Kazuo Yana

Instantaneous frequency analysis of systolic murmur for phonocardiogram

This paper proposes the use of instantaneous frequency for the characterization of phonocardiogram, in the range from the first heart sound to systolic heart sound. The data were collected from nation wide phonocardiogram screening of the elementary school children conducted in Japan. We have estimated instantaneous frequency of the phonocardiogram from averaged Wigner-Ville distribution. The data analysis showed a different change of instantaneous frequency between innocent murmur and pathological murmur. It could provide a new method for classifying the systolic murmur into innocent murmur and pathological murmur.

Detection and classification of systolic murmur for phonocardiogram screening

This paper introduces a method for the automatic classification of the phonocardiogram. The method utilizes a multi layer perceptron for detecting the presence of the systolic murmur and a spectral analysis for separating innocent murmur from pathological murmur. The effectiveness of the method was confirmed by applying the method to the data collected from nationwide phonocardiogram screening of the elementary school children conducted in Japan. Data include 44 pathological systolic murmur, 61 innocent murmur and 36 normal data. Average correct detection rate of 90.8% was achieved for systolic murmur detection. Spectral analysis then successfully separated musical murmur from pathological systolic murmur. The method could be an effective assistance for medical doctors make their final diagnoses.

Statistical properties of simultaneously recorded fluctuations in pupil diameter and heart rate

Describes the relationship between fluctuations in pupil diameter (PD) and heart rate (HR). Simultaneous recording of PD, HR and instantaneous lung volume (ILV) has been made for ten normal healthy subjects. The spectral analysis showed that the cross phase spectrum between PD and HR fluctuations had a constant value -/spl pi//2 over fairly long frequency range (0.1-0.4 Hz). In addition to this, the authors have observed that the power spectrum of the PD fluctuations showed 1/f/sup 3/ spectral pattern in the frequency range while the power spectrum of HR variability showed 1/f spectral pattern. This findings implies that HR variability is associated with the derivative of PD fluctuations and may be important for the characterization of PD fluctuations as an index to measure autonomic nervous activity.

Surface electromyogram recruitment analysis using higher order spectrum

This paper proposes a new noninvasive method for electromyogram (EMG) recruitment analysis. The power and bispectrum have been utilized in estimating the properties of 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 of normal biceps muscles as an illustrative example for confirming the practical feasibility of the method.

Bispectral Analysis Of Filtered Impulse Processes With Applications To The Analysis Of Bioelectric Phenomena

This article presents two applications of hi-spectra for the analysis of bioelectric phenomena modeled as filtered impulse processes; the surface elect romyogram and spent aneous synaptic potentials. Estimation of the elementary waveform and the frequency of its occurrence is important for the analysis of such phenomena when the frequency of elementary waveform occurrence is high enough to cause heavy waveform contamination. Explicit expressions of the elementary waveform and the frequency of its occurrence in terms of the power spectrum and hi-spectrum are first presented in the case that the waveform occurrence times form a stationary Poisson process. Then the computer simulation demonstrates the applicability of the method for the analysis of bioelectric phenomena under consideration. The estimation of the elementary waveform may be utilized as a means of making noninvasive diagnoses of neuromuscular disorder and estimation of the frequency of elementary waveform occurrence may be applied to the analysis of transmitter release at bio-synapses.

Phonocardiogram classification using time-frequency representation

Introduces a method for classifying systolic murmurs using the time-space representation of a phonocardiogram (PCG). The method detects the presence of systolic murmurs by a neural network utilizing variance sequences. Then, a wavelet transform is applied to that data classified as being related to a systolic murmur. A second neural network classifies the time-frequency distribution thus obtained into abnormal and benign murmurs. The proposed method was applied to data obtained from a nationwide phonocardiogram screening of elementary school children conducted in Japan. A correct decision rate of 98% was achieved for the first stage (detecting the presence of a systolic murmur). Correct classification rates for abnormal and benign murmurs were 78.6% and 84.5% respectively. The method could be useful in assisting medical doctors to make a final decision.

Detection and classification of systolic murmur using a neural network

Introduces a method for the automatic classification of the phonocardiogram. The method utilizes a multilayer perceptron for detecting the presence of the systolic murmur and a spectral analysis technique for separating innocent murmur from pathological murmur. The effectiveness of the method was confirmed by applying the method to data collected from a nationwide phonocardiogram screening study of Japanese elementary school children.

Heart rate variability change induced by the mental stress: the effect of accumulated fatigue

Evidence is shown that accumulated fatigue greatly affects statistical properties of the heart rate fluctuations. Comparison of heart rate variabilities before and after an exhausting physical and mental task was made for six volunteer male subjects. Significant changes in mean heart rate, variances and spectral characteristics were found showing the importance of considering the background physical and mental condition in the characterization of the heart rate.

Estimation of the density of the filtered Poisson impulse process: a parametric approach

The Poisson driven pth order autoregressive (PDAR(p)) process is defined as the output of a continuous-time autoregressive system, driven by a stationary Poisson impulse process. An explicit formula for estimating the density of the Poisson impulse process is derived by combining the second- and third-order cumulants of the discretized PDAR(p) process. The validity of the proposed method is assessed through Monte Carlo simulations in some specific examples. >

Adaptive estimation of the degree of system nonlinearity

Real systems more or less have nonlinear characteristics. In some applications it may be useful if one could measure the degree of the system nonlinearity, e.g. when one may want to know if the operating point of an electronic circuit is properly set. In stochastic dynamical system modeling based on observed input and output time series, it may be important to check first if the target system can be reasonably modeled as a linear system. Coherence function has been an index of system nonlinearity and conventionally used as a probe for the detection of system nonlinearity. However, in the case where the system output includes additive exogenous noise, low coherence value could not distinguish if the system is linear with additive output noise or the system is nonlinear. As an alternative, an index called the degree of system nonlinearity d.n. which is not affected by the presence of output noise has been previously proposed and effectively applied to the study of characterizing the heart rate variability. In some applications such as monitoring operating point of the electronic system, real time tracking of the degree of nonlinearity may be useful. This paper extends the previously proposed d.n. estimation method, adopting an adaptive signal processing method, to track changes in the degree of system nonlinearity based on observed system input and output time series.