The Influence of Vectorcardiogram Orientation on the T/QRS Ratio Obtained Via Non-Invasive Fetal ECG.

Non-invasive fetal electrocardiography (NI-FECG) is an emerging technology that demonstrates potential for providing novel physiological information compared to traditional ultrasound-based cardiotocography (CTG). However, few studies have investigated the reliability of signal features derived via this technique for diagnostic use. One feature of NI-FECG recordings proposed for the purpose of identifying fetal distress is the T/QRS ratio, which has been indicated to change in response to fetal hypoxia. As the T/QRS ratio measures characteristics of the heart's electrical activity in 3D space (represented as the vectorcardiogram), it is critical to understand how changes in the vectorcardiogram orientation may influence the reliability of this feature. To study this influence, this work simulates NI-FECG recordings using eight finite element models of the maternal-fetal anatomy and calculates the T/QRS ratio for a range of vector-cardiogram orientations and sensor positions. To quantify the potential for T/QRS ratio estimation error in real world data, these results are compared to those observed in a homogeneous volume conductor model, as assumed by many existing signal processing techniques. Our results demonstrate that the fetal vectorcardiogram orientation has a significant influence on the reliability of the T/QRS ratio obtained via NI-FECG. Varying the vectorcardiogram orientation through a range of -30 to +30 degrees along each coordinate axis results in the potential for the T/QRS ratio to be underestimated by up to 94% and overestimated by up to 240% if a homogeneous volume conductor model is assumed. Furthermore, we find that the sensor positioning on the maternal abdomen strongly affects the range of the T/QRS ratio estimation error. These results confirm that further study must be undertaken to determine the relationship between the physiological and signal processing domains before utilizing the T/QRS ratio obtained via NI-FECG for diagnostic purposes.

Robust Heart Rate Estimation During Physical Exercise Using Photoplethysmographic Signals.

A method for estimating heart rate (HR) from photoplethysmographic (PPG) signal, during physical exercise, is presented in this paper. Accurate and reliable estimation of HR from PPG during intensive physical activity is challenging because intense motion artifacts can easily mask the true HR. If PPG signal is contaminated by intense motion artifacts, the highest peak of PPG spectrum is shifted from true HR due to motion artifacts. The proposed method employs a simple technique using spectral estimation and median filtering for HR estimation from intensely motion artifacts corrupted PPG signal. Experimental result for a database of 12 subjects recorded during fast running showed that the average absolute estimation error was 1.31 beats/minute.

The effects of asymmetric volume conductor modeling on non-invasive fetal ECG extraction.

OBJECTIVE: Non-invasive fetal electrocardiography (NI-FECG) shows promise for capturing novel physiological information that may indicate signs of fetal distress. However, significant deterioration in NI-FECG signal quality occurs during the presence of a highly non-conductive layer known as vernix caseosa which forms on the fetal body surface beginning in approximately the 28th week of gestation. This work investigates asymmetric modeling of vernix caseosa and other maternal-fetal tissues in accordance with clinical observations and assesses their impacts for NI-FECG signal processing. APPROACH: We develop a process for simulating dynamic maternal-fetal abdominal ECG mixtures using a synthetic cardiac source model embedded in a finite element volume conductor. Using this process, changes in NI-FECG signal morphology are assessed in an extensive set of finite element models including spatially variable distributions of vernix caseosa. MAIN RESULTS: Our simulations show that volume conductor asymmetry can result in over 70% error in the observed T/QRS ratio and significant changes to signal morphology compared to a homogeneous volume conductor model. Volume conductor effects must be considered when analyzing T/QRS ratios obtained via NI-FECG and should be considered in future algorithm benchmarks using simulated data. SIGNIFICANCE: This work shows that without knowledge of the influence of volume conductor effects, clinical evaluation of the T/QRS ratio derived via NI-FECG should be avoided.

Multi-lag tone-entropy in neonatal stress.

Heart rate variability (HRV) has been analysed using linear and nonlinear methods. In the framework of a controlled neonatal stress model, we applied tone-entropy (T-E) analysis at multiple lags to understand the influence of external stressors on healthy term neonates. Forty term neonates were included in the study. HRV was analysed using multi-lag T-E at two resting and two stress phases (heel stimulation and a heel stick blood drawing phase). Higher mean entropy values and lower mean tone values when stressed showed a reduction in randomness with increased sympathetic and reduced parasympathetic activity. A ROC analysis was used to estimate the diagnostic performances of tone and entropy and combining both features. Comparing the resting and simulation phase separately, the performance of tone outperformed entropy, but combining the two in a quadratic linear regression model, neonates in resting as compared to stress phases could be distinguished with high accuracy. This raises the possibility that when applied across short time segments, multi-lag T-E becomes an additional tool for more objective assessment of neonatal stress.

Ensemble Empirical Mode Decomposition With Principal Component Analysis: A Novel Approach for Extracting Respiratory Rate and Heart Rate From Photoplethysmographic Signal.

The photoplethysmographic (PPG) signal measures the local variations of blood volume in tissues, reflecting the peripheral pulse modulated by cardiac activity, respiration, and other physiological effects. Therefore, PPG can be used to extract the vital cardiorespiratory signals like heart rate (HR), and respiratory rate (RR) and this will reduce the number of sensors connected to the patient's body for recording these vital signs. In this paper, we propose an algorithm based on ensemble empirical mode decomposition with principal component analysis (EEMD-PCA) as a novel approach to estimate HR and RR simultaneously from PPG signal. To examine the performance of the proposed algorithm, we used 310 (from 35 subjects) and 632 (from 42 subjects) epochs of simultaneously recorded electrocardiogram, PPG, and respiratory signal extracted from MIMIC (Physionet ATM data bank) and Capnobase database, respectively. Results of EEMD-PCA-based extraction of HR and RR from PPG signal showed that the median RMS error (1st and 3rd quartiles) obtained in MIMIC data set for RR was 0.89 (0, 1.78) breaths/min, for HR was 0.57 (0.30, 0.71) beats/min and in Capnobase data set it was 2.77 (0.50, 5.9) breaths/min and 0.69 (0.54, 1.10) beats/min for RR and HR, respectively. These results illustrated that the proposed EEMD-PCA approach is more accurate in estimating HR and RR than other existing methods. Efficient and reliable extraction of HR and RR from the pulse oximeter's PPG signal will help patients for monitoring HR and RR with low cost and less discomfort.

Modified thresholding technique of MMSPCA for extracting respiratory activity from short length PPG signal.

In this paper, we propose an automatic threshold selection of modified multi scale principal component analysis (MMSPCA) for reliable extraction of respiratory activity (RA) from short length photoplethysmographic (PPG) signals. MMSPCA was applied to the PPG signal with a varying data length, from 30 seconds to 60 seconds, to extract the respiratory activity. To examine the performance, we used 100 epochs of simultaneously recorded PPG and respiratory signals extracted from the MIMIC database (Physionet ATM data bank). The respiratory signal used as the ground truth and several performance measurement metrics such as magnitude squared coherence (MSC), correlation coefficients (CC), and normalized root mean square error (NRMSE) were used to compare the performance of MMSPCA based PPG derived RA. At the data length of 30 seconds, MSC, CC and NRMSE for proposed thresholding were 0.65, 0.62 and -0.82 dB respectively where as they were 0.68, 0.47 and 0.25 dB respectively for existing thresholding. These results illustrated that the proposed threshold selection performs better than existing threshold selection for short length data.

An EEMD-PCA approach to extract heart rate, respiratory rate and respiratory activity from PPG signal.

The pulse oximeter's photoplethysmographic (PPG) signals, measure the local variations of blood volume in tissues, reflecting the peripheral pulse modulated by cardiac activity, respiration and other physiological effects. Therefore, PPG can be used to extract the vital cardiorespiratory signals like heart rate (HR), respiratory rate (RR) and respiratory activity (RA) and this will reduce the number of sensors connected to the patient's body for recording vital signs. In this paper, we propose an algorithm based on ensemble empirical mode decomposition with principal component analysis (EEMD-PCA) as a novel approach to estimate HR, RR and RA simultaneously from PPG signal. To examine the performance of the proposed algorithm, we used 45 epochs of PPG, electrocardiogram (ECG) and respiratory signal extracted from the MIMIC database (Physionet ATM data bank). The ECG and capnograph based respiratory signal were used as the ground truth and several metrics such as magnitude squared coherence (MSC), correlation coefficients (CC) and root mean square (RMS) error were used to compare the performance of EEMD-PCA algorithm with most of the existing methods in the literature. Results of EEMD-PCA based extraction of HR, RR and RA from PPG signal showed that the median RMS error (quartiles) obtained for RR was 0 (0, 0.89) breaths/min, for HR was 0.62 (0.56, 0.66) beats/min and for RA the average value of MSC and CC was 0.95 and 0.89 respectively. These results illustrated that the proposed EEMD-PCA approach is more accurate in estimating HR, RR and RA than other existing methods.