This paper presents a time domain technique for estimating transfer characteristics from fluctuations of instantaneous lung volume (ILV) to heart rate (HR). An effective procedure for estimating the impulse response of HR to ILV is proposed. Pre- and post-processing procedures, including prefiltering of the HR signal, preenhancement of the high frequency content of the ILV signal, and post-filtering of the estimated impulse response, together with a random breathing technique, are shown to effectively reduce spurious transfer gain so as to get a stable estimate of the impulse response. Analysis of the data collected from fourteen healthy male subjects in various conditions revealed that there are three components in the impulse response: fast positive, delayed slow negative, and oscillatory. The effects of the autonomic blocking agents propranolol and atropine on these transfer characteristics are also described.
This paper proposes a new non-invasive method for the Electromyogram(EMG) recruitment analysis. The power and bispectrum have been utilized in estimating the property o f newly recruited neuromuscular unit (NMU) activity. A recursive procedure to estimate the newly recruited motor unit action potential (MUAP) waveforms and its occurring frequency in the incremental force generation scheme has been introduced. The method has been applied to the surface EMG data above normal biceps muscles a s an illustrative example for confirming the practical feasibility of the method. I. IIVI'RODUCI'IONSophisticated cooperation of many muscles enables complex and smooth motion of living animals. Since force generation mechanism is quite different ftom any artificial mechanical equipment, it is interesting to b o w the mechanism from an engineering stand point of view. A statistical method to estimate the amplitude and the number of newly recruited MUAPs has been proposed by Kanosue et al. [l] for the incremental forth generation scheme. The method uses the second and forth order moments with a parametric model of the elementary h4UAP waveforms. W+ have generalized the method and enabled us to estimate MUM waveforms and their occurring fresuency without any assumption for the MUAP waveforms. To clarify the proposed method, the surface EMG has been recodedon normal biceps and processed. I% have observed the increase in MUAP amplitude and decrease in their occurring firesuency aMxlrding to the increase in the muscle forth. The result agreed well with previous reports and showed the feasibility of the proposed method. II. m1oDAssuming that the surface EMG is xxxlled shot noise, the powerandbi-spectrum P ( o ) , B(wl,w2) has been described as follows,Here, H(w) and R respectively denote a single MUAP waveform and its occurring fmyency. Combining these equations, we get the following formula necessafy to estimate H( w ) and R from obtained power and bi-spectrum. J4)Hem, (P (0 =WH(@ 1. Q (0 is estimated from the bispectrum using the phase estimation algorithm found in [3]. One may question that the occurring time sequence of MUAP waveforms doesn't follow a Poisson process since single M U M occurrences are known to be reasonably modeled as a renewal process with a fairly regular Gaussian interval distribution. However, we are observing superposed MUAps from many NMUs. In such a case, as far as we are analyzing surfaceEMG through a short term time window, the assumption that the pooled MUAP occurrence times follow a Poisson process is reasonable. W+ will next &der the case where the muscle load has been increased in step-wke manner. We define the power and bi-spectrum at the k-th load level as pk (0 )and B k (0 2).The difference power and bi-spectrum M k (0 and m k (a 1 ,U 2) are in-uced as, 1345
A statistical method for testing the Poisson hypothesis of spontaneous quantal transmitter release at neuromuscular junctions has been proposed. The notion of the Poisson hypothesis is extended so as to allow for nonstationarity in the data, since nonstationarity is commonly seen in the occurrence of spontaneous miniature potentials. Special emphasis has been put on the nonstationary analysis of the quantal release. A time scaling technique has been introduced and is discussed for the analysis. Artificially generated data, which simulate three types of nonstationary spontaneous quantal release, i.e., Poisson, non-Poisson-clustered, and non-Poisson-ordered types, were analyzed to demonstrate the effectiveness of the method. Some sets of miniature endplate potentials, intracellularly recorded at frog sartorius neuromuscular junctions in low Ca++ and high Mg++ solutions showing apparent nonstationarities, were analyzed as illustrative examples. The proposed method will extend the range of applicable data for the statistical analysis of spontaneous quantal transmitter release.
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