pseudoperiodic data sets, we devise an effective method to generate surrogates. Unlike other algorithms, this method properly copes with pseudoperiodic orbits contaminated with linear colored observational noise. We will demonstrate the ability of this algorithm to distinguish chaotic orbits from pseudoperiodic orbits through simulation data sets from the Rossler system. As an example of application of this algorithm, we will also employ it to investigate a human electrocardiogram record.

Constructing models of nonlinear time series is typically NP-hard. One of the difficulties is the local minima, and it is difficult to find a global best model. Some methods have already been proposed that attempt to find good models with reasonable computation time. In this paper we propose new methods that can compensate for a drawback of a method previously proposed by Judd and Mees. A standard approach to NP-hard problems is simulated annealing. We apply these methods to build models of annual sunspot numbers and a laser time series, and compare the results. The results indicate that the performance of the proposed method is comparable to that of simulated annealing in both time series. The performance of Judd and Mees method is almost the same as that of the other methods for the annual sunspot data, but not as good for laser time series. The Judd and Mees method is computationally the fastest of all the methods, and the proposed method is faster than simulated annealing.

Many models of the dynamics of nonlinear time series have large numbers of parameters and tend to overfit. This paper discusses algorithms for selecting the best basis functions from a dictionary for a model of a time series. Selecting the optimal subset of basis functions is typically an NP-hard problem which usually has to be solved by heuristic methods. In this paper, we propose a new heuristic that is a refinement of a previous one. We demonstrate with applications to artificial and real data. The results indicate that the method proposed in this paper is able to obtain better models in most cases.

Mixed venous oxygen saturation (SvO(2)) has been proposed as one of the suitable parameters for physiologic control of a total artificial heart (TAH). To establish the practical application of SvO(2), we investigated the response of cardiac output (CO) and SvO(2) to step-loaded exercise. A normal calf was surgically equipped with an ultrasonic flowmeter probe and an oximetry catheter in the pulmonary artery to measure CO and SvO(2), respectively. Three stage step treadmill exercise tests (1, 2, and 4 km/h) were performed three times. While CO increased from 8.9 L/min at preexercise level to 9.7, 10.2 and 11.4 L/min at 1, 2, and 4 km/h, respectively, SvO(2) decreased from 59.6% to 56.8, 55.3, and 52.2%, respectively. There existed a linear correlation between the magnitude of changes in CO and SvO(2). CO and SvO(2) exhibited a similar course of change, expressing an inverted exponential curve. The time constant of SvO(2) was from 19 to 35 seconds, whereas that of CO was from 21 to 39 seconds. We conclude that SvO(2) changes in close association with CO during exercise and has good potential to be a parameter for physiologic control of a TAH, by reflecting the recipient's CO demand without conspicuous time delay.

We have been developing an externally coupled transcutaneous energy transmission system (ECTETS) for a totally implantable total artificial heart (TITAH). When the ECTETS is unable to supply the energy to drive the TITAH from outside the body, a rechargeable internal back-up battery (RIBB) implanted inside the body is used as a back-up to supply the required energy, This paper reports on the performance characteristics of our ECTETS with an RIBB. In this study, a lithium-ion (Li+) secondary battery was used as the RIBB. The transcutaneous energy transmission and the charging control characteristics of the ECTETS, while simultaneously supplying energy to the TITAH and the RIBB, were evaluated in an in vitro experiment. The output power and transmission efficiency of the ECTETS operating in this mode were found to vary from 20 W to 34 W and from 84% to 71%, respectively. It was also found that a sufficient power of more than 20 W could be supplied to the TITAH. The time needed to fully charge the RIBB was 117 minutes, and a fully charged RIBB could drive the TITAH, consuming 20 W for 62 minutes, It may, therefore, reasonably be concluded that the ECTETS with the RIBB is sufficient to drive the TITAH.

We developed a control of the total artificial heart (TAH) using mixed venous oxygen saturation (SvO(2)). With this method, the TAH output was controlled by automatically adjusting drive parameters in response to changes in measured SvO(2). The feasibility and validity of this method were evaluated in a series of treadmill exercise tests using a calf implanted with a pneumatic TAH. The cardiac output (CO) and SvO(2) were measured with an ultrasonic flowmeter and an oximetric catheter, respectively. The calf performed graded exercise under a fixed drive control mode (fixed mode), in which the drive parameters were unchanged throughout the treadmill test and under the SvO(2) based control mode (SvO(2) mode). The calf could go to Stage 6 in both modes, but the maximal oxygen consumption in the SvO(2) mode was 1.5 times as large as that of the fixed mode. Compared with the fixed mode, CO was increased effectively in the SvO(2) mode, and the capacity for exercise was augmented. When considering the relationship between oxygen consumption and oxygen delivery, it was revealed that the CO in the SvO(2) mode responded more adequately to the recipient's oxygen demand than that in the fixed mode. We conclude that this SvO(2) based control method is feasible and physiologically effective.

理想的な全人工心臓 (TAH) の流量制御法では, 生体の要求に見合った拍出流量が実現され, その方法は長期にわたっても信頼性があることが重要である. 混合静脈血酸素飽和度 (SvO2) は, 生体の酸素代謝状況を反映し, 持続計測の技術も確立されており, TAHの流量制御には実現性の高い有望な指標である. 本研究では, 正常仔牛1頭から得られた運動時のSvO2と心拍出量との線形相関式を用いて, 測定されたSvO2値から目標心拍出量を算出, 制御する駆動プログラムを作成した. 空気駆動型TAH装着仔牛でtreadmill運動負荷試験を行い, この制御法を用いて流量制御を行った. その結果, 設定拍出量は, SvO2の変化に従い, 生体内でもほぼ目標通りに実現された. 血行動態も許容範囲内で, 酸素要求に対する実現流量も正常動物に近い関係が得られた. また, 運動耐容力の向上も認め, SvO2を用いた生理的制御法の可能性が示された.

We have been developing an electrohydraulic total artificial heart system. The system comprises an intrathoracic pumping unit composed of diaphragm type ellipsoidal blood pumps and an energy converter in addition to an electronics unit. The in vivo performance of the pumping unit was evaluated in a series of animal implantations with 3 calves weighing 62-85 kg. An interatrial shunt 4.5 mm in diameter was made in the atrial septum to compensate left-right imbalance. Two calves died early postoperatively, one of external controller power failure and the other of interatrial shunt stenosis due to thrombus formation. One calf, however, survived over 10 days under stable circulatory conditions. No abnormality was found in the oxygen metabolic condition or in major organ functions. The generation and dissipation of heat from the device was acceptable. This animal died of device malfunction caused by energy converter bearing breakdown. The device demonstrated a good anatomic fit without compromising the great vessels and adjacent tissues. It is concluded that the pumping unit has a sufficient in vivo basic performance although appropriate countermeasures are to be implemented against the detected problems concerning mechanical durability and interatrial shunt patency.

For physiological control of a total artificial heart (TAH), applying mixed venous oxygen saturation (Svo(2)) as a parameter for TAH control is a promising approach regarding sensitivity to the recipient's oxygen demand and the practical possibility of continuous monitoring using near infrared rays through transparent blood pump housings. To develop a control method for the TAH using Svo(2), the relationship between Svo(2) and cardiac output (CO) was investigated in a normal calf, and a control algorithm was developed based on this correlation. Then the feasibility of this method (Svo(2) mode) was evaluated in a calf implanted with a pneumatic TAH and compared with the fixed drive control mode (fixed mode) in which the drive parameters were unchanged. The calf performed a graded exercise test in both modes. The CO was effectively increased from 7.3 to 13.0 L/min in the Svo(2) mode, and the capacity for exercise was augmented compared to the fixed mode. We conclude that this Svo(2) mode is feasible and may be effectively applied in TAH control.

Three kinds of rechargeable batteries, NiCd, NiMH, and Li+, were compared for the purpose of selecting the most appropriate battery for use in a Rechargeable Internal Back-up Battery (RIBB) unit for a Totally Implantable Artificial Heart. Batteries of each kind were connected in series to obtain the required driving voltage of 24 V. The NiCd and NiMH batteries were charged by a constant current of 1 C, and the Li+ batteries were charged first by a constant current of 1 C and later by a constant voltage of 28.7 V. All three types of batteries were discharged using a dummy electronic pulsatile load consuming 20 W of power. The tests showed that the Li+ batteries were capable of supplying the required energy for more than 60 minutes. The Li+ batteries had a specific energy of 121.5 Wh/kg, which was more than 3× that of the NiCd and NiMH, and an energy density of 282.5 Wh/L more than double that of the other two. In addition, the Li+ batteries recorded the least temperature rise during charging and discharging. The results of our tests conclusively showed that the Li+ battery is the best among the three for use in an RIBB from the viewpoint of energy density and temperature rise.

体内完全埋込型全人工心臓用体外結合型経皮エネルギー伝送システム(TETS)の生体適合性を実用化の面からin vitro, in vivo実験にて評価した. in vitro実験では模擬負荷を用いて約24Wの電気エネルギーを伝送し, シリコン封入前後の整流回路の発熱分布特性をそれぞれ空気中で評価した. in vivo実験:では体重44kgの成山羊に体内(2次)コイルと整流回路を埋め込み, 模擬負荷を用い約24Wの電気エネルギーを伝送し, 整流回路の内部及び表面, 体外コイル表面の発熱, エネルギー伝送効率を評価した. また, エネルギー伝送中に時速2km/hのトレッドミル試験を行い, 体動によるエネルギー伝送効率への影響を検討した. その結果, 体内埋込型整流回路に異常な高温部は認められず, 生体組織に対して充分許容範囲内の発熱特性を示した. また, 伝送効率は実験条件によらず80～85%を維持した. 以上の点から, 開発中のTETSは充分な生体適合性および実用性を有することが確認された.

DC-DCエネルギー伝送効率が86%以上の完全埋込型人工心臓駆動用体外結合形経皮エネルギー伝送システム(ECTETS)のin vitro及びin vivo実験を行った。体内コイル、体内整流回路をヤギ皮下に埋込み、体外に設置された(1)電子負荷装置または(2)電気油圧駆動方式完全埋込型人工心臓をECTETSにより動作させた。その結果、(1)において、DC-DCエネルギー伝送効率は、81.4%(出力電力19.2W)であった。In vitroと比べ効率が低下したのは、体外コイル滅菌のための消毒液の付着により分布静電容量が増加したことが原因であることがわかった。また、(2)人工心臓の駆動(70bpm)においては、駆動電力、拍出流量、DC-DCエネルギー伝送効率、埋込部最高温度はそれぞれ20.7W、6.2L/min、82.1%、40.4℃となり生体に影響がない程度であることが確認された。

In order to enhance the implantability of the externally-coupled transcutaneous energy transmission system (ECTETS), efficiencies in various parts of the circuit system were improved to reduce heat dissipation. As a result. a DC-to-DC energy transmission efficiency of 85% or more was obtained in an in vitro experiment conducted at a power output of 19W. In the in vivo measurement of this improved ECTETS with the internal coil and the rectifier circuit implanted under the skin of a living goad, a DC-to-DC energy transmission efficiency of 82% or more was obtained. Fluctuation of efficiency due to movement of the body was found to be less than 0.5%. when this improved ECTETS is used with die totally implantable electrohydraulic artificial heart with an overflow mock circulatory system connected as a load. the pump output flow at a changing heart rate was found to be 4.4-7.0L/min (50-110bpm). For continuous operation at 70bpm, the DC-to-DC energy transmission efficiency was 81% or more, the pump output flow was 6.2L/min, and the temperature in the part of implantation was lower than 41 degrees C, a temperature that can hardly affect a living body.