Electrical and Electronic Engineering - Research Publications
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ItemEffect of ECG-derived respiration (EDR) on modeling ventricular repolarization dynamics in different physiological and psychological conditions(SPRINGER HEIDELBERG, 2014-10)Ventricular repolarization dynamics is an important predictor of the outcome in cardiovascular diseases. Mathematical modeling of the heart rate variability (RR interval variability) and ventricular repolarization variability (QT interval variability) is one of the popular methods to understand the dynamics of ventricular repolarization. Although ECG derived respiration (EDR) was previously suggested as a surrogate of respiration, but the effect of respiratory movement on ventricular repolarization dynamics was not studied. In this study, the importance of considering the effect of respiration and the validity of using EDR as a surrogate of respiration for linear parametric modeling of ventricular repolarization variability is studied in two cases with different physiological and psychological conditions. In the first case study, we used 20 young and 20 old healthy subjects' ECG and respiration data from Fantasia database at Physionet to analyze a bivariate QT-RR and a trivariate [Formula: see text] model structure to study the aging effect on cardiac repolarization variability. In the second study, we used 16 healthy subjects' data from drivedb (stress detection for automobile drivers) database at Physionet to do the same analysis for different psychological condition (i.e., in stressed and no stress condition). The results of our study showed that model having respiratory information (QT-RR-RESP and QT-RR-EDR) gave significantly better fit value (p < 0.05) than that of found from the QT-RR model. EDR showed statistically similar (p > 0.05) performance as that of respiration as an exogenous model input in describing repolarization variability irrespective of age and different mental conditions. Another finding of our study is that both respiration and EDR-based models can significantly (p < 0.05) differentiate the ventricular repolarization dynamics between healthy subjects of different age groups and with different psychological conditions, whereas models without respiration or EDR cannot distinguish between the groups. These results established the importance of using respiration and the validity of using EDR as a surrogate of respiration in the absence of respiration signal recording in linear parametric modeling of ventricular repolarization variability in healthy subjects.
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ItemRisk stratification of cardiac autonomic neuropathy based on multi-lag Tone-Entropy(SPRINGER HEIDELBERG, 2013-05)Cardiac autonomic neuropathy (CAN) is an irreversible condition affecting the autonomic nervous system, which leads to abnormal functioning of the visceral organs and affects critical body functions such as blood pressure, heart rate and kidney filtration. This study presents multi-lag Tone-Entropy (T-E) analysis of heart rate variability (HRV) at multiple lags as a screening tool for CAN. A total of 41 ECG recordings were acquired from diabetic subjects with definite CAN (CAN+) and without CAN (CAN-) and analyzed. Tone and entropy values of each patient were calculated for different beat sequence lengths (len: 50-900) and lags (m: 1-8). The CAN- group was found to have a lower mean tone value compared to that of CAN+ group for all m and len, whereas the mean entropy value was higher in CAN- than that in CAN+ group. Leave-one-out (LOO) cross-validation tests using a quadratic discriminant (QD) classifier were applied to investigate the performance of multi-lag T-E features. We obtained 100 % accuracy for tone and entropy with len = 250 and m = {2, 3} settings, which is better than the performance of T-E technique based on lag m = 1. The results demonstrate the usefulness of multi-lag T-E analysis over single lag analysis in CAN diagnosis for risk stratification and highlight the change in autonomic nervous system modulation of the heart rate associated with cardiac autonomic neuropathy.
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ItemNo Preview AvailableAnalyzing systolic-diastolic interval interaction characteristics in diabetic cardiac autonomic neuropathy progression(Institute of Electrical and Electronics Engineers (IEEE), 2015-01-01)Cardiac autonomic neuropathy (CAN), one of the major complications in diabetes, if detected at the subclinical stage allows for effective treatment and avoiding further complication including cardiovascular pathology. Surface ECG (Electrocardiogram)-based diagnosis of CAN is useful to overcome the limitation of existing cardiovascular autonomic reflex tests traditionally used for CAN identification in clinical settings. The aim of this paper is to analyze the changes in the mechanical function of the ventricles in terms of systolic-diastolic interval interaction (SDI) from a surface ECG to assess the severity of CAN progression [no CAN, early CAN (ECAN) or subclinical CAN, and definite CAN (DCAN) or clinical CAN]. ECG signals recorded in supine resting condition from 72 diabetic subjects without CAN (CAN-) and 70 diabetic subjects with CAN were analyzed in this paper. The severity of CAN was determined by Ewing's Cardiovascular autonomic reflex tests. Fifty-five subjects of the CAN group had ECAN and 15 subjects had DCAN. In this paper, we propose an improved version of the SDI parameter (i.e., TQ/RR interval ratio) measured from the electrical diastolic interval (i.e., TQ interval) and the heart rate interval (i.e., RR interval). The performance of the proposed SDI measure was compared with the performance of the existing SDI measure (i.e., QT/TQ interval ratio). The proposed SDI parameter showed significant differences among three groups (no CAN, ECAN, and DCAN). In addition, the proposed SDI parameter was found to be more sensitive in detecting CAN progression than other ECG interval-based features traditionally used for CAN diagnosis. The modified SDI parameter might be used as an alternative measure for the Ewing autonomic reflex tests to identify CAN progression for those subjects who are unable to perform the traditional tests. These findings could also complement the echocardiographic findings of the left ventricular diastolic dysfunction by providing additional information about alteration in systolic and diastolic intervals in heart failure.
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ItemSensitivity of temporal heart rate variability in Poincare plot to changes in parasympathetic nervous system activity(BMC, 2011-03-03)BACKGROUND: A novel descriptor (Complex Correlation Measure (CCM)) for measuring the variability in the temporal structure of Poincaré plot has been developed to characterize or distinguish between Poincaré plots with similar shapes. METHODS: This study was designed to assess the changes in temporal structure of the Poincaré plot using CCM during atropine infusion, 70° head-up tilt and scopolamine administration in healthy human subjects. CCM quantifies the point-to-point variation of the signal rather than gross description of the Poincaré plot. The physiological relevance of CCM was demonstrated by comparing the changes in CCM values with autonomic perturbation during all phases of the experiment. The sensitivities of short term variability (SD1), long term variability (SD2) and variability in temporal structure (CCM) were analyzed by changing the temporal structure by shuffling the sequences of points of the Poincaré plot. Surrogate analysis was used to show CCM as a measure of changes in temporal structure rather than random noise and sensitivity of CCM with changes in parasympathetic activity. RESULTS: CCM was found to be most sensitive to changes in temporal structure of the Poincaré plot as compared to SD1 and SD2. The values of all descriptors decreased with decrease in parasympathetic activity during atropine infusion and 70° head-up tilt phase. In contrast, values of all descriptors increased with increase in parasympathetic activity during scopolamine administration. CONCLUSIONS: The concordant reduction and enhancement in CCM values with parasympathetic activity indicates that the temporal variability of Poincaré plot is modulated by the parasympathetic activity which correlates with changes in CCM values. CCM is more sensitive than SD1 and SD2 to changes of parasympathetic activity.
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ItemAnalysis of fetal heart rate asymmetry before and after 35 weeks of gestation(ELSEVIER SCI LTD, 2015-08)
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ItemTone Entropy Analysis of Foetal Heart Rate Variability(MDPI, 2015-03)
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ItemInvestigating scale invariant dynamics in minimum toe clearance variability of the young and elderly during treadmill walking(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2008-08)Current research applying variability measures of gait parameters has demonstrated promise for helping to solve one of the "holy grails" of geriatric research by defining markers that can be used to prospectively identify persons at risk of falling . The minimum toe clearance (MTC) event occurs during the leg swing phase of the gait cycle and is a task highly sensitive to the spatial and balance control properties of the locomotor system. The aim of this study is to build upon the current state of research by investigating the magnitude and dynamic structure from the MTC time series fluctuations due to aging and locomotor disorder. Thirty healthy young (HY), 27 healthy elderly (HE), and 10 falls risk (FR) elderly individuals (who presented a prior history of trip-related falls) participated in treadmill walking for at least 10 min at their preferred speed. Continuous MTC data were collected and the first 512 data points were analyzed. The following variability indices were quantified: 1) MTC mean and standard deviation (SD), 2) PoincarE plot indices of MTC variability (SD1, SD2, SD1/SD2), 3) a wavelet based multiscale exponent beta to describe the dynamic structure of MTC fluctuations, and 4) detrended fluctuation analysis exponent alpha to investigate the presence of long-range correlations in MTC time series data. Results showed that stride-to-stride MTC time series has a nonlinear structure in all three groups when compared against randomly shuffled surrogate MTC data. Test on aging effects showed the MTC central tendency was significantly lower (p < 0.01) and the magnitude of the MTC variability significantly higher (p < 0.01). This trend changed when comparing FR subjects against age-matched HE as both the central tendency (p < 0.01) and magnitude of the variability (p < 0.01) increased significantly in FR. Although the magnitude of MTC variability increased with age, the nonlinear indices represented by alpha, beta, and SD1/SD2 demonstrated that the nonlinear structure of MTC does not change significantly due to aging (p > 0.05). There were, however, significant differences between HY and FR for beta (between scale 1 and 2; p < 0.01) and alpha (p < 0.05). Out of all the variability measures applied, beta(Wv2-4), SD1/SD2, SD2 of critical MTC parameter were found to be potential markers to be able to reliably identify FR from HE subjects. Further research is required to understand the mechanisms underlying the cause of MTC variability.
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ItemSupport Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2009-01)Obstructive sleep apnea syndrome (OSAS) is associated with cardiovascular morbidity as well as excessive daytime sleepiness and poor quality of life. In this study, we apply a machine learning technique [support vector machines (SVMs)] for automated recognition of OSAS types from their nocturnal ECG recordings. A total of 125 sets of nocturnal ECG recordings acquired from normal subjects (OSAS - ) and subjects with OSAS (OSAS +), each of approximately 8 h in duration, were analyzed. Features extracted from successive wavelet coefficient levels after wavelet decomposition of signals due to heart rate variability (HRV) from RR intervals and ECG-derived respiration (EDR) from R waves of QRS amplitudes were used as inputs to the SVMs to recognize OSAS +/- subjects. Using leave-one-out technique, the maximum accuracy of classification for 83 training sets was found to be 100% for SVMs using a subset of selected combination of HRV and EDR features. Independent test results on 42 subjects showed that it correctly recognized 24 out of 26 OSAS + subjects and 15 out of 16 OSAS - subjects (accuracy = 92.85%; Cohen's kappa value of 0.85). For estimating the relative severity of OSAS, the posterior probabilities of SVM outputs were calculated and compared with respective apnea/hypopnea index. These results suggest superior performance of SVMs in OSAS recognition supported by wavelet-based features of ECG. The results demonstrate considerable potential in applying SVMs in an ECG-based screening device that can aid a sleep specialist in the initial assessment of patients with suspected OSAS.