Robust Fetal Heart Beat Detection via R-Peak Intervals Distribution

Quantifying Sharpness and Nonlinearity in Neonatal Seizure Dynamics

The integration of multiple electrophysiological biomarkers is crucial for monitoring neonatal seizure dynamics. The present study aimed to characterize the temporal dynamics of neonatal seizures by analyzing intrinsic waveforms of epileptic electroencephalogram (EEG) signals. We proposed a complementary set of methods considering envelope power, focal sharpness changes, and nonlinear patterns of EEG signals of 79 neonates with seizures. Features derived from EEG signals were used as input to the machine learning classifier. All three characteristics were significantly elevated during seizure events, as agreed upon by all viewers (P < 0.0001). Envelope power was elevated in the entire seizure period, and the degree of nonlinearity rose at the termination of a seizure event. Epileptic sharpness effectively characterizes an entire seizure event, complementing the role of envelope power in identifying its onset. However, the degree of nonlinearity showed superior discriminability for the termination of a seizure event. The proposed computational methods for intrinsic sharp or nonlinear EEG patterns evolving during neonatal seizure could share some features with envelope power. Current findings may be helpful in developing strategies to improve neonatal seizure monitoring.

A novel dynamic cardiorespiratory coupling quantification method reveals the effect of aging on the autonomic nervous system

Traditional cardiopulmonary coupling (CPC) based on the Fourier transform shares an inherent trade-off between temporal and frequency resolutions with fixed window designs. Therefore, a cross-wavelet cardiorespiratory coupling (CRC) method was developed to highlight interwave cardiorespiratory dynamics and applied to evaluate the age effect on the autonomic regulation of cardiorespiratory function. The cross-wavelet CRC visualization successfully reflected dynamic alignments between R-wave interval signal (RR intervals) and respiration. Strong and continuous CRC was shown if there was perfect temporal coordination between consecutive R waves and respiration, while CRC becomes weaker and intermittent without such coordination. Using real data collected on electrocardiogram (ECG) and respiratory signals, the heart rate variability (HRV) and CRC were calculated. Subsequently, comparisons were conducted between young and elderly individuals. Young individuals had significantly higher partial time and frequency HRV indices than elderly individuals, indicating stronger control of parasympathetic regulation. The overall coupling strength of the CRC of young individuals was higher than that of elderly individuals, especially in high-frequency power, which was significantly lower in the elderly group than in the young group, achieving better results than the HRV indices in terms of statistical significance. Further analyses of the time-frequency dynamics of CRC indices revealed that the coupling strength was consistently higher in the high-frequency (HF) band (0.15-0.4 Hz) in young participants compared to elderly individuals. The dynamic CRC between respiration and HRV indices was accessible by integrating the cross-wavelet spectrum and coherence. Young participants had a significantly higher level of CRC in the HF band, indicating that aging reduces vagus nerve modulation.

A Novel Sleep Staging Method Based on EEG and ECG Multimodal Features Combination

Accurate sleep staging evaluates the quality of sleep, supporting the clinical diagnosis and intervention of sleep disorders and related diseases. Although previous attempts to classify sleep stages have achieved high classification performance, little attention has been paid to integrating the rich information in brain and heart dynamics during sleep for sleep staging. In this study, we propose a generalized EEG and ECG multimodal feature combination to classify sleep stages with high efficiency and accuracy. Briefly, a hybrid features combination in terms of multiscale entropy and intrinsic mode function are used to reflect nonlinear dynamics in multichannel EEGs, along with heart rate variability measures over time/frequency domains, and sample entropy across scales are applied for ECGs. For both the max-relevance and min-redundancy method and principal component analysis were used for dimensionality reduction. The selected features were classified by four traditional machine learning classifiers. Macro-F1 score, macro-geometric mean, and Cohen kappa value are adopted to evaluate the classification performance of each class in an imbalanced dataset. Experimental results show that EEG features contribute more to wake stage classification while ECG features contribute more to deep sleep stages. The proposed combination achieves the highest accuracy of 84.3% and the highest kappa value of 0.794 on the support vector machine in the ISRUC-S3 dataset, suggesting the proposed multimodal features combination is promising in accuracy and efficiency compared to other state-of-the-art methods.

A new algorithm for fetal heart rate detection: Fractional order calculus approach

OBJECTIVES

A new modified Pan-Tompkins' (mPT) method for fetal heart rate detection is presented. The mPT method is based on the hypothesis that optimal fractional order derivative and optimal window width of the moving average filter would enable efficient estimation of fetal heart rate from surface abdominal electrophysiological recordings with relatively low signal-to-noise ratios.

METHODS

The algorithm is tested on signals recorded from the abdomen of pregnant women available from the PhysioNet Computing in Cardiology Challenge database. Fetal heart rate detection is performed on 10-s long segments selected by the estimation of signal-to-noise ratios (the extravagance of the fetal QRS peak to its surroundings and to the whole signal; and the mean ratio of fetal and maternal QRS peaks) and on the manually selected segments.

RESULTS

The best results are obtained via criteria based on the extravagance of the fetal QRS peak to its surroundings that reached average sensitivity of 97%, positive predictive value of 97%, error rate of ∼3.5%, and F1 score of 97%. The obtained averaged optimal parameters for mPT are 0.51 for fractional order and 24.5 ms for the window width of the moving average filter.

CONCLUSION

Proposed mPT algorithm showed satisfactory performance for fetal heart rate detection. Further adaptations of the presented mPT method could be used for peak detection in noisy environments in biomedical signal analysis in general.

Brain Complexity Predicts Response to Adrenocorticotropic Hormone in Infantile Epileptic Spasms Syndrome: A Retrospective Study

INTRODUCTION

Infantile epileptic spasms syndrome (IESS) is an age-specific and severe epileptic encephalopathy. Although adrenocorticotropic hormone (ACTH) is currently considered the preferred first-line treatment, it is not always effective and may cause side effects. Therefore, seeking a reliable biomarker to predict the treatment response could benefit clinicians in modifying treatment options.

METHODS

In this study, the complexities of electroencephalogram (EEG) recordings from 15 control subjects and 40 patients with IESS before and after ACTH therapy were retrospectively reviewed using multiscale entropy (MSE). These 40 patients were divided into responders and nonresponders according to their responses to ACTH.

RESULTS

The EEG complexities of the patients with IESS were significantly lower than those of the healthy controls. A favorable response to treatment showed increasing complexity in the γ band but exhibited a reduction in the β/α-frequency band, and again significantly elevated in the δ band, wherein the latter was prominent in the parieto-occipital regions in particular. Greater reduction in complexity was significantly linked with poorer prognosis in general. Occipital EEG complexities in the γ band revealed optimized performance in recognizing response to the treatment, corresponding to the area under the receiver operating characteristic curves as 0.8621, while complexities of the δ band served as a fair predictor of unfavorable outcomes globally.

CONCLUSION

We suggest that optimizing frequency-specific complexities over critical brain regions may be a promising strategy to facilitate predicting treatment response in IESS.

Novel Method for Fetal and Maternal Heart Rate Measurements Using 2-D Ultrasound Color Doppler Flow Images

Fetal heart rate (FHR) and maternal heart rate (MHR) are important indicators of fetal well-being during pregnancy. A common method in clinical examination is to estimate the FHR using the Doppler shift of echoes from umbilical artery blood flow based on an ultrasound pulsed-wave (PW) Doppler technique. Similarly, a sampling gate can be located at the maternal blood flow to measure MHR using PW Doppler. Ultrasound color Doppler flow imaging (CDFI) is one of the most commonly used imaging modes for clinical fetal examinations. Color coding is employed to display the blood flow velocity and direction in color grades according to the Doppler shift. Continuous CDF images contain dynamic changes characteristics of the blood flow. The periodic characteristics can be used to obtain heart rate information. Therefore, here we propose a novel method to measure FHR and MHR simultaneously using CDF images. The proposed method calculates the histogram of color similarity of CDF images to initially extract the periodic characteristics of the CDF image sequence. The histogram of color similarity function is then processed by a bandpass filter and autocorrelation operation to reduce noise and enhance periodicity. Finally, peak detection is performed on the processed signal to obtain the period and estimate the heart rate. The proposed method can measure the FHR and MHR in parallel after selecting two regions containing the umbilical artery and maternal blood flow, respectively. Thus, the method has high computational efficiency. The proposed method was evaluated on a Doppler flow phantom and clinical CDF images and then compared with the PW Doppler method. The correlation analysis and Bland-Altman plots reveal that the proposed method agrees well with the PW Doppler. It is a sanity check method for real-time clinical FHR and MHR measurements.

Non-linear Methods Predominant in Fetal Heart Rate Analysis: A Systematic Review

The analysis of fetal heart rate variability has served as a scientific and diagnostic tool to quantify cardiac activity fluctuations, being good indicators of fetal well-being. Many mathematical analyses were proposed to evaluate fetal heart rate variability. We focused on non-linear analysis based on concepts of chaos, fractality, and complexity: entropies, compression, fractal analysis, and wavelets. These methods have been successfully applied in the signal processing phase and increase knowledge about cardiovascular dynamics in healthy and pathological fetuses. This review summarizes those methods and investigates how non-linear measures are related to each paper's research objectives. Of the 388 articles obtained in the PubMed/Medline database and of the 421 articles in the Web of Science database, 270 articles were included in the review after all exclusion criteria were applied. While approximate entropy is the most used method in classification papers, in signal processing, the most used non-linear method was Daubechies wavelets. The top five primary research objectives covered by the selected papers were detection of signal processing, hypoxia, maturation or gestational age, intrauterine growth restriction, and fetal distress. This review shows that non-linear indices can be used to assess numerous prenatal conditions. However, they are not yet applied in clinical practice due to some critical concerns. Some studies show that the combination of several linear and non-linear indices would be ideal for improving the analysis of the fetus's well-being. Future studies should narrow the research question so a meta-analysis could be performed, probing the indices' performance.

Fetal Heart Rate Detection Using First Derivative of ECG Waveform and Multiple Weighting Functions

Fetal heart rate monitoring using the abdominal electrocardiograph (ECG) is an important topic for the diagnosis of heart defects. Many studies on fetal heart rate detection have been presented, however, their accuracy is still unsatisfactory. That is because the fetal ECG waveform is contaminated by maternal ECG interference, muscle contractions, and motion artifacts. One of the conventional methods is to detect the R-peaks from the integrated power of the frequency corresponding to the fetal heartbeats. However, the detection accuracy of the R-peaks is not enough. In this paper, we propose a method to generate the candidates of R-peaks using the first derivative of the signal and to pick up the estimated heartbeats by a multiple weighting function. The proposed multiple weighting function is designed by the Gaussian distribution, of which parameters are set from a grid search with the goal of minimizing the standard deviation of RR intervals (neighboring R-peaks intervals). The validation for the proposed framework has been evaluated on real-world data, which got the better accuracy than the conventional method that detects R-peaks from the integrated power and uses the weighting function produced by a fixed parameter of Gaussian distribution [12]. The averaged absolute error (AAE) which compares the estimated fetal heart rate and the reference fetal heart rate has been decreased by 17.528 bpm.

The Value of Heart Rhythm Complexity in Identifying High-Risk Pulmonary Hypertension Patients

Pulmonary hypertension (PH) is a fatal disease—even with state-of-the-art medical treatment. Non-invasive clinical tools for risk stratification are still lacking. The aim of this study was to investigate the clinical utility of heart rhythm complexity in risk stratification for PH patients. We prospectively enrolled 54 PH patients, including 20 high-risk patients (group A; defined as WHO functional class IV or class III with severely compromised hemodynamics), and 34 low-risk patients (group B). Both linear and non-linear heart rate variability (HRV) variables, including detrended fluctuation analysis (DFA) and multiscale entropy (MSE), were analyzed. In linear and non-linear HRV analysis, low frequency and high frequency ratio, DFAα1, MSE slope 5, scale 5, and area 6–20 were significantly lower in group A. Among all HRV variables, MSE scale 5 (AUC: 0.758) had the best predictive power to discriminate the two groups. In multivariable analysis, MSE scale 5 (p = 0.010) was the only significantly predictor of severe PH in all HRV variables. In conclusion, the patients with severe PH had worse heart rhythm complexity. MSE parameters, especially scale 5, can help to identify high-risk PH patients.

Waveform changes with the evolution of beta bursts in the human subthalamic nucleus

OBJECTIVE

Phasic bursts of beta band synchronisation have been linked to motor impairment in Parkinson's disease (PD). However, little is known about what terminates bursts.

METHODS

We used the Hilbert-Huang transform to investigate beta bursts in the local field potential recorded from the subthalamic nucleus in nine patients with PD on and off levodopa.

RESULTS

The sharpness of the beta waveform extrema fell as burst amplitude dropped. Conversely, an index of phase slips between waveform extrema, and the power of concurrent theta activity increased as burst amplitude fell. Theta activity was also increased on levodopa when beta bursts were attenuated. These phenomena were associated with reduction in coupling between beta phase and high gamma activity amplitude. We discuss how these findings may suggest that beta burst termination is associated with relative desynchronization of the beta drive, increase in competing theta activity and increased phase slips in the beta activity.

CONCLUSIONS

We characterise the dynamical nature of beta bursts, thereby permitting inferences about underlying activities and, in particular, about why bursts terminate.

SIGNIFICANCE

Understanding the dynamical nature of beta bursts may help point to interventions that can cause their termination and potentially treat motor impairment in PD.

The critical role of respiratory sinus arrhythmia on temporal cardiac dynamics

Temporal cardiac properties provide alternative information in analyzing heart rate variability (HRV), which may be disregarded by the standard HRV analyses. Patients with congestive heart failure (CHF) are known to have distinct temporal features from the healthy individuals. However, the underlying mechanism leading to the variation remains unclear. Whether or not these parameters can finely classify the severity for CHF patients is uncertain as well. In this work, an electrocardiogram was monitored in advanced CHF patients using 24-h Holter in four conditions, including baseline, one and three months after atenolol therapy, and healthy individuals. Slope and area under the curve (AUC) of multiscale entropy (MSE) curve over short (scales 1-5) and long (scales 6-20) scales, and detrended fluctuation analysis (DFA) scaling exponents at short (4-11 beats) and intermediate (>11 beats) window sizes were calculated. The results show that short-time scale MSE-derived parameters (slope: -0.08 ± 0.10, -0.03 ± 0.10, 0.02 ± 0.06, 0.08 ± 0.06; AUC: 4.03 ± 2.11, 4.69 ± 1.28, 4.73 ± 0.94, and 6.17 ± 1.23) and short-time scale DFA exponent (0.79 ± 0.16, 0.95 ± 0.22, 1.11 ± 0.19, and 1.35 ± 0.20) can hierarchically classify all four conditions. More importantly, simulated R-R intervals with different fractions and amplitude of respiratory sinus arrhythmia (RSA) components were examined to validate our hypothesis regarding the essentiality of RSA in the improvement of cardiovascular function, and its tight association with unpredictability and fractal property of HRV, which is in line with our hypothesis that RSA contributes significantly to the generation of the unpredictability and fractal behavior of HR dynamics.NEW & NOTEWORTHY Temporal cardiac properties provide useful diagnostic parameters for patients with congestive heart failure (CHF). Our study hierarchically classified CHF patients with β-blocker treatment by using multiscale entropy and detrended fluctuation analysis. Also, we provided the evidence to validate the critical role of respiratory sinus arrhythmia in the fractal properties of heart rate variability.