On a unified point process approach for the characterization of bioelectric discrete phenomena.

This paper discusses a unified method based on the theory of point processes to characterize various types of bioelectric discrete signals such as heart beat timing, myoelectric activity, discharge of primary sensory neurons or neurons in the central nervous systems. The doubly stochastic point processes, in which the discrete event occurring intensity is stochastic, forms the most general class to characterize the discrete phenomena. In this paper the self-exciting process has been shown to be useful to characterize wide range of discrete biosignals. The modeling of conditional intensity function is the essential part of the characterization. When the intensity has a parametric model, the maximum likelihood parameter estimation will be the useful way to characterize the phenomena. The effectiveness of the method is demonstrated by a specific modeling of the spontaneous neuronal burst discharges recorded from the brain thalamus during the neuro surgery. The first approximation model has four parameters obtained by the instantaneous nonlinearly transformed sinusoidal function. An extended model allows arbitrary periodic intensity with refractory period. Predicted interval histograms show good agreement with the observed ones indicating the validity of the proposed method.

Sudden cardiac arrest risk stratification based on 24-hour Holter ECG statistics.

This study examined the feasibility of using indices obtained from a long term Holter ECG record for sudden cardiac arrest (SCA) risk stratification. The ndices tested were the QT-RR interval co-variability and the alternans ratio percentile (ARP(θ)) which is defined as the θ(th) percentile of alternans ratios over a 24 hour period. The QT-RR interval co-variabilities are evaluated by the serial correlation coefficient between QT and RR trend sequences (QTRC). Previously reported Kalman filter technique and a simple smoothing spline method for the trend estimation are compared. Parameter θ in the alternans ratio percentile index was optimized to achieve the best classification accuracy. These indices were estimated from 26 cardiovascular outpatients for Holter ECG record. Patients were classified into high and low risk groups according to their clinical diagnosis, and the obtained indices were compared with those of 25 control subjects. A risk stratification using the two indices QTRC and ARP(θ) yielded an average sensitivity of 0.812 and a specificity of 0.925. The sensitivities and specificities of all three categories exceeded 0.8 except for the sensitivity to detect the high-risk patient group. Other short-term ECG parameters may need to be incorporated in order to improve the sensitivity.

Long and short term QT-RR interval co-variability in type 2 diabetes.

This paper examines the long and short term co-variability of QT and RR intervals for diabetic patients to explore if the QT-RR co-variability could yield a noble index for the stratification of clinical severity of the disease. Twenty four hour Holter ECG recordings are made for 19 type 2 diabetic (T2DM) patients and 25 normal subjects. RR and QT intervals are extracted from ECG signals sampled at 200 Hz and their co-variability has been examined. To see the long term QT-RR co-variability, correlation coefficients and mutual entropies between QT and RR intervals have been estimated for original beat to beat intervals and smoothed median interval series of successive one hundred beats. Mutual entropy for both beat-to-beat and smoothed median QT and RR interval series showed statistically significant differences between T2DM and control subjects whereas differences in correlation coefficients showed significant difference only for beat-to-beat intervals. Mutual entropy between both beat-to-beat and smoothed median QT-RR interval sequences showed the equally well separation between T2DM patients and control subjects: Mutual entropy and serial correlation coefficients for beat to beat intervals are respectively 1.42 ± 0.33 (bits), 0.856 ± 0.055 for control and 0.752 ± 0.23 (bits), 0.756 ± 0.10 for T2DM patients. Scatter diagram between RR and QT intervals show apparent nonlinearity which validate this result. Short term QT-RR co-variability has been examined by spline smoothed QTc series and sporadic changes have been observed for the control subjects whereas no such changes are found in diabetic patients. This new phenomenon could be a mean for the clinical characterization of diabetes.

Greater insulin resistance indicates decreased diurnal variation in the QT interval in patients with type 2 diabetes.

Circadian variations in the QT interval (QT) and QT dispersion are decreased in patients with type 2 diabetes because of cardioneuropathy. Insulin resistance has been recently identified as an independent determinant of QT prolongation in normoglycemic women. However, the relationship between QT prolongation and the degree of insulin resistance as well as circadian variation remains unclear in diabetic patients. This study was designed to assess the relationship between insulin resistance and the circadian variation in QT measurements in patients with type 2 diabetes. In 14 patients with diabetes, QT, corrected QT (QTc), QT dispersion, QTc dispersion, and RR interval (RR) were analyzed using 12-lead Holter monitoring and commercial software. The degree of diurnal variation in each measurement was defined as the amplitude between the maximum and mean values on curves fitted using the mean cosinor method (A_QT, A_QTc, A_QT dispersion, A_QTc dispersion, and A_RR). The cosine curve was fitted to all measured values in each QT measurement and RR for 24 h. Insulin resistance (glucose infusion rate (GIR)) was measured using the euglycemic hyperinsulinemic glucose clamp method. The maximum QT, QTc, QT dispersion, and QTc dispersion were >450 ms. GIR was significantly correlated with A_QT only (r = 0.59, P < 0.05). GIR was not correlated with other variables, and was dependent only on the circadian variation in QT.

T-wave alternans search over 24 hour holter ECG recordings based on singular value decomposition.

This paper proposes an efficient method to search for T-wave alternans (TWA) over 24 hour Holter ECG recordings. After appropriate pre-processing to remove baseline drift and artifact, data are segmented to 2 minute successive time intervals. For each beat in the segment, singular value decomposition is applied to derive orthogonal characteristic signals. Then two prominent orthogonal signals are used for the TWA search. A pair of alternans indices is defined for each beat as the orthogonal waveform distance between the target beat and the adjacent two beats. When alternans presents, the first index will be larger than the second index. The periodogram of the sequence of alternans indices in each segment yields a useful alternans measure named Alternans Ratio (AR). To show the effectiveness of the measure, the method is applied to 25 control and 24 data from patients with various cardio vascular disorders. AR distribution showed prominent differences among subject groups. It has been demonstrated that the measure AR is not only useful to detect the presence of TWA but the AR distribution can be used for the stratification of the TWA risk.

RR-QT interval trend covariability for sudden cardiac death risk stratification.

This paper examines the feasibility of the trend covariability between QT and RR Intervals (QTIs and RRIs) be a novel mean of the sudden cardiac death (SCD) risk stratification. Twenty four hour beat to beat QTIs and RRIs are measured from Holter ECG recordings of 25 normal control subjects (SCD-C), 14 low SCD risk patients (SCD-L) with high blood pressure or light cardiac arrhythmia and 11 SCD high risk patients (SCD-H) with heart attack history. The Kalman filtering technique has been applied to decompose 24 hour short term mean QTIs and RRIs sequences into trend components and additive random variations. The correlation coefficients (TC-QT/RR) and cross entropies (TE-QT/RR) between the QT and RR trend signals are estimated. Cross entropy TE-QT/RR achieved the best stratification of subject groups. TE-QT/RR distribution for SCD-C, -L -H subject groups were 1.697 ± 0.058, 1.160 ± 0.099, 0.920 ± 0.067. The differences in entropy values are statistically significant for all classes pairs (SCD-H and -C (p<0.00001); -L and -C (p<0.001); -H and -L (p<0.05) The result indicates that the TE-QT/RR could be a novel index for the SCD risk stratification.

A method for characterizing circadian changes in QT intervals of diabetic patients.

This paper proposes a method to characterize circadian changes in QT intervals for diabetic studies. Although properties of QT intervals for diabetic patients are extensively studied, their circadian changes are not fully understood. Recently, the traditional cosinor method has been utilized for a study examining the relationship between QT circadian changes and the insulin resistance of the diabetic patients. For better characterization of the circadian change in QT intervals of this kind, spline smoothing technique applied to a decimated data set of QT intervals is proposed. New indices named QT circadian transition time (QTCT) and QT circadian transition amplitude (QTCA) associated with the subjects' awakening process are defined to characterize diabetic patients' condition. The method is applied to ten normal and fifteen type 2 diabetic patients. The proposed indices showed significantly lower values for type 2 diabetic patients compared to the control subjects indicating their effectiveness for the characterization.

State classification of heart rate variability by an artificial neural network in frequency domain.

This paper examines the feasibility of accurate state classification of autonomic nervous activity (ANA) based on the power spectral pattern of the heart rate fluctuations (HRFs). Some attempts have been made to utilize artificial neural networks (ANNs) to classify HRFs for clinical diagnoses such as ischemic cardiomyopathy, arrhythmia or sleep apnea. To establish the firm bases for making such clinical diagnoses, it may be important to examine the classification accuracy for the data in physiologically well defined conditions by e.g. application of autonomic blocking agents. In this paper the three layered perceptron has been trained by the heart rate data in variety of ANS states yielded by the application of Atropine and Propranolol to 14 healthy male subjects. Six state (control, atropine and propranolol for each of the spine and upright posture) classification based on power spectrum showed average sensitivity of 67.2% and specificity 91.2%. Four state (control, atropine, propranolol and double block for either spine or upright posture) resulted in the average classification sensitivity of 75.7% and specificity 95.5%. The paper revealed that entropy bandwidth and indices originated from characteristic oscillations of blood pressure change improve the classification accuracy.

Spontaneous variability analysis for characterizing cardiovascular responses to water ingestion.

This paper examines the effect of water ingestion on the cardiovascular system, utilizing advanced fluctuation analysis. The ingestion of water has been known to significantly raise the blood pressure in subjects with autonomic disorders, resulting in the effect of preventing syncope occurrences. For precise characterization of the effect of water ingestion, head-up tilt experiments at 80 degrees have been conducted for fourteen healthy subjects, ranging in age from 16 to 24. Systolic/diastolic blood pressures (sBP/dBP), total peripheral resistance index (TRPI) and ECG RR intervals (RRIs) were measured for thirty minutes before and after the isotonic water ingestion of 340 ml. Blood pressures: sBP (2.8%), dBP(3.6%), and TPRI (5.3%) showed statistically significant increases after the water ingestion. RRIs also tended to increase (2.3%), although they were not statistically significant. The data analysis confirmed that the water injection of 340 ml has the acute effect against the syncope occurrences that are mainly due to the increase in TPRI. Then heart rate (HR) spectral analysis with the derivative of the cubic spline interpolation (DCSI) method, and a closed loop system identification technique, which associate fluctuations in sBP and HR, are utilized for further precise characterization of the change in recorded physiologic quantities.

Influence of exposure to diesel emissions in rats and distribution profile for R-R interval.

In order to evaluate the influence of diesel emissions on cardiac function we extracted resting R-R interval and a body activity index derived on a distribution profile of R-R interval. After seven months of exposure in rats revealed that particulate matter in diesel emissions decreases resting R-R interval and activity.