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Article ; Online: IEEE EMBS International Student Conferences -Insights From 2022 Editions.

Cakici, Andre L / Aleman, Bryan / Grooby, Ethan

2023 Volume 13, Issue 6, Page(s) 29–32

Abstract: 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 ... ...

Abstract	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. One of these programs, IEEE EMBS ISC, is the flagship event under the oversight of the Professional Development Portfolio. The purpose of the ISCs is to help students learn to manage an IEEE-style conference in a low-pressure environment and improve on their soft skills like leadership, communication, teamwork, and project management. Moreover, it gives them a platform to practice giving and receiving peer feedback on scientific writing and presentations, as well as making international connections which could turn into future collaborations.
MeSH term(s)	Humans ; Adolescent ; Engineering ; Students ; Learning ; Curriculum ; Societies, Medical
Language	English
Publishing date	2023-10-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	2567191-1
ISSN	2154-2317 ; 2154-2287
ISSN (online)	2154-2317
ISSN	2154-2287
DOI	10.1109/MPULS.2022.3227855
Database	MEDical Literature Analysis and Retrieval System OnLINE

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

Grooby, Ethan / Sitaula, Chiranjibi / Chang Kwok, T'ng / Sharkey, Don / Marzbanrad, Faezeh / Malhotra, Atul

Pediatric research

2023 Volume 93, Issue 2, Page(s) 413–425

Abstract: 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 ...

Abstract	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. We then reviewed the advances in sensor technologies and wearable modalities for this application. Common wearable modalities included clothing (39%); chest/abdominal belts (25%); and adhesive patches (15%). Popular singular physiological information from sensors included electrocardiogram (15%), breathing (24%), oxygen saturation and photoplethysmography (13%). Many studies (46%) incorporated a combination of these signals. There has been extensive research in neonatal cardiorespiratory monitoring using both single and multi-parameter systems. Poor data quality is a common issue and further research into combining multi-sensor information to alleviate this should be investigated. IMPACT STATEMENT: State-of-the-art review of sensor technology for wearable neonatal cardiorespiratory monitoring. Review of the designs for wearable neonatal cardiorespiratory monitoring. The use of multi-sensor information to improve physiological data quality has been limited in past research. Several sensor technologies have been implemented and tested on adults that have yet to be explored in the newborn population.
MeSH term(s)	Adult ; Infant, Newborn ; Humans ; Artificial Intelligence ; Monitoring, Physiologic/methods ; Wearable Electronic Devices ; Respiration
Language	English
Publishing date	2023-01-02
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	4411-8
ISSN	1530-0447 ; 0031-3998
ISSN (online)	1530-0447
ISSN	0031-3998
DOI	10.1038/s41390-022-02416-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 564: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: Artificial intelligence-driven wearable technologies for neonatal cardiorespiratory monitoring. Part 2: artificial intelligence.

Sitaula, Chiranjibi / Grooby, Ethan / Kwok, T'ng Chang / Sharkey, Don / Marzbanrad, Faezeh / Malhotra, Atul

Pediatric research

2022 Volume 93, Issue 2, Page(s) 426–436

Abstract: 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 ...

Abstract	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. We reviewed the advances in AI development for this application and potential future directions. For this review, we assimilated machine learning (ML) algorithms developed for neonatal cardiorespiratory monitoring, designed a taxonomy, and categorised the methods based on their learning capabilities and performance. Results: For AI related to wearable technologies for neonatal cardio-respiratory monitoring, 63% of studies utilised traditional ML techniques and 35% utilised deep learning techniques, including 6% that applied transfer learning on pre-trained models. Conclusions: A detailed review of AI methods for neonatal cardiorespiratory wearable sensors is presented along with their advantages and disadvantages. Hierarchical models and suggestions for future developments are highlighted to translate these AI technologies into patient benefit. Impact: State-of-the-art review in artificial intelligence used for wearable neonatal cardiorespiratory monitoring. Taxonomy design for artificial intelligence methods. Comparative study of AI methods based on their advantages and disadvantages.
MeSH term(s)	Infant, Newborn ; Humans ; Artificial Intelligence ; Algorithms ; Machine Learning ; Wearable Electronic Devices ; Heart
Language	English
Publishing date	2022-12-13
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	4411-8
ISSN	1530-0447 ; 0031-3998
ISSN (online)	1530-0447
ISSN	0031-3998
DOI	10.1038/s41390-022-02417-w
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 564: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: Neonatal Face and Facial Landmark Detection from Video Recordings.

Grooby, Ethan / Sitaula, Chiranjibi / Ahani, Soodeh / Holsti, Liisa / Malhotra, Atul / Dumont, Guy A / Marzbanrad, Faezeh

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2023 Volume 2023, Page(s) 1–5

Abstract: 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 ... ...

Abstract	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. Additionally, the re-orientation of input images and fusion of trained deep learning models was explored. Our proposed model based on YOLOv7Face outperformed existing methods with a mean average precision of 84.8% for face detection, and a normalised mean error of 0.072 for facial landmark detection. Overall, this will assist in the development of fully automated neonatal health assessment algorithms.Clinical relevance- Accurate face and facial landmark detection provides an automated and non-contact option to assist in video-based neonatal health applications.
MeSH term(s)	Infant, Newborn ; Humans ; Face ; Algorithms ; Video Recording ; Pain Measurement ; Research Design
Language	English
Publishing date	2023-12-08
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC40787.2023.10340960
Database	MEDical Literature Analysis and Retrieval System OnLINE

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

Grooby, Ethan / Sitaula, Chiranjibi / Tan, Kenneth / Zhou, Lindsay / King, Arrabella / Ramanathan, Ashwin / Malhotra, Atul / Dumont, Guy A / Marzbanrad, Faezeh

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2022 Volume 2022, Page(s) 4996–4999

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 1 min post-delivery, to enable early ... ...

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 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. For each newborn, 1 min 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. Clinical relevance--- This paper investigates the feasibility of digital stethoscope recorded chest sounds for early detection of respiratory distress in term newborn babies, to enable timely treatment and management.
MeSH term(s)	Auscultation ; Female ; Humans ; Infant, Newborn ; Parturition ; Pregnancy ; Respiratory Distress Syndrome, Newborn/diagnosis ; Respiratory Sounds/diagnosis ; Stethoscopes
Language	English
Publishing date	2022-09-09
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC48229.2022.9871449
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Noisy Neonatal Chest Sound Separation for High-Quality Heart and Lung Sounds.

Grooby, Ethan / Sitaula, Chiranjibi / Fattahi, Davood / Sameni, Reza / Tan, Kenneth / Zhou, Lindsay / King, Arrabella / Ramanathan, Ashwin / Malhotra, Atul / Dumont, Guy / Marzbanrad, Faezeh

IEEE journal of biomedical and health informatics

2023 Volume 27, Issue 6, Page(s) 2635–2646

Abstract: 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- ... ...

Abstract	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. To assess these methods and compare them with existing single-channel separation methods, an artificial mixture dataset was generated comprising heart, lung, and noise sounds. Signal-to-noise ratios were then calculated for these artificial mixtures. These methods were also tested on real-world noisy neonatal chest sounds and assessed based on vital sign estimation error, and a signal quality score of 1-5, developed in our previous works. Overall, both the proposed NMF and NMCF methods outperform the next best existing method by 2.7 dB to 11.6 dB for the artificial dataset, and 0.40 to 1.12 signal quality improvement for the real-world dataset. The median processing time for the sound separation of a 10 s recording was found to be 28.3 s for NMCF and 342 ms for NMF. With the stable and robust performance of our proposed methods, we believe these methods are useful to denoise neonatal heart and lung sounds in the real-world environment.
MeSH term(s)	Infant, Newborn ; Humans ; Respiratory Sounds ; Artificial Intelligence ; Noise ; Stethoscopes ; Monitoring, Physiologic ; Algorithms ; Heart Sounds ; Signal Processing, Computer-Assisted
Language	English
Publishing date	2023-06-05
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2022.3215995
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 5483: Show issues

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Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Book ; Online: Neonatal Face and Facial Landmark Detection from Video Recordings

Grooby, Ethan / Sitaula, Chiranjibi / Ahani, Soodeh / Holsti, Liisa / Malhotra, Atul / Dumont, Guy A. / Marzbanrad, Faezeh

2023

Abstract	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. Additionally, the re-orientation of input images and fusion of trained deep learning models was explored. Our proposed model based on YOLOv7Face outperformed existing methods with a mean average precision of 84.8% for face detection, and a normalised mean error of 0.072 for facial landmark detection. Overall, this will assist in the development of fully automated neonatal health assessment algorithms. Comment: 5 pages, 2 tables. Paper submitted for potential publication as a conference paper at the 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2023
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2023-02-08
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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

Grooby, Ethan / He, Jinyuan / Fattahi, Davood / Zhou, Lindsay / King, Arrabella / Ramanathan, Ashwin / Malhotra, Atul / Dumont, Guy A / Marzbanrad, Faezeh

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2021 Volume 2021, Page(s) 5668–5673

Abstract: 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- ... ...

Abstract	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. This method is achieved through training with 20 high quality heart and lung sounds, in parallel with separating the sounds of the noisy recording. The method was tested on 68 10-second noisy recordings containing both heart and lung sounds and compared to the current state of the art Non-negative Matrix Factorisation methods. 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 respectively, when compared to existing methods.
MeSH term(s)	Algorithms ; Heart Sounds ; Humans ; Infant, Newborn ; Noise ; Respiratory Sounds ; Sound Recordings
Language	English
Publishing date	2021-12-10
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC46164.2021.9630256
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Neonatal Heart and Lung Sound Quality Assessment for Robust Heart and Breathing Rate Estimation for Telehealth Applications.

Grooby, Ethan / He, Jinyuan / Kiewsky, Julie / Fattahi, Davood / Zhou, Lindsay / King, Arrabella / Ramanathan, Ashwin / Malhotra, Atul / Dumont, Guy A / Marzbanrad, Faezeh

IEEE journal of biomedical and health informatics

2021 Volume 25, Issue 12, Page(s) 4255–4266

Abstract: 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 ... ...

Abstract	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. A total of 88 10-second long chest sounds were taken from 76 preterm and full-term babies. Six annotators independently assessed the signal quality, number of detectable beats, and breathing periods from these recordings. For quality classification, 187 and 182 features were extracted from heart and lung sounds, respectively. After feature selection, class balancing, and hyperparameter optimization, a dynamic binary classification model was trained. Then HR and BR were automatically estimated from the chest sound and several approaches were compared.The results of subject-wise leave-one-out cross-validation, showed that the model distinguished high and low quality recordings in the test set with 96% specificity, 81% sensitivity and 93% accuracy for heart sounds, and 86% specificity, 69% sensitivity and 82% accuracy for lung sounds. The HR and BR estimated from high quality sounds resulted in significantly less median absolute error (4 bpm and 12 bpm difference, respectively) compared to those from low quality sounds. The methods presented in this work, facilitates automated neonatal chest sound auscultation for future telehealth applications.
MeSH term(s)	Algorithms ; Auscultation ; Heart Sounds ; Humans ; Infant, Newborn ; Reproducibility of Results ; Respiratory Sounds/diagnosis ; Telemedicine
Language	English
Publishing date	2021-12-06
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2020.3047602
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 5483: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Book ; Online: A New Non-Negative Matrix Co-Factorisation Approach for Noisy Neonatal Chest Sound Separation

Grooby, Ethan / He, Jinyuan / Fattahi, Davood / Zhou, Lindsay / King, Arrabella / Ramanathan, Ashwin / Malhotra, Atul / Dumont, Guy A. / Marzbanrad, Faezeh

2021

Abstract: Obtaining high-quality heart and lung sounds enables clinicians to accurately assess a newborn's cardio-respiratory health and provide timely care. However, noisy chest sound recordings are common, hindering timely and accurate assessment. A new Non- ... ...

Abstract	Obtaining high-quality heart and lung sounds enables clinicians to accurately assess a newborn's 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. This method is achieved through training with 20 high-quality heart and lung sounds, in parallel with separating the sounds of the noisy recording. The method was tested on 68 10-second noisy recordings containing both heart and lung sounds and compared to the current state of the art Non-negative Matrix Factorisation methods. 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 respectively, when compared to existing methods. Comment: 6 pages, 2 figures. To appear as conference paper at 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1st-5th November 2021
Keywords	Electrical Engineering and Systems Science - Audio and Speech Processing ; Computer Science - Machine Learning ; Computer Science - Sound ; Electrical Engineering and Systems Science - Signal Processing
Subject code	780
Publishing date	2021-09-03
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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