Hirohisa Mizuta

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.

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.

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.

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.

An Improved QT Interval Measurement based on Singular Value Decomposition

This paper proposes an improved method of automatic ECG QT interval measurement based on the singular value decomposition (SVD) of multiple lead ECG signals. SVD separates multiple lead ECG record into orthogonal signals. Major orthogonal signals associated with high singular values are selected first for subsequent analysis. Instantaneous norm of the major three orthogonal signals are used for estimating Q wave initiating time tQ. Two dimensional trajectory of the major orthogonal signals are utilized for T wave end time tTE estimation. The T wave trajectory stagnates at tTE. For the accurate tTE estimation, this stagnation of the trajectory is proposed to be detected by its change in tangential angle. The proposed method was applied to 17 ECG data from normal subjects, patients of long QT syndrome (LQTS) and left ventricular hypertrophy (LVH) to demonstrate its effectiveness. Good consistent agreement, mean relative error of 5.01%, between estimated QT intervals and those of manual measurement by an experienced cardiologist was achieved

An optimal term selection scheme for the Volterra system model applied to the analysis of heart rate fluctuations

Proposes a new method of selecting effective terms of Volterra type nonlinear bio-system models. An efficient algorithm combining genetic algorithm with the householder transform has been introduced for selecting terms which contribute to the AIC (Akaikes Information Criteria) value reduction. Computer simulation confirmed that the considerable reduction of the number of necessary Volterra coefficients has been achieved by the proposed method. The method has been also applied to the estimation of transfer characteristics from the instantaneous lung volume to heart rate fluctuations as an illustrative example.

Extraction of underlying genuine heart rate variability using adaptive signal canceling algorithm

This paper proposes a method for extracting underlying genuine heart rate fluctuations, separating them from distinct respiratory and blood pressure originating fluctuations. Heart rate fluctuations, instantaneous lung volume and blood pressure changes are simultaneously recorded from nine normal subjects aged from 20-24 years old. A signal cancellation scheme together with the adaptive RLS algorithm has been applied to the data to cancel respiration and blood pressure originated changes in the heart rate variability. It was shown that after cancellation, the power spectrum represented a consistent monotonic 1/f/sup a/ type pattern, removing uncertain large individual variations, which enables a clear interpretation of heart rate variability.

A feedback analysis of heart rate fluctuations related to blood pressure change: effect of autonomic blockade

This paper describes the method of relating changes in blood pressure to the heart rate fluctuations. A linear signal generation model with a feedback loop has been introduced and bivariate autoregressive model is applied for the analysis. Computer simulation was carried out to see if the transfer characteristics in both directions could be estimated accurately under the circumstance where both heart rate and blood pressure have a common exogenous source of fluctuations with 1/f spectral pattern. Then the method was applied to the data from 14 male subjects aged from 19 to 38. It was concluded that the parasympathetic blockade abolishes the impulse response of the heart rate fluctuations to the change in blood pressure.

Neural network size determination for electrocardiography inverse dipole solutions

Describes a method to find an appropriate neural network size for obtaining the inverse dipole solution for electrocardiography. A network size reduction algorithm has been proposed. Computer simulation under the single dipole model revealed that the algorithm can greatly reduce the number of hidden layer units while maintaining performance.

A method for the non-stationary analysis of heart rate fluctuations related to instantaneous lung volume

This paper describes a method for the time domain characterization of the heart rate fluctuations relating to the instantaneous lung volume. The recursive least squares algorithm is utilized to track the change in the impulse response of the heart rate to the instantaneous lung volume signal. Computer simulation has validated the proposed method. Results of a real data analysis are also presented as an illustrative example.