Robust, real-time generic detector based on a multi-feature probabilistic method

Optimized CNN-based denoising strategy for enhancing longitudinal monitoring of heart failure

Cardiac vibration signal analysis emerges as a remarkable tool for the diagnosis of heart conditions. Our recent study shows the feasibility of the longitudinal monitoring of chronic heart diseases, particularly heart failure, using a gastric fundus implant. However, cardiac vibration data, captured from the implant, positioned at the gastric fundus, can be highly affected by different noises and artefacts. This study introduces a novel methodology for addressing denoising challenges in the longitudinal monitoring of chronic heart diseases, using gastric fundus implants. More precisely, a novel method is designed, by repurposing pre-trained convolutional neural network models, originally designed for classification tasks, with adequately chosen convolution filters. The proposed approach efficiently tackles noise and artefacts reduction in the acquired accelerometer signals. Moreover, the integration of additional Hilbert and Homomorphic envelopes enhances the implant's ability to better segment heart sounds, namely S1 and S2. The quality assessment of this denoising strategy is performed, in the lack of ground truth, by rather evaluating its impact on a classification stage that is introduced to the proposed pipeline. Compared to standard denoising matrix factorization and tensor decomposition-based methods, results on a real 3D accelerometer dataset acquired from a set of pigs, with and without heart failure, demonstrate the efficacy of such a proposed optimized CNN-based approach with the best balance between enhancing the segmentation accuracy and preserving a maximum usable record.

Impact of a music intervention on heart rate variability in very preterm infants

AIM

Infants born very preterm spend their early postnatal life in a neonatal intensive care unit, where irregular and unpredictable sounds replace the structured and familiar intrauterine auditory environment. Music interventions may contribute to alleviate these deleterious effects by reducing stress and providing a form of environmental enrichment.

MATERIAL AND METHODS

This was an ancillary study as part of a blinded randomised controlled clinical trial entitled the effect of music on preterm infant's brain development. It measured the impact of music listening on the autonomic nervous system (ANS), we assessed heart rate variability (HRV) through high-resolution recordings of heart rate monitoring, at three specific postmenstrual ages in premature infants.

RESULTS

From 29 included subjects, 18 were assessed for complete HRV dataset, including nine assigned to the music intervention and nine to the control group. Postmenstrual age appeared to be the main factor influencing HRV from 33 weeks to term equivalent age. Further analyses did not reveal any detectable effect of music intervention on ANS response.

CONCLUSION

This study found that ANS responses were not modified by recorded music intervention in very preterm infants during wakefulness or sleep onset. Further research is warranted to explore other factors influencing ANS development in this population.

Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review

BACKGROUND

Computational signal preprocessing is a prerequisite for developing data-driven predictive models for clinical decision support. Thus, identifying the best practices that adhere to clinical principles is critical to ensure transparency and reproducibility to drive clinical adoption. It further fosters reproducible, ethical, and reliable conduct of studies. This procedure is also crucial for setting up a software quality management system to ensure regulatory compliance in developing software as a medical device aimed at early preclinical detection of clinical deterioration.

OBJECTIVE

This scoping review focuses on the neonatal intensive care unit setting and summarizes the state-of-the-art computational methods used for preprocessing neonatal clinical physiological signals; these signals are used for the development of machine learning models to predict the risk of adverse outcomes.

METHODS

Five databases (PubMed, Web of Science, Scopus, IEEE, and ACM Digital Library) were searched using a combination of keywords and MeSH (Medical Subject Headings) terms. A total of 3585 papers from 2013 to January 2023 were identified based on the defined search terms and inclusion criteria. After removing duplicates, 2994 (83.51%) papers were screened by title and abstract, and 81 (0.03%) were selected for full-text review. Of these, 52 (64%) were eligible for inclusion in the detailed analysis.

RESULTS

Of the 52 articles reviewed, 24 (46%) studies focused on diagnostic models, while the remainder (n=28, 54%) focused on prognostic models. The analysis conducted in these studies involved various physiological signals, with electrocardiograms being the most prevalent. Different programming languages were used, with MATLAB and Python being notable. The monitoring and capturing of physiological data used diverse systems, impacting data quality and introducing study heterogeneity. Outcomes of interest included sepsis, apnea, bradycardia, mortality, necrotizing enterocolitis, and hypoxic-ischemic encephalopathy, with some studies analyzing combinations of adverse outcomes. We found a partial or complete lack of transparency in reporting the setting and the methods used for signal preprocessing. This includes reporting methods to handle missing data, segment size for considered analysis, and details regarding the modification of the state-of-the-art methods for physiological signal processing to align with the clinical principles for neonates. Only 7 (13%) of the 52 reviewed studies reported all the recommended preprocessing steps, which could have impacts on the downstream analysis.

CONCLUSIONS

The review found heterogeneity in the techniques used and inconsistent reporting of parameters and procedures used for preprocessing neonatal physiological signals, which is necessary to confirm adherence to clinical and software quality management system practices, usefulness, and choice of best practices. Enhancing transparency in reporting and standardizing procedures will boost study interpretation and reproducibility and expedite clinical adoption, instilling confidence in the research findings and streamlining the translation of research outcomes into clinical practice, ultimately contributing to the advancement of neonatal care and patient outcomes.

Early exposure to mercury and cardiovascular function of seven-year old children in Guadeloupe (French West Indies)

BACKGROUND

The cardiotoxicity of prenatal exposure to mercury has been suggested in populations having regular contaminated seafood intake, though replications in the literature are inconsistent.

METHODS

The Timoun Mother-Child Cohort Study was set up in Guadeloupe, an island in the Caribbean Sea where seafood consumption is regular. At seven years of age, 592 children underwent a medical examination, including cardiac function assessment. Blood pressure (BP) was taken using an automated blood pressure monitor, heart rate variability (HRV, 9 parameters) and electrocardiogram (ECG) characteristics (QT, T-wave parameters) were measured using Holter cardiac monitoring during the examination. Total mercury concentrations were measured in cord blood at birth (median = 6.6 μg/L, N = 399) and in the children's blood at age 7 (median = 1.7 μg/L, N = 310). Adjusted linear and non-linear modelling was used to study the association of each cardiac parameter with prenatal and childhood exposures. Sensitivity analyses included co-exposures to lead and cadmium, adjustment for maternal seafood consumption, selenium and polyunsaturated fatty acids (n3-PUFAs), and for sporting activity.

RESULTS

Higher prenatal mercury was associated with higher systolic BP at 7 years of age (βlog2 = 1.02; 95% Confidence Interval (CI) = 0.10, 1.19). In boys, intermediate prenatal exposure was associated with reduced overall HRV and parasympathetic activity, and longer QT was observed with increasing prenatal mercury (βlog2 = 4.02; CI = 0.48, 7.56). In girls, HRV tended to increase linearly with prenatal exposure, and no association was observed with QT-wave related parameters. Mercury exposure at 7 years was associated with decreased BP in girls (βlog2 = -1.13; CI = -2.22, -0.004 for diastolic BP). In boys, the low/high-frequency (LF/HF) ratio increased for intermediate levels of exposure.

CONCLUSION

Our study suggests sex-specific and non-monotonic modifications in some cardiac health parameters following prenatal exposure to mercury in pre-pubertal children from an insular fish-consuming population.

Analysis of Cardiac Vibration Signals Acquired From a Novel Implant Placed on the Gastric Fundus

The analysis of cardiac vibration signals has been shown as an interesting tool for the follow-up of chronic pathologies involving the cardiovascular system, such as heart failure (HF). However, methods to obtain high-quality, real-world and longitudinal data, that do not require the involvement of the patient to correctly and regularly acquire these signals, remain to be developed. Implantable systems may be a solution to this observability challenge. In this paper, we evaluate the feasibility of acquiring useful electrocardiographic (ECG) and accelerometry (ACC) data from an innovative implant located in the gastric fundus. In a first phase, we compare data acquired from the gastric fundus with gold standard data acquired from surface sensors on 2 pigs. A second phase investigates the feasibility of deriving useful hemodynamic markers from these gastric signals using data from 4 healthy pigs and 3 pigs with induced HF with longitudinal recordings. The following data processing chain was applied to the recordings: (1) ECG and ACC data denoising, (2) noise-robust real-time QRS detection from ECG signals and cardiac cycle segmentation, (3) Correlation analysis of the cardiac cycles and computation of coherent mean from aligned ECG and ACC, (4) cardiac vibration components segmentation (S1 and S2) from the coherent mean ACC data, and (5) estimation of signal context and a signal-to-noise ratio (SNR) on both signals. Results show a high correlation between the markers acquired from the gastric and thoracic sites, as well as pre-clinical evidence on the feasibility of chronic cardiovascular monitoring from an implantable cardiac device located at the gastric fundus, the main challenge remains on the optimization of the signal-to-noise ratio, in particular for the handling of some sources of noise that are specific to the gastric acquisition site.

Modeling patient-specific desaturation patterns in sleep apnea

OBJECTIVE

The physiological mechanisms involved in cardio-respiratory responses to sleep apnea events are not yet fully elucidated. A model-based approach is proposed to analyse the acute desaturation response to obstructive apneas.

METHODS

An integrated model of cardio-respiratory interactions was proposed and parameters were identified, using an evolutionary algorithm, on a database composed of 107 obstructive apneas acquired from 10 patients (HYPNOS clinical study). Unsupervised clustering was applied to the identified parameters in order to characterize the phenotype of each response to obstructive apneas.

RESULTS

A close match was observed between simulated oxygen saturation (SaO2) and experimental SaO2 in all identifications (median RMSE = 1.3892%). Two clusters of parameters, associated with different dynamics related to sleep apnea and periodic breathing were obtained.

CONCLUSION AND SIGNIFICANCE

The proposed patient and event-specific model-based analysis provides understanding on specific desaturation patterns, consequent to apnea events, with potential applications for personalized diagnosis and treatment.

Evaluation of maturation in preterm infants through an ensemble machine learning algorithm using physiological signals

This study was designed to test if heart rate variability (HRV) data from preterm and full-term infants could be used to estimate their functional maturational age (FMA), using a machine learning model. We propose that the FMA, and its deviation from the postmenstrual age (PMA) of the infants could inform physicians about the progress of the maturation of the infants. The HRV data was acquired from 50 healthy infants, born between 25 and 41 weeks of gestational age, who did not present any signs of abnormal maturation relative to their age group during the period of observation. The HRV features were used as input for a machine learning model that uses filtering and genetic algorithms for feature selection, and an ensemble machine learning (EML) algorithm, which combines linear and random forest regressions, to produce as output a FMA. Using HRV data, the FMA had a mean absolute error of 0.93 weeks, 95% CI [0.78, 1.08], compared to the PMA. These results demonstrate that HRV features of newborn infants can be used by an EML model to estimate their FMA. This method was also generalized using respiration rate variability (RRV) and bradycardia data, obtaining similar results. The FMA, predicted either by HRV, RRV or bradycardia, and its deviation from the true PMA of the infants, could be used as a surrogate measure of the maturational age of the infants, which could potentially be monitored non-invasively and in real-time in the setting of neonatal intensive care units.

Early bradycardia detection and therapeutic interventions in preterm infant monitoring

In very preterm infants, cardio-respiratory events and associated hypoxemia occurring during early postnatal life have been associated with risks of retinopathy, growth alteration and neurodevelopment impairment. These events are commonly detected by continuous cardio-respiratory monitoring in neonatal intensive care units (NICU), through the associated bradycardia. NICU nurse interventions are mainly triggered by these alarms. In this work, we acquired data from 52 preterm infants during NICU monitoring, in order to propose an early bradycardia detector which is based on a decentralized fusion of three detectors. The main objective is to improve automatic detection under real-life conditions without altering performance with respect to that of a monitor commonly used in NICU. We used heart rate lower than 80 bpm during at least 10 sec to define bradycardia. With this definition we observed a high rate of false alarms (64%) in real-life and that 29% of the relevant alarms were not followed by manual interventions. Concerning the proposed detection method, when compared to current monitors, it provided a significant decrease of the detection delay of 2.9 seconds, without alteration of the sensitivity (97.6% vs 95.2%) and false alarm rate (63.7% vs 64.1%). We expect that such an early detection will improve the response of the newborn to the intervention and allow for the development of new automatic therapeutic strategies which could complement manual intervention and decrease the sepsis risk.

Early Detection of Late Onset Sepsis in Premature Infants Using Visibility Graph Analysis of Heart Rate Variability

OBJECTIVE

This study was designed to test the diagnostic value of visibility graph features derived from the heart rate time series to predict late onset sepsis (LOS) in preterm infants using machine learning.

METHODS

The heart rate variability (HRV) data was acquired from 49 premature newborns hospitalized in neonatal intensive care units (NICU). The LOS group consisted of patients who received more than five days of antibiotics, at least 72 hours after birth. The control group consisted of infants who did not receive antibiotics. HRV features in the days prior to the start of antibiotics (LOS group) or in a randomly selected period (control group) were compared against a baseline value calculated during a calibration period. After automatic feature selection, four machine learning algorithms were trained. All the tests were done using two variants of the feature set: one only included traditional HRV features, and the other additionally included visibility graph features. Performance was studied using area under the receiver operating characteristics curve (AUROC).

RESULTS

The best performance for detecting LOS was obtained with logistic regression, using the feature set including visibility graph features, with AUROC of 87.7% during the six hours preceding the start of antibiotics, and with predictive potential (AUROC above 70%) as early as 42 h before start of antibiotics.

CONCLUSION

These results demonstrate the usefulness of introducing visibility graph indexes in HRV analysis for sepsis prediction in newborns.

SIGNIFICANCE

The method proposed the possibility of non-invasive, real-time monitoring of risk of LOS in a NICU setting.

The hidden waves in the ECG uncovered revealing a sound automated interpretation method

A novel approach for analysing cardiac rhythm data is presented in this paper. Heartbeats are decomposed into the five fundamental P, Q, R, S and T waves plus an error term to account for artifacts in the data which provides a meaningful, physical interpretation of the heart's electric system. The morphology of each wave is concisely described using four parameters that allow all the different patterns in heartbeats to be characterized and thus differentiated This multi-purpose approach solves such questions as the extraction of interpretable features, the detection of the fiducial marks of the fundamental waves, or the generation of synthetic data and the denoising of signals. Yet the greatest benefit from this new discovery will be the automatic diagnosis of heart anomalies as well as other clinical uses with great advantages compared to the rigid, vulnerable and black box machine learning procedures, widely used in medical devices. The paper shows the enormous potential of the method in practice; specifically, the capability to discriminate subjects, characterize morphologies and detect the fiducial marks (reference points) are validated numerically using simulated and real data, thus proving that it outperforms its competitors.

Diagnosis of Neonatal Late-Onset Infection in Very Preterm Infant: Inter-Observer Agreement and International Classifications

Background: The definition of late-onset bacterial sepsis (LOS) in very preterm infants is not unified. The objective was to assess the concordance of LOS diagnosis between experts in neonatal infection and international classifications and to evaluate the potential impact on heart rate variability and rate of “bronchopulmonary dysplasia or death”. Methods: A retrospective (2017–2020) multicenter study including hospitalized infants born before 31 weeks of gestation with intention to treat at least 5-days with antibiotics was performed. LOS was classified as “certain or probable” or “doubtful” independently by five experts and according to four international classifications with concordance assessed by Fleiss’s kappa test. Results: LOS was suspected at seven days (IQR: 5–11) of life in 48 infants. Following expert classification, 36 of them (75%) were considered as “certain or probable” (kappa = 0.41). Following international classification, this number varied from 13 to 46 (kappa = −0.08). Using the expert classification, “bronchopulmonary dysplasia or death” occurred less frequently in the doubtful group (25% vs. 78%, p < 0.001). Differences existed in HRV changes between the two groups. Conclusion: The definition of LOS is not consensual with a low international and moderate inter-observer agreement. This affects the evaluation of associated organ dysfunction and prognosis.

Quiet Sleep Organization of Very Preterm Infants Is Correlated With Postnatal Maturation

Background: Sleep is an important determinant of brain development in preterm infants. Its temporal organization varies with gestational age (GA) and post-menstrual age (PMA) but little is known about how sleep develops in very preterm infants. The objective was to study the correlation between the temporal organization of quiet sleep (QS) and maturation in premature infants without severe complications during their neonatal hospitalization. Methods: Percentage of time spent in QS and average duration of time intervals (ADI) spent in QS were analyzed from a cohort of newborns with no severe complications included in the Digi-NewB prospective, multicentric, observational study in 2017-19. Three groups were analyzed according to GA: Group 1 (27-30 weeks), Group 2 (33-37 weeks), Group 3 (>39 weeks). Two 8-h video recordings were acquired in groups 1 and 2: after birth (T1) and before discharge from hospital (T2). The annotation of the QS phases was performed by analyzing video recordings together with heart rate and respiratory traces thanks to a dedicated software tool of visualization and annotation of multimodal long-time recordings, with a double expert reading. Results are expressed as median (interquartile range, IQR). Correlations were analyzed using a linear mixed model. Results: Five newborns were studied in each group (160 h of recording). Median time spent in QS increased from 13.0% [IQR: 13-20] to 28.8% [IQR: 27-30] and from 17.0% [IQR: 15-21] to 29.6% [IQR: 29.5-31.5] in Group 1 and 2, respectively. Median ADI increased from 54 [IQR: 53-54] to 288 s [IQR: 279-428] and from 90 [IQR: 84-96] to 258 s [IQR: 168-312] in Group 1 and 2. Both groups reach values similar to that of group 3, respectively 28.2% [IQR: 24.5-31.3] and 270 s [IQR: 210-402]. The correlation between PMA and time spent in QS or ADI were, respectively 0.73 (p < 10^-4) and 0.46 (p = 0.06). Multilinear analysis using temporal organization of QS gave an accurate estimate of PMA (r ² = 0.87, p < 0.001). Conclusion: The temporal organization of QS is correlated with PMA in newborns without severe complication. An automated standardized continuous behavioral quantification of QS could be interesting to monitor during the hospitalization stay in neonatal units.