EMBC 2024: Student Activities Committee Events and Reflections.

The International Conference of the IEEE Engineering Medicine and Biology Society (EMBC) is the largest international biomedical engineering conference. In 2024, over 1,100 students and young professionals attended the conference in Orlando, FL, USA, from 15 to 19 July. EMBS Student Activities Committee (SAC) is involved in the annual international conference of the society, to aid students in finding a suitable space and providing programs that support personal and professional development.

Age-Based Heart Sound Segmentation for Accessible Pediatric Cardiovascular Disease Screening.

Digital stethoscopes provide a possible cost-effective solution to accessible screening of cardiovascular diseases in low-to-middle-income countries. Heart sound segmentation is an essential step in computer-aided screening. This paper examines the underlying adult-based assumptions and presumptions of state-of-the-art heart sound segmentation algorithms, and then proposes an age-based heart sound segmentation to provide higher accuracy performance for pediatric phonocardiograms.

NeoSSNet: Real-Time Neonatal Chest Sound Separation Using Deep Learning.

Auscultation for neonates is a simple and non-invasive method of diagnosing cardiovascular and respiratory disease. However, obtaining high-quality chest sounds containing only heart or lung sounds is non-trivial. Hence, this study introduces a new deep-learning model named NeoSSNet and evaluates its performance in neonatal chest sound separation with previous methods.

Neonatal Face and Facial Landmark Detection from Video Recordings.

This paper explores automated face and facial landmark detection of neonates, which is an important first step in many video-based neonatal health applications, such as vital sign estimation, pain assessment, sleep-wake classification, and jaundice detection. Utilising three publicly available datasets of neonates in the clinical environment, 366 images (258 subjects) and 89 (66 subjects) were annotated for training and testing, respectively. Transfer learning was applied to two YOLO-based models, with input training images augmented with random horizontal flipping, photo-metric colour distortion, translation and scaling during each training epoch.

IEEE EMBS International Student Conferences -Insights From 2022 Editions.

Student members within IEEE Engineering in Medicine and Biology Society (EMBS) are one of the most active segments among all other membership levels. Student-led initiatives all around the world have shown the necessity to give students the opportunity to present solutions to educational challenges, aiming to make the learning of young people an enriching and continuous experience while honing their organizational skills. IEEE EMBS SAC [1], formed under vice president for member and student activities, has taken the responsibility to initiate and implement programs for undergraduate and graduate student members of the society.

Artificial intelligence-driven wearable technologies for neonatal cardiorespiratory monitoring: Part 1 wearable technology.

With the development of Artificial Intelligence techniques, smart health monitoring is becoming more popular. In this study, we investigate the trend of wearable sensors being adopted and developed in neonatal cardiorespiratory monitoring. We performed a search of papers published from the year 2000 onwards.

Artificial intelligence-driven wearable technologies for neonatal cardiorespiratory monitoring. Part 2: artificial intelligence.

Background: With the development of Artificial Intelligence (AI) techniques, smart health monitoring, particularly neonatal cardiorespiratory monitoring with wearable devices, is becoming more popular. To this end, it is crucial to investigate the trend of AI and wearable sensors being developed in this domain.

Methods: We performed a review of papers published in IEEE Xplore, Scopus, and PubMed from the year 2000 onwards, to understand the use of AI for neonatal cardiorespiratory monitoring with wearable technologies.

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

Stethoscope-recorded chest sounds provide the opportunity for remote cardio-respiratory health monitoring of neonates. However, reliable monitoring requires high-quality heart and lung sounds. This paper presents novel artificial intelligence-based Non-negative Matrix Factorisation (NMF) and Non-negative Matrix Co-Factorisation (NMCF) methods for neonatal chest sound separation.

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

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 1 min 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.

A New Non-Negative Matrix Co-Factorisation Approach for Noisy Neonatal Chest Sound Separation.

Obtaining high quality heart and lung sounds enables clinicians to accurately assess a newborns cardio-respiratory health and provide timely care. However, noisy chest sound recordings are common, hindering timely and accurate assessment. A new Non-negative Matrix Co-Factorisation based approach is proposed to separate noisy chest sound recordings into heart, lung and noise components to address this problem.

Neonatal Heart and Lung Sound Quality Assessment for Robust Heart and Breathing Rate Estimation for Telehealth Applications.

With advances in digital stethoscopes, internet of things, signal processing and machine learning, chest sounds can be easily collected and transmitted to the cloud for remote monitoring and diagnosis. However, low quality of recordings complicates remote monitoring and diagnosis, particularly for neonatal care. This paper proposes a new method to objectively and automatically assess the signal quality to improve the accuracy and reliability of heart rate (HR) and breathing rate (BR) estimation from noisy neonatal chest sounds.