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

• URL: http://arxiv.org/abs/2201.10105v1
• Date: Tue, 25 Jan 2022 05:46:52 GMT
• Title: Prediction of Neonatal Respiratory Distress in Term Babies at Birth from Digital Stethoscope Recorded Chest Sounds
• Authors: Ethan Grooby, Chiranjibi Sitaula, Kenneth Tan, Lindsay Zhou, Arrabella King, Ashwin Ramanathan, Atul Malhotra, Guy A. Dumont, Faezeh Marzbanrad
• Abstract summary: This paper investigates the usage of digital stethoscope recorded chest sounds taken within 1min post-delivery. The algorithm produced specificity, sensitivity, and accuracy results of 85.0%, 66.7% and 81.8%, respectively.
• Score: 2.466324275447402
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neonatal respiratory distress is a common condition that if left untreated, can lead to short- and long-term complications. This paper investigates the usage of digital stethoscope recorded chest sounds taken within 1min post-delivery, to enable early detection and prediction of neonatal respiratory distress. Fifty-one term newborns were included in this study, 9 of whom developed respiratory distress. For each newborn, 1min anterior and posterior recordings were taken. These recordings were pre-processed to remove noisy segments and obtain high-quality heart and lung sounds. The random undersampling boosting (RUSBoost) classifier was then trained on a variety of features, such as power and vital sign features extracted from the heart and lung sounds. The RUSBoost algorithm produced specificity, sensitivity, and accuracy results of 85.0%, 66.7% and 81.8%, respectively.

Related papers

• Automatic Infant Respiration Estimation from Video: A Deep Flow-based Algorithm and a Novel Public Benchmark [10.097634735211654]
  We develop a deep-learning method for estimating respiratory rate and waveform from plain video footage in natural settings. Our model supports the first public annotated infant respiration dataset with 125 videos. When trained and tested on the AIR-125 infant data, our method significantly outperforms other state-of-the-art methods in respiratory rate estimation.
  arXiv  Detail & Related papers  (2023-07-24T19:59:15Z)
• Using BOLD-fMRI to Compute the Respiration Volume per Time (RTV) and Respiration Variation (RV) with Convolutional Neural Networks (CNN) in the Human Connectome Development Cohort [55.41644538483948]
  This study proposes a one-dimensional CNN model for reconstruction of two respiratory measures, RV and RVT. Results show that a CNN can capture informative features from resting BOLD signals and reconstruct realistic RV and RVT timeseries.
  arXiv  Detail & Related papers  (2023-07-03T18:06:36Z)
• Activity Recognition From Newborn Resuscitation Videos [3.54339629317433]
  A dataset of recorded signals during newborn resuscitation, including videos, has been collected in Haydom, Tanzania. We propose a two-step deep neural network system, ORAA-net, to do activity recognition during newborn resuscitation. The system recognized the activities newborn uncovered, stimulation, ventilation and suction with a mean precision of 77.67 %, a mean recall of 77,64 %, and a mean accuracy of 92.40 %.
  arXiv  Detail & Related papers  (2023-03-14T11:04:32Z)
• Investigating Pulse-Echo Sound Speed Estimation in Breast Ultrasound with Deep Learning [44.70495434283752]
  We propose a deep-learning approach for sound speed estimation from in-phase and quadrature ultrasound signals. We develop a large-scale simulated ultrasound dataset that generates quasi-realistic breast tissue. We evaluate the model on simulated, phantom, and in-vivo breast ultrasound data.
  arXiv  Detail & Related papers  (2023-02-06T19:02:44Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Noisy Neonatal Chest Sound Separation for High-Quality Heart and Lung Sounds [2.536333994672575]
  stethoscope-recorded chest sounds provide opportunity for remote cardio-respiratory health monitoring of neonates. reliable monitoring requires high-quality heart and lung sounds. This paper presents novel Non-negative Matrix Factorisation (NMF) and Non-negative Matrix Co-Factorisation (NMCF) methods for neonatal chest sound separation.
  arXiv  Detail & Related papers  (2022-01-10T08:38:10Z)
• Real-Time Multi-Level Neonatal Heart and Lung Sound Quality Assessment for Telehealth Applications [0.08872883781800303]
  Digital stethoscopes in combination with telehealth allow chest sounds to be easily collected and transmitted for remote monitoring and diagnosis. Low-quality recordings complicate the remote monitoring and diagnosis. New method is proposed to objectively and automatically assess heart and lung signal quality on a 5-level scale in real-time.
  arXiv  Detail & Related papers  (2021-09-29T01:08:20Z)
• A New Non-Negative Matrix Co-Factorisation Approach for Noisy Neonatal Chest Sound Separation [0.09512887847441218]
  A new Non-negative Matrix Co-Factorisation-based approach is proposed to separate noisy chest sound recordings into heart, lung, and noise components. This method is achieved through training with 20 high-quality heart and lung sounds, in parallel with separating the sounds of the noisy recording. Results show significant improvements in heart and lung sound quality scores respectively, and improved accuracy of 3.6bpm and 1.2bpm in heart and breathing rate estimation.
  arXiv  Detail & Related papers  (2021-09-04T02:48:02Z)
• Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural Networks [68.8204255655161]
  We adapt an ensemble of Convolutional Neural Networks to classify if a speaker is infected with COVID-19 or not. Ultimately, it achieves an Unweighted Average Recall (UAR) of 74.9%, or an Area Under ROC Curve (AUC) of 80.7% by ensembling neural networks.
  arXiv  Detail & Related papers  (2020-12-29T01:14:17Z)
• Identification of deep breath while moving forward based on multiple body regions and graph signal analysis [45.62293065676075]
  This paper presents an unobtrusive solution that can automatically identify deep breath when a person is walking past the global depth camera. In validation experiments, the proposed approach outperforms the comparative methods with the accuracy, precision, recall and F1 of 75.5%, 76.2%, 75.0% and 75.2%, respectively.
  arXiv  Detail & Related papers  (2020-10-20T08:26:50Z)
• Deep Learning for Automatic Pneumonia Detection [72.55423549641714]
  Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. Computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. We develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-excitation deep convolution neural networks, augmentations and multi-task learning.
  arXiv  Detail & Related papers  (2020-05-28T10:54:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.