Search Results (25)

Enterovirus (EV) infections in neonates can be transmitted vertically or horizontally, with symptoms ranging from mild to severe, including myocarditis, meningoencephalitis, and hepatitis. Neonates with EV-induced myocarditis may present severe cardiovascular disease with sudden onset of arrhythmia. Neonatal arrhythmias, particularly in low birth weight or critically ill infants, can impair cardiac function and worsen outcomes. EV targets cardiomyocyte receptors, inducing apoptosis pathways and triggering cardiac conduction disturbances. We present an extremely low-birth-weight preterm infant (GW 27 + 6) who developed EV-induced myocarditis, complicated with a sudden onset of supraventricular tachycardia (SVT), pericardial effusion and bi-atrial enlargement. Despite multi-agent regimen, including propranolol, flecainide, and amiodarone, the infant showed persistent junctional rhythm until seven months of age, later transitioning to atrial rhythm with stable cardiac function. A review of previously published rhythm disturbances due to EV-induced myocarditis is presented. Newborns with EV-induced arrhythmia may require a multi-modal treatment such as a multi-agent medical regimen or, in severe non-responsive cases, an electrophysiological approach. EV infections may cause long-term cardiovascular comorbidities (such as left ventricular dysfunction or mitral valve regurgitation), necessitating continuous monitoring through echocardiography and ECG. Collaboration between neonatologists and pediatric cardiologists is crucial for effective treatment and follow-up. Full article

(1) Background: Sudden Infant Death Syndrome (SIDS) represents sudden and unexplained deaths during the sleep of infants under one year of age, despite thorough investigation. Screening for a prolonged QTc interval, a marker for Long QT Syndrome (LQTS), should be conducted on all newborns to reduce the incidence of SIDS. Neonatal electrocardiograms (ECGs) could identify congenital heart defects (CHDs) early, especially those not detected at birth. Infants with prolonged QTc intervals typically undergo genetic analysis for Long QT Syndrome. (2) Methods: The study involved infants aged 20–40 days, born with no apparent clinical signs of heart disease, with initial ECG screening. Infants with prenatal diagnoses or signs/symptoms of CHDs identified immediately after birth, as well as infants who had previously had an ECG or echocardiogram for other medical reasons, were excluded from the study. We used statistical software (SPSS version 22.0) to analyze the data. (3) Results: Of the 42,200 infants involved, 2245 were enrolled, with 39.9% being males. Following this initial screening, 164 children (37.8% males) with prolonged QTc intervals underwent further evaluation. Out of these 164 children, 27 children were confirmed to have LQTS. However, only 18 children were finally investigated for genetic mutations, and mutations were identified in 11 tests. The most common mutations were LQT1 (54.5%), LQT2 (36.4%), and LQT3 (1 patient). Treatment options included propranolol (39.8%), nadolol (22.2%), inderal (11.1%), metoprolol (11.1%), and no treatment (16.7%). The most common abnormalities were focal right bundle branch block (54.5%), left axis deviation (9.2%), and nonspecific ventricular repolarization abnormalities (7.1%). Multiple anomalies were found in 0.47% of children with focal right bundle branch block. Structural abnormalities were associated with specific features in 267 patients (11.9%), primarily isolated patent foramen ovale (PFO) at 61.4%. (4) Conclusions: This screening approach has demonstrated effectiveness in the early identification of LQTS and other cardiac rhythm anomalies, with additional identification of mutations and/or prolonged QTc intervals in family members. Identifying other ECG abnormalities and congenital heart malformations further enhances the benefits of the screening. Full article

Infant electrocardiograms (ECGs) and heart rates (HRs) are very useful biosignals for psychological research and clinical work, but can be hard to analyse properly, particularly longform (≥5 min) recordings taken in naturalistic environments. Infant HRs are typically much faster than adult HRs, and so some of the underlying frequency assumptions made about adult ECGs may not hold for infants. However, the bulk of publicly available ECG approaches focus on adult data. Here, existing open source ECG approaches are tested on infant datasets. The best-performing open source method is then modified to maximise its performance on infant data (e.g., including a 15 Hz high-pass filter, adding local peak correction). The HR signal is then subsequently analysed, developing an approach for cleaning data with separate sets of parameters for the analysis of cleaner and noisier HRs. A Signal Quality Index (SQI) for HR is also developed, providing insights into where a signal is recoverable and where it is not, allowing for more confidence in the analysis performed on naturalistic recordings. The tools developed and reported in this paper provide a base for the future analysis of infant ECGs and related biophysical characteristics. Of particular importance, the proposed solutions outlined here can be efficiently applied to real-world, large datasets. Full article

Across five studies, we present the preliminary technical validation of an infant-wearable platform, LittleBeats™, that integrates electrocardiogram (ECG), inertial measurement unit (IMU), and audio sensors. Each sensor modality is validated against data from gold-standard equipment using established algorithms and laboratory tasks. Interbeat interval (IBI) data obtained from the LittleBeats™ ECG sensor indicate acceptable mean absolute percent error rates for both adults (Study 1, N = 16) and infants (Study 2, N = 5) across low- and high-challenge sessions and expected patterns of change in respiratory sinus arrythmia (RSA). For automated activity recognition (upright vs. walk vs. glide vs. squat) using accelerometer data from the LittleBeats™ IMU (Study 3, N = 12 adults), performance was good to excellent, with smartphone (industry standard) data outperforming LittleBeats™ by less than 4 percentage points. Speech emotion recognition (Study 4, N = 8 adults) applied to LittleBeats™ versus smartphone audio data indicated a comparable performance, with no significant difference in error rates. On an automatic speech recognition task (Study 5, N = 12 adults), the best performing algorithm yielded relatively low word error rates, although LittleBeats™ (4.16%) versus smartphone (2.73%) error rates were somewhat higher. Together, these validation studies indicate that LittleBeats™ sensors yield a data quality that is largely comparable to those obtained from gold-standard devices and established protocols used in prior research. Full article

Techniques for noninvasively acquiring the vital information of infants and young children are considered very useful in the fields of healthcare and medical care. An unobstructive measurement method for sleeping infants and young children under the age of 6 years using a sheet-type vital sensor with a polyvinylidene fluoride (PVDF) pressure-sensitive layer is demonstrated. The signal filter conditions to obtain the ballistocardiogram (BCG) and phonocardiogram (PCG) are discussed from the waveform data of infants and young children. The difference in signal processing conditions was caused by the physique of the infants and young children. The peak-to-peak interval (PPI) extracted from the BCG or PCG during sleep showed an extremely high correlation with the R-to-R interval (RRI) extracted from the electrocardiogram (ECG). The vital changes until awakening in infants monitored using a sheet sensor were also investigated. In infants under one year of age that awakened spontaneously, the distinctive vital changes during awakening were observed. Understanding the changes in the heartbeat and respiration signs of infants and young children during sleep is essential for improving the accuracy of abnormality detection by unobstructive sensors. Full article

The classification of sleep state in preterm infants, particularly in distinguishing between active sleep (AS) and quiet sleep (QS), has been investigated using cardiorespiratory information such as electrocardiography (ECG) and respiratory signals. However, accurately differentiating between AS and wake remains challenging; therefore, there is a pressing need to include additional information to further enhance the classification performance. To address the challenge, this study explores the effectiveness of incorporating video-based actigraphy analysis alongside cardiorespiratory signals for classifying the sleep states of preterm infants. The study enrolled eight preterm infants, and a total of 91 features were extracted from ECG, respiratory signals, and video-based actigraphy. By employing an extremely randomized trees (ET) algorithm and leave-one-subject-out cross-validation, a kappa score of 0.33 was achieved for the classification of AS, QS, and wake using cardiorespiratory features only. The kappa score significantly improved to 0.39 when incorporating eight video-based actigraphy features. Furthermore, the classification performance of AS and wake also improved, showing a kappa score increase of 0.21. These suggest that combining video-based actigraphy with cardiorespiratory signals can potentially enhance the performance of sleep-state classification in preterm infants. In addition, we highlighted the distinct strengths and limitations of video-based actigraphy and cardiorespiratory data in classifying specific sleep states. Full article

There have been sustained efforts toward using naturalistic methods in developmental science to measure infant behaviors in the real world from an egocentric perspective because statistical regularities in the environment can shape and be shaped by the developing infant. However, there is no user-friendly and unobtrusive technology to densely and reliably sample life in the wild. To address this gap, we present the design, implementation and validation of the EgoActive platform, which addresses limitations of existing wearable technologies for developmental research. EgoActive records the active infants’ egocentric perspective of the world via a miniature wireless head-mounted camera concurrently with their physiological responses to this input via a lightweight, wireless ECG/acceleration sensor. We also provide software tools to facilitate data analyses. Our validation studies showed that the cameras and body sensors performed well. Families also reported that the platform was comfortable, easy to use and operate, and did not interfere with daily activities. The synchronized multimodal data from the EgoActive platform can help tease apart complex processes that are important for child development to further our understanding of areas ranging from executive function to emotion processing and social learning. Full article

Background. It has been known for decades that breastfeeding leads to a lower risk of asthma, respiratory infections, or metabolic syndrome at school age. In addition, evidence is now accumulating on the influence of breast milk on the shape, volume, or function of the heart and lungs. Within this field of research into the effects of breast milk on the structure of the heart and lungs, we have set out to analyze the differential electrocardiographic characteristics of schoolchildren who were once breastfed. Method. This was an observational cross-sectional study, including 138 children aged 6 or 12 consecutively presenting to a well-child clinic between May and December 2022. Inclusion criteria. The ability to perform reproducible ECG records, the feasibility of weighing and measuring patient, and breastfeeding data collected from birth were used as the inclusion criteria. Results. Using the 40° cut-off value for the mean P-wave axis among schoolchildren, 76% of never-breastfed children in our sample have a P-wave axis in a more vertical position than the mean as compared to 58% of ever-breastfed children (OR: 2.25; 95% CI: 3.13–1.36); there was no other significant difference between infant feeding groups in somatometric characteristics or ECG parameters. Conclusion. We found a significant difference of the mean values of the P-wave axis between never- and ever-breastfed children. Although this report should be approached cautiously, these findings add to the renewed interest in discerning developmental interventions to improve cardiovascular health. Full article

In recent years, there has been a rise in the prevalence of autism spectrum disorder (ASD). The diagnosis of ASD requires behavioral observation and standardized testing completed by highly trained experts. Early intervention for ASD can begin as early as 1–2 years of age, but ASD diagnoses are not typically made until ages 2–5 years, thus delaying the start of intervention. There is an urgent need for non-invasive biomarkers to detect ASD in infancy. While previous research using physiological recordings has focused on brain-based biomarkers of ASD, this study investigated the potential of electrocardiogram (ECG) recordings as an ASD biomarker in 3–6-month-old infants. We recorded the heart activity of infants at typical and elevated familial likelihood for ASD during naturalistic interactions with objects and caregivers. After obtaining the ECG signals, features such as heart rate variability (HRV) and sympathetic and parasympathetic activities were extracted. Then we evaluated the effectiveness of multiple machine learning classifiers for classifying ASD likelihood. Our findings support our hypothesis that infant ECG signals contain important information about ASD familial likelihood. Amongthe various machine learning algorithms tested, KNN performed best according to sensitivity (0.70 ± 0.117), F1-score (0.689 ± 0.124), precision (0.717 ± 0.128), accuracy (0.70 ± 0.117, p-value = 0.02), and ROC (0.686 ± 0.122, p-value = 0.06). These results suggest that ECG signals contain relevant information about the likelihood of an infant developing ASD. Future studies should consider the potential of information contained in ECG, and other indices of autonomic control, for the development of biomarkers of ASD in infancy. Full article

Background: Repeated fetal heart rates (FHR) < 3rd percentile for gestational age (GA) with 1:1 atrioventricular conduction (sinus bradycardia) can be a marker for long QT syndrome. We hypothesized that other inherited arrhythmia syndromes might present with fetal sinus bradycardia. Methods: We reviewed pregnancies referred with sinus bradycardia to the Colorado Fetal Care Center between 2013 and 2023. FHR/GA data, family history, medication exposure, normalized isovolumic contraction times (n-IVRT), postnatal genetic testing, and ECGs at 4–6 weeks after birth were reviewed. Results: Twenty-nine bradycardic subjects were evaluated by fetal echocardiography. Five were lost to follow-up, one refused genetic testing, and one had negative genetic testing for any inherited arrhythmia. Six had non-genetic causes of fetal bradycardia with normal prenatal n-IVRT and postnatal QTc. Thirteen carried pathogenic variants in RYR2 (n = 2), HCN4 (n = 2), KCNQ1 (6), and other LQTS genes (n = 4). The postnatal QTc was <470 ms in subjects with RYR2, HCN4, and two of those with KCNQ1 mutations, and >470 ms in subjects with CALM 2, KCNH2, SCN5A, and four of those with KCNQ1 mutations. LQTS and RYR2 mutations were associated with prolonged n-IVRT, but HCN4 was not. Two fetuses died in utero with variants of uncertain significance (CACNA1 and KCNE1). Cascade testing uncovered six affected but undiagnosed parents and confirmed familial inheritance in five. Conclusion: In addition to heralding LQTS, repeated FHR < 3rd percentile for GA is a risk factor for other inherited arrhythmia syndromes. These findings suggest that genetic testing should be offered to infants with a history of FHR < 3rd percentile for GA even if the postnatal ECG demonstrates a normal QTc interval. Full article

Neonatal seizure is an important clinical symptom of brain dysfunction, which is more common in infancy than in childhood. At present, video electroencephalogram (VEEG) technology is widely used in clinical practice. However, video electroencephalogram technology has several disadvantages. For example, the wires connecting the medical instruments may interfere with the infant’s movement and the gel patch electrode or disk electrode commonly used to monitor EEG may cause skin allergies or even tears. For the above reasons, we developed a wearable multi-sensor platform for newborns to collect physiological and movement signals. In this study, we designed a second-generation multi-sensor platform and developed an automatic detection algorithm for neonatal seizures based on ECG, respiration and acceleration. Data for 38 neonates were recorded at the Children’s Hospital of Fudan University in Shanghai. The total recording time was approximately 300 h. Four of the patients had seizures during data collection. The total recording time for the four patients was approximately 34 h, with 30 seizure episodes recorded. These data were evaluated by the algorithm. To evaluate the effectiveness of combining ECG, respiration and movement, we compared the performance of three types of seizure detectors. The first detector included features from ECG, respiration and acceleration records; the second detector incorporated features based on respiratory movement from respiration and acceleration records; and the third detector used only ECG-based features from ECG records. Our study illustrated that, compared with the detector utilizing individual modal features, multi-modal feature detectors could achieve favorable overall performance, reduce false alarm rates and give higher F-measures. Full article

Arrhythmia is one of the causes of sudden infant death, and it is very important to detect fetal arrhythmia for fetal well-being. Fetal electrocardiogram (FECG) is one of the methods to detect a heartbeat. Fetal arrhythmia can be detected based on the heartbeat detection results from FECG signals such as heartbeat intervals. However, the accuracy of arrhythmia detection easily degrades depending on the accuracy of heartbeat detection. In this paper, we propose a deep learning-based fetal arrhythmia detection method using FECG signals. Recently, arrhythmia detection methods using adult ECG signals have achieved a high arrhythmia detection accuracy based on deep learning. Motivated by this fact, in the proposed method, the acquired FECG signals are segmented, and the segments are input into a deep learning model that classifies them into normal or arrhythmia ones. Based on the classification results of multiple segments, a subject is judged as a healthy or arrhythmia subject. Each segment of the training data is divided into three categories based on the estimated heartbeat interval: (i) normal, (ii) arrhythmia, and (iii) a segment that could be both normal and arrhythmic. Only segments labeled as normal or arrhythmia are used for training a deep learning model to achieve a higher classification accuracy of the model. Through these procedures, the proposed method detects fetal arrhythmia with fewer effects of heartbeat detection results. The experimental results show that the proposed method achieves 96.2% accuracy, 100% specificity, and 100% recall, improving the values of conventional methods based on heartbeat detection and feature detection. Full article

For the precise preoperative evaluation of complex congenital heart diseases (CHDs) with reduced radiation dose exposure, we assessed the diagnostic validity and reliability of low-dose prospective ECG-gated cardiac CT (CCT). Forty-two individuals with complex CHDs who underwent preoperative CCT as part of a prospective study were included. Each CCT image was examined independently by two radiologists. The primary reference for assessing the diagnostic validity of the CCT was the post-operative data. Infants and neonates were the most common age group suffering from complex CHDs. The mean volume of the CT dose index was 1.44 ± 0.47 mGy, the mean value of the dose-length product was 14.13 ± 5.4 mGy*cm, and the mean value of the effective radiation dose was 0.58 ± 0.13 mSv. The sensitivity, specificity, PPV, NPV, and accuracy of the low-dose prospective ECG-gated CCT for identifying complex CHDs were 95.6%, 98%, 97%, 97%, and 97% for reader 1 and 92.6%, 97%, 95.5%, 95.1%, and 95.2% for reader 2, respectively. The overall inter-reader agreement for interpreting the cardiac CCTs was good (κ = 0.74). According to the results of our investigation, low-dose prospective ECG-gated CCT is a useful and trustworthy method for assessing coronary arteries and making a precise preoperative diagnosis of complex CHDs. Full article

Early identification of infants at risk of neurodevelopmental delay is an essential public health aim. Such a diagnosis allows early interventions for infants that maximally take advantage of the neural plasticity in the developing brain. Using standardized physiological developmental tests, such as the assessment of neurophysiological response to environmental events using cardiac orienting responses (CORs), is a promising and effective approach for early recognition of neurodevelopmental delay. Previous CORs have been collected on children using large bulky equipment that would not be feasible for widespread screening in routine clinical visits. We developed a portable wireless electrocardiogram (ECG) system along with a custom application for IOS tablets that, in tandem, can extract CORs with sufficient physiologic and timing accuracy to reflect the well-characterized ECG response to both auditory and visual stimuli. The sensor described here serves as an initial step in determining the extent to which COR tools are cost-effective for the early screening of children to determine who is at risk of developing neurocognitive deficits and may benefit from early interventions. We demonstrated that our approach, based on a wireless heartbeat sensor system and a custom mobile application for stimulus display and data recording, is sufficient to capture CORs from infants. The COR monitoring approach described here with mobile technology is an example of a desired standardized physiologic assessment that is a cost-and-time efficient, scalable method for early recognition of neurodevelopmental delay. Full article

A field-programmable gate array (FPGA) based system for digital filtering in a neonatal heart rate monitoring system is presented. The system employs electric potential sensors (EPS) and contains a single hardware filter stage for antialiasing. The remaining digital signal processing required to provide a clinical standard electrocardiogram (ECG) is performed on the FPGA (myRIO 1900, National Instruments Corporation of Austin, Austin, TX, USA). This is compared with a previous microprocessor version (Raspberry Pi 3, BCM2837 processor, Raspberry Pi Ltd, Cambridge, UK) containing a dual hardware/software filtering scheme, with the aim of simplifying the analog front end and allowing for reconfigurable filtering in the digital domain. A custom neonate phantom was employed to emulate real world conditions and ambient noise. The developed FPGA system was shown to have a signal quality comparable with the microprocessor implementation, with an average signal-to-noise ratio loss of 2%. A 12 dB increase in the attenuation of the predominant 50 Hz noise was shown, indicating filter effectiveness gains. The phantom was used to broadcast data from the preterm infant cardio-respiratory signals database (PICSDB) and the FPGA filtering scheme was shown to remove the majority of the ambient 50 Hz noise with an average reduction of 30 dB, and provided a clean ECG signal. These results demonstrate that FPGA-filtered EPS ECGs have comparable signal quality to the combined HW/SW filtering implementation, with a reduction in complexity and power consumption. Full article