Real-time Neonatal Chest Sound Separation using Deep Learning

no code implementations • 26 Oct 2023 • Yang Yi Poh, Ethan Grooby, Kenneth Tan, Lindsay Zhou, Arrabella King, Ashwin Ramanathan, Atul Malhotra, Mehrtash Harandi, Faezeh Marzbanrad

Inspired by the Conv-TasNet model, the proposed model has an encoder, decoder, and mask generator.

Prediction of Neonatal Respiratory Distress in Term Babies at Birth from Digital Stethoscope Recorded Chest Sounds

no code implementations • 25 Jan 2022 • Ethan Grooby, Chiranjibi Sitaula, Kenneth Tan, Lindsay Zhou, Arrabella King, Ashwin Ramanathan, Atul Malhotra, Guy A. Dumont, Faezeh Marzbanrad

Neonatal respiratory distress is a common condition that if left untreated, can lead to short- and long-term complications.

Specificity

Noisy Neonatal Chest Sound Separation for High-Quality Heart and Lung Sounds

1 code implementation • 10 Jan 2022 • Ethan Grooby, Chiranjibi Sitaula, Davood Fattahi, Reza Sameni, Kenneth Tan, Lindsay Zhou, Arrabella King, Ashwin Ramanathan, Atul Malhotra, Guy A. Dumont, Faezeh Marzbanrad

The median processing time for the sound separation of a 10s recording was found to be 28. 3s for NMCF and 342ms for NMF.

Real-Time Multi-Level Neonatal Heart and Lung Sound Quality Assessment for Telehealth Applications

1 code implementation • 29 Sep 2021 • Ethan Grooby, Chiranjibi Sitaula, Davood Fattahi, Reza Sameni, Kenneth Tan, Lindsay Zhou, Arrabella King, Ashwin Ramanathan, Atul Malhotra, Guy A. Dumont, Faezeh Marzbanrad

In this study, a new method is proposed to objectively and automatically assess heart and lung signal quality on a 5-level scale in real-time and to assess the effect of signal quality on vital sign estimation.

feature selection Ordinal Classification