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Biology subjects

Sarafan, S.

Publications and source records attributed to Sarafan, S..

2 recordsLinked to original sources

Simultaneous Cardiac and Neurological Monitoring to Assess Chemical Exposures and Drug Toxicity in Xenopus Laevis

Simultaneous monitoring of electrocardiogram (ECG) and electroencephalogram (EEG) under chemical exposure requires innovative engineering techniques that can capture minute physiological changes in studied animal models. However, this is often administered with a bulky system that may cause signal distortions and discomfort for animals. We develop an integrated bioelectronic sensing system to provide simultaneous ECG and EEG assessment in real-time under chemical exposure for Xenopus laevis. The microelectrode array (MEA) membrane with integrated ECG and EEG sensing offers an opportunity to achieve multichannel noninvasive electrophysiological monitoring with favorable dimensions and spatial resolution. To validate the performance of our system, we assessed the ECG and EEG of Xenopus under exposure of Pentylenetetrazol (PTZ), an epilepsy-inducing drug. Effects of PTZ were detected with clear ECG and EEG alterations, including frequent ictal and interictal EEG events, 30 dB average EEG amplitude elevations, abnormal ECG morphology, and heart rate changes. Overall, our Xenopus-based real-time electrophysiology monitoring system holds high potential for many applications in drug screening and remote environmental toxicity monitoring.

physiology↗

Fetal Electrocardiogram Extraction from the Mother's Abdominal Signal Using the Ensemble Kalman Filter

Fetal electrocardiogram (fECG) assessment is essential throughout pregnancy to monitor the wellbeing and development of the fetus, and to possibly diagnose potential congenital heart defects. Due to the high noise incorporated in the abdominal ECG (aECG) signals, the extraction of fECG has been challenging. And it is even a lot more difficult for fECG extraction if only one channel of aECG is provided, i.e. in a compact patch device. In this paper, we propose a novel algorithm based on the Ensemble Kalman Filter (EnKF) for non-invasive fECG extraction from a single-channel aECG signal. To assess the performance of the proposed algorithm, we used our own clinical data, obtained from a pilot study with 10 subjects each of 20 min recording, and data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations. The proposed methodology shows the average positive predictive value (PPV) of 97.59%, sensitivity (SE) of 96.91%, and F1-score of 97.25% from the PhysioNet 2013 Challenge bank. Our results also indicate that the proposed algorithm is reliable and effective, and it outperforms the recently proposed Extended Kalman Filter (EKF) based algorithm.

bioengineering↗