Advanced automatic detection of fetal body movements from multichannel magnetocardiographic signals

Different narcotic gases and concentrations for immobilization of ostrich embryos for in-ovo imaging

In-ovo imaging using avian eggs has been described as a potential alternative to animal testing using rodents. However, imaging studies are hampered by embryonal motion producing artifacts. This study aims at systematically comparing isoflurane, desflurane and sevoflurane in three different concentrations in ostrich embryos. Biomagnetic signals of ostrich embryos were recorded analyzing cardiac action and motion. Ten groups comprising eight ostrich embryos each were investigated: Control, isoflurane (2%, 4%, and 6%), desflurane (6%, 12%, and 18%) and sevoflurane (3%, 5%, and 8%). Each ostrich egg was exposed to the same narcotic gas and concentration on development day (DD) 31 and 34. Narcotic gas exposure was upheld for 90 min and embryos were monitored for additional 75 min. Toxicity was evaluated by verifying embryo viability 24 h after the experiments. Initial heart rate of mean 148 beats/min (DD 31) and 136 beats/min (DD 34) decreased over time by 44-48 beats/minute. No significant differences were observed between groups. All narcotic gases led to distinct movement reduction after mean 8 min. Embryos exposed to desflurane 6% showed residual movements. Isoflurane 6% and sevoflurane 8% produced motion-free time intervals of mean 70 min after discontinuation of narcotic gas exposure. Only one embryo death occurred after narcotic gas exposure with desflurane 6%. This study shows that isoflurane, desflurane and sevoflurane are suitable for ostrich embryo immobilization, which is a prerequisite for motion-artifact free imaging. Application of isoflurane 6% and sevoflurane 8% is a) safe as no embryonal deaths occurred after exposure and b) effective as immobilization was observed for approx. 70 min after the end of narcotic gas exposure. These results should be interpreted with caution regarding transferability to other avian species as differences in embryo size and incubation duration exist.

Non-Contact Monitoring of Fetal Movement Using Abdominal Video Recording

Fetal movement (FM) is an important indicator of fetal health. However, the current methods of FM detection are unsuitable for ambulatory or long-term observation. This paper proposes a non-contact method for monitoring FM. We recorded abdominal videos from pregnant women and then detected the maternal abdominal region within each frame. FM signals were acquired by optical flow color-coding, ensemble empirical mode decomposition, energy ratio, and correlation analysis. FM spikes, indicating the occurrence of FMs, were recognized using the differential threshold method. FM parameters including number, interval, duration, and percentage were calculated, and good agreement was found with the manual labeling performed by the professionals, achieving true detection rate, positive predictive value, sensitivity, accuracy, and F1_score of 95.75%, 95.26%, 95.75%, 91.40%, and 95.50%, respectively. The changes in FM parameters with gestational week were consistent with pregnancy progress. In general, this study provides a novel contactless FM monitoring technology for use at home.

Removing Clinical Motion Artifacts During Ventilation Monitoring With Electrical Impedance Tomography: Introduction of Methodology and Validation With Simulation and Patient Data

Objective

Electrical impedance tomography (EIT) is a bedside tool for lung ventilation and perfusion assessment. However, the ability for long-term monitoring diminished due to interferences from clinical interventions and motion artifacts. The purpose of this study is to investigate the feasibility of the discrete wavelet transform (DWT) to detect and remove the common types of motion artifacts in thoracic EIT.

Methods

Baseline drifting, step-like and spike-like interferences were simulated to mimic three common types of motion artifacts. The discrete wavelet decomposition was employed to characterize those motion artifacts in different frequency levels with different wavelet coefficients, and those motion artifacts were then attenuated by suppressing the relevant wavelet coefficients. Further validation was conducted in two patients when motion artifacts were introduced through pulsating mattress and deliberate body movements. The db8 wavelet was used to decompose the contaminated signals into several sublevels.

Results

In the simulation study, it was shown that, after being processed by DWT, the signal consistency improved by 92.98% for baseline drifting, 97.83% for the step-like artifact, and 62.83% for the spike-like artifact; the signal similarity improved by 77.49% for baseline drifting, 73.47% for the step-like artifact, and 2.35% for the spike-like artifact. Results from patient data demonstrated the EIT image errors decreased by 89.24% (baseline drifting), 88.45% (step-like artifact), and 97.80% (spike-like artifact), respectively; the data correlations between EIT images without artifacts and the processed were all > 0.95.

Conclusion

This study found that DWT is a universal and effective tool to detect and remove these motion artifacts.

Automatic fetal movement recognition from multi-channel accelerometry data

BACKGROUND AND OBJECTIVE

Significant health care resources are allocated to monitoring high risk pregnancies to minimize growth compromise, reduce morbidity and prevent stillbirth. Fetal movement has been recognized as an important indicator of fetal health. Studies have shown that 25% of pregnancies with decreased fetal movement in the third trimester led to poor outcomes at birth. The studies have also shown that maternal perception of fetal movement is highly subjective and varies from person to person. A non-invasive system for fetal movement detection that can be used outside hospital would represent an advance in at-home monitoring of at-risk pregnancies. This is a challenging task that requires the use of advanced signal processing techniques to differentiate genuine fetal movements from contaminating artefacts.

METHODS

This manuscript proposes a novel algorithm for automatic fetal movement recognition using data collected from wearable tri-axial accelerometers strategically placed on the maternal abdomen. The novelty of the work resides in the efficient removal of artefacts and in distinctive feature extraction. The proposed algorithm used independent component analysis (ICA) for dimensionality reduction and artefact removal. A supplemental technique based on discrete wavelet transform (DWT) was also used to remove artefacts.

RESULTS

To identify fetal movements, 31 features were extracted from the acceleration data. Based on these features, several classifiers were used to distinguish fetal from non-fetal movements. Robustness of the classifiers was tested for various concentrations of artefacts in the classification data. The best performance was achieved by Bagging classifier algorithm, with random forest as its basis classifier, yielding an accuracy ranging from 87.6% to 95.8% depending on the artefact concentration level.

CONCLUSIONS

A high performance detection of fetal movements can be achieved using accelerometery-based systems suitable for long-term monitoring.

The Relationship between Gestational Diabetes Metabolic Control and Fetal Autonomic Regulation, Movement and Birth Weight

(1) Background: Maternal metabolic control in gestational diabetes is suggested to influence fetal autonomic control and movement activity, which may have fetal outcome implications. We aimed to analyze the relationship between maternal metabolic control, fetal autonomic heart rate regulation, activity and birth weight. (2) Methods: Prospective noninterventional longitudinal cohort monitoring study accompanying 19 patients with specialist clinical care for gestational diabetes. Monthly fetal magnetocardiography with electro-physiologically-based beat-to-beat heart rate recording for analysis of heart rate variability (HRV) and the ‘fetal movement index’ (FMI) was performed. Data were compared to 167 healthy pregnant women retrieved from our pre-existing study database. (3) Results: Fetal vagal tone was increased with gestational diabetes compared to controls, whereas sympathetic tone and FMI did not differ. Within the diabetic population, sympathetic activation was associated with higher maternal blood-glucose levels. Maternal blood-glucose levels correlated positively with birth weight z scores. FMI showed no correlation with birth weight but attenuated the positive correlation between maternal blood-glucose levels and birth weight. (4) Conclusion: Fetal autonomic control is altered by gestational diabetes and maternal blood-glucose level, even if metabolic adjustment and outcome is comparable to healthy controls.