skip to main content

Search for: All records

Creators/Authors contains: "Daniele, Michael"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Free, publicly-accessible full text available December 1, 2023
  2. Longitudinal fetal health monitoring is essential for high-risk pregnancies. Heart rate and heart rate variability are prime indicators of fetal health. In this work, we implemented two neural network architectures for heartbeat detection on a set of fetal phonocardiogram signals captured using fetal Doppler and a digital stethoscope. We test the efficacy of these networks using the raw signals and the hand-crafted energy from the signal. The results show a Convolutional Neural Network is the most efficient at identifying the S1 waveforms in a heartbeat, and its performance is improved when using the energy of the Doppler signals. We further discuss issues, such as low Signal-to-Noise Ratios (SNR), present in the training of a model based on the stethoscope signals. Finally, we show that we can improve the SNR, and subsequently the performance of the stethoscope, by matching the energy from the stethoscope to that of the Doppler signal.
  3. Herein, a 60-electrode array is fabricated down the length of a microchamber for analysis of a microphysiological system. The electrode array is fabricated by standard photolithographic, metallization, and etching techniques. Permutations of 2-wire impedance measurements (10 Hz to 1 MHz) are made along the length of the microchannel using a multiplexer, Gamry potentiostat, and custom Labview code. An impedance "heat map" is created via custom algorithms. Spatial resolution and mapping capabilities are exhibited using conductive NaCl solutions and 2D cell culture.