Abstract
Background and Objectives: Prematurity of birth occurs before the 37th week of gestation and affects up to 10% of births worldwide. It is correlated with critical outcomes; therefore, constant monitoring in neonatal intensive care units or home environments is required. The aim of this work was to develop solutions for remote neonatal intensive supervision systems, which should assist medical diagnosis of premature infants and raise alarm at cardiac abnormalities, such as bradycardia. Additionally, the COVID-19 pandemic has put a worldwide stress upon the medical staff and the management of healthcare units. Materials and Methods: A traditional medical diagnosing scheme was set up, implemented with the aid of powerful mathematical operators. The algorithm was tailored to the infants’ personal ECG characteristics and was tested on real ECG data from the publicly available PhysioNet database “Preterm Infant Cardio-Respiratory Signals Database”. Different processing problems were solved: noise filtering, baseline drift removal, event detection and compression of medical data using the à trous wavelet transform. Results: In all 10 available clinical cases, the bradycardia events annotated by the physicians were correctly detected using the RR intervals. Compressing the ECG signals for remote transmission, we obtained compression ratios (CR) varying from 1.72 to 7.42, with the median CR value around 3. Conclusions: We noticed that a significant amount of noise can be added to a signal while monitoring using standard clinical sensors. We tried to offer solutions for these technical problems. Recent studies have shown that persons infected with the COVID-19 disease are frequently reported to develop cardiovascular symptoms and cardiac arrhythmias. An automatic surveillance system (both for neonates and adults) has a practical medical application. The proposed algorithm is personalized, no fixed reference value being applied, and the algorithm follows the neonate’s cardiac rhythm changes. The performance depends on the characteristics of the input ECG. The signal-to-noise ratio of the processed ECG was improved, with a value of up to 10 dB.
Highlights
30 s:1 h 32 min 30 s) was taken to check two clinical aspects (Figure 4): whether the baseline drift removal algorithm is efficient on segments with pronounced drift and whether the mentioned algorithm was influencing the detection of bradycardia segments which occurred on noisy ECG segments
The bradycardia alarm given through the proposed method coincided with the annotated bradycardia episodes on the PhysioNet database method coincided with the annotated bradycardia episodes on the PhysioNet database
The bradycardia alarm given through the proposed method coincided with the annotated bradycardia episodes on the PhysioNet database [52]
Summary
Born infants and neonates with low birth weight are associated with an immature cardio-respiratory system and an immature immune system, being at greater risk of developing severe infections [4,5,6]. The COVID-19 pandemic with the associated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) puts some stress on both mother and child, with many questions still being left about the future development of the newborn. The psychological stress upon the mother should not be neglected [7,8], as several studies have associated SARS infections during pregnancy with preterm delivery and intrauterine growth restrictions [9,10,11]. The pandemic is reported to affect the work of neonatal intensive care unit (NICUs) [12]. One in four of the children who were hospitalized needed intensive care.” [13]): lately (19 October 2021), Romania has reported a record number of COVID infections of 18,863 cases [14], with 486 infected minors
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