Multiscale Entropy Analysis of Heart Rate Variability in Neonatal Patients with and without Seizures

Prediction models for lymph node metastasis in cervical cancer based on preoperative heart rate variability

Background

The occurrence of lymph node metastasis (LNM) is one of the critical factors in determining the staging, treatment and prognosis of cervical cancer (CC). Heart rate variability (HRV) is associated with LNM in patients with CC. The purpose of this study was to validate the feasibility of machine learning (ML) models constructed with preoperative HRV as a feature of CC patients in predicting CC LNM.

Methods

A total of 292 patients with pathologically confirmed CC admitted to the Department of Gynecological Oncology of the First Affiliated Hospital of Bengbu Medical University from November 2020 to September 2023 were included in the study. The patient' preoperative 5-min electrocardiogram data were collected, and HRV time-domain, frequency-domain and non-linear analyses were subsequently performed, and six ML models were constructed based on 32 parameters. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Results

Among the 6 ML models, the random forest (RF) model showed the best predictive performance, as specified by the following metrics on the test set: AUC (0.852), accuracy (0.744), sensitivity (0.783), and specificity (0.785).

Conclusion

The RF model built with preoperative HRV parameters showed superior performance in CC LNM prediction, but multicenter studies with larger datasets are needed to validate our findings, and the physiopathological mechanisms between HRV and CC LNM need to be further explored.

Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review

The most critical burden for People with Epilepsy (PwE) is represented by seizures, the unpredictability of which severely impacts quality of life. The design of real-time warning systems that can detect or even predict ictal events would enhance seizure management, leading to high benefits for PwE and their caregivers. In the past, various research works highlighted that seizure onset is anticipated by significant changes in autonomic cardiac control, which can be assessed through heart rate variability (HRV). This manuscript conducted a scoping review of the literature analyzing HRV-based methods for detecting or predicting ictal events. An initial search on the PubMed database returned 402 papers, 72 of which met the inclusion criteria and were included in the review. These results suggest that seizure detection is more accurate in neonatal and pediatric patients due to more significant autonomic modifications during the ictal transitions. In addition, conventional metrics are often incapable of capturing cardiac autonomic variations and should be replaced with more advanced methodologies, considering non-linear HRV features and machine learning tools for processing them. Finally, studies investigating wearable systems for heart monitoring denoted how HRV constitutes an efficient biomarker for seizure detection in patients presenting significant alterations in autonomic cardiac control during ictal events.

Artificial Intelligence for Personalized Genetics and New Drug Development: Benefits and Cautions

As the global health care system grapples with steadily rising costs, increasing numbers of admissions, and the chronic defection of doctors and nurses from the profession, appropriate measures need to be put in place to reverse this course before it is too late [...].

Advances in Multivariate and Multiscale Physiological Signal Analysis

Physiological systems are characterized by complex dynamics and nonlinear behaviors due to their intricate structural organization and regulatory mechanisms [...].

Multiscale entropy as a metric of brain maturation in a large cohort of typically developing children born preterm using longitudinal high-density EEG in the first two years of life

Objective.We aimed to analyze whether complexity of brain electrical activity (EEG) measured by multiscale entropy (MSE) increases with brain maturation during the first two years of life. We also aimed to investigate whether this complexity shows regional differences across the brain, and whether changes in complexity are influenced by extrauterine life experience duration.Approach.We measured MSE of EEG signals recorded longitudinally using a high-density setup (64 or 128 electrodes) in 84 typically developing infants born preterm (<32 weeks' gestation) from term age to two years. We analyzed the complexity index and maximum value of MSE over increasing age, across brain regions, and in function of extrauterine life duration, and used correlation matrices as a metric of functional connectivity of the cerebral cortex.Main results.We found an increase of strong inter-channel correlation of MSE (R > 0.8) with increasing age. Regional analysis showed significantly increased MSE between 3 and 24 months of corrected age in the posterior and middle regions with respect to the anterior region. We found a weak relationship (adjusted R²= 0.135) between MSE and extrauterine life duration.Significance.These findings suggest that brain functional connectivity increases with maturation during the first two years of life. EEG complexity shows regional differences with earlier maturation of the visual cortex and brain regions involved in joint attention than of regions involved in cognitive analysis, abstract thought, and social behavior regulation. Finally, our MSE analysis suggested only a weak influence of early extrauterine life experiences (prior to term age) on EEG complexity.

Improving the Accuracy of R-Peak Detection in a Wearable Armband Device for Daily Life Electrocardiogram Monitoring Using a Deep Convolutional Denoising Encoder-Decoder Network

Continuous long-term heart rate (HR) monitoring using wearable devices is desirable to aid in the diagnosis of many health-related conditions. Recently, we have developed an armband device that does not use obstructive leads, has dry electrodes which are convenient for long-term electrocardiogram (ECG) recording, and has been shown to be an effective alternate approach for continuous ECG monitoring. However, motion artifacts (MA) due to electromyogram (EMG) contractions are acknowledged as the major challenge of an armband. In this study, we used a deep convolutional neural network denoising encoder-decoder (CNNDED) to enhance the accuracy of R-peak detection in MA-corrupted ECG recordings obtained by an armband device. We collected simultaneous 24-hour ECG recordings using both the armband device and a Holter monitor on 10 subjects. Each 10-sec ECG segment was converted to a time-frequency representation and subsequently used as the input to CNNDED. During the training process, the model learned to accentuate the location of R peaks by amplifying their values in each ECG beat and suppressing the remaining waveforms. For the training output, the model used the R-peak location information from the simultaneously collected Holter ECG data, which were considered as the reference. The performance of CNNDED was evaluated on an independent test data set using the standard performance metrics. The mean relative error of the estimated HR with respect to the Holter data was 17.5 and 7.3 beats/min, pre- and post-CNNDED, respectively. The mean relative difference of the root mean square of successive difference values were 0.23 and 0.06 before and after applying CNNDED, respectively. Although further study is needed, the current preliminary results suggest that CNNDED can improve detection of R peaks even when they are completely buried in the presence of EMG artifacts.

Heart Rate Variability Analysis for Seizure Detection in Neonatal Intensive Care Units

In Neonatal Intensive Care Units (NICUs), the early detection of neonatal seizures is of utmost importance for a timely clinical intervention. Over the years, several neonatal seizure detection systems were proposed to detect neonatal seizures automatically and speed up seizure diagnosis, most based on the EEG signal analysis. Recently, research has focused on other possible seizure markers, such as electrocardiography (ECG). This work proposes an ECG-based NSD system to investigate the usefulness of heart rate variability (HRV) analysis to detect neonatal seizures in the NICUs. HRV analysis is performed considering time-domain, frequency-domain, entropy and multiscale entropy features. The performance is evaluated on a dataset of ECG signals from 51 full-term babies, 29 seizure-free. The proposed system gives results comparable to those reported in the literature: Area Under the Receiver Operating Characteristic Curve = 62%, Sensitivity = 47%, Specificity = 67%. Moreover, the system’s performance is evaluated in a real clinical environment, inevitably affected by several artefacts. To the best of our knowledge, our study proposes for the first time a multi-feature ECG-based NSD system that also offers a comparative analysis between babies suffering from seizures and seizure-free ones.

Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals

In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate (FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing. Despite the availability of several approaches to analyze the variability of FHR signals (namely the FHRV), there are still shadows hindering a comprehensive understanding of how linear and nonlinear dynamics are involved in the control of the fetal heart rhythm. In this study, we propose a straightforward processing and modeling route for a deeper understanding of the relationships between the characteristics of the FHR signal. A multiparametric modeling and investigation of the factors influencing the FHR accelerations, chosen as major indicator of fetal wellbeing, is carried out by means of linear and nonlinear techniques, blockwise dimension reduction, and artificial neural networks. The obtained results show that linear features are more influential compared to nonlinear ones in the modeling of HRV in healthy fetuses. In addition, the results suggest that the investigation of nonlinear dynamics and the use of predictive tools in the field of FHRV should be undertaken carefully and limited to defined pregnancy periods and FHR mean values to provide interpretable and reliable information to clinicians and researchers.