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Book ; Online: Recent Advances in Doppler Signal Processing and Modelling Techniques for Fetal Monitoring

Kimura, Yoshitaka / Marzbanrad, Faezeh / Khandoker, Ahsan H.

2018

Abstract: The guest editors of this eBook have accepted 10 very high-quality submissions for inclusion in a special issue of Frontiers in Physiology. The key difference between this eBook and contemporary fetal physiology related literature is that this Research ... ...

Abstract	The guest editors of this eBook have accepted 10 very high-quality submissions for inclusion in a special issue of Frontiers in Physiology. The key difference between this eBook and contemporary fetal physiology related literature is that this Research Topic summarizes additional insights into the physiological link between physiologically understandable mathematical indices of fetal signals and the developing cardiovascular functions in fetal health and compromises. This book should be of considerable help to researchers, professionals in fetal monitoring device industries, academics, and graduate students from a wide range of disciplines. The text provides a comprehensive account of recent research in this emerging field and we anticipate that the concepts presented here will generate further research in this field
Keywords	Science (General) ; Physiology
Size	1 electronic resource (99 p.)
Publisher	Frontiers Media SA
Document type	Book ; Online
Note	English ; Open Access
HBZ-ID	HT020101850
ISBN	9782889455362 ; 288945536X
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: EEG-based Emotion Recognition Using Sub-Band Time-Delay Correlations.

Alskafi, Feryal A / Khandoker, Ahsan H / Marzbanrad, Faezeh / Jelinek, Herbert F

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) 1–4

Abstract: Emotion recognition is a challenging task with many potential applications in psychology, psychiatry, and human-computer interaction (HCI). The use of time-delay information in the controlled time-delay stability (cTDS) algorithm can help to capture the ... ...

Abstract	Emotion recognition is a challenging task with many potential applications in psychology, psychiatry, and human-computer interaction (HCI). The use of time-delay information in the controlled time-delay stability (cTDS) algorithm can help to capture the temporal dynamics of EEG signals, including sub-band information and bi-directional coupling that can aid in emotion recognition and identification of specific connectivity patterns between brain rhythms. Incorporating EEG frequency bands can be used to design better emotion recognition systems. This paper evaluates the cTDS algorithm for binary classification tasks of arousal and valence using EEG sub-band signals. This method achieved a high accuracy of 91.1% for arousal and 91.7% for valence based on one electrode recording site at Fp1. The cTDS algorithm is a promising approach to analyzing brain network interactions. It can be particularly applicable to arousal and valence classification tasks, especially within a complex, multimodal feature space associated with understanding psychiatric disorders and HCI applications.
MeSH term(s)	Humans ; Electroencephalography/methods ; Emotions ; Brain ; Algorithms ; Software
Language	English
Publishing date	2023-12-11
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/EMBC40787.2023.10340014
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Publisher Correction: Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection.

Sitaula, Chiranjibi / Shahi, Tej Bahadur / Aryal, Sunil / Marzbanrad, Faezeh

Scientific reports

2022 Volume 12, Issue 1, Page(s) 1010

Language	English
Publishing date	2022-01-13
Publishing country	England
Document type	Published Erratum
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-05240-9
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: A framework to quantify controlled directed interactions in network physiology applied to cognitive function assessment.

Marzbanrad, Faezeh / Yaghmaie, Negin / Jelinek, Herbert F

Scientific reports

2020 Volume 10, Issue 1, Page(s) 18505

Abstract: The complex nature of physiological systems where multiple organs interact to form a network is complicated by direct and indirect interactions, with varying strength and direction of influence. This study proposes a novel framework which quantifies ... ...

Abstract	The complex nature of physiological systems where multiple organs interact to form a network is complicated by direct and indirect interactions, with varying strength and direction of influence. This study proposes a novel framework which quantifies directional and pairwise couplings, while controlling for the effect of indirect interactions. Simulation results confirm the superiority of this framework in uncovering directional primary links compared to previous published methods. In a practical application of cognitive attention and alertness tasks, the method was used to assess controlled directed interactions between the cardiac, respiratory and brain activities (prefrontal cortex). It revealed increased interactions during the alertness task between brain wave activity on the left side of the brain with heart rate and respiration compared to resting phases. During the attention task, an increased number of right brain wave interactions involving respiration was also observed compared to rest, in addition to left brain wave activity with heart rate. The proposed framework potentially assesses directional interactions in complex network physiology and may detect cognitive dysfunctions associated with altered network physiology.
MeSH term(s)	Adult ; Attention/physiology ; Brain/physiology ; Brain Mapping/methods ; Cognition/physiology ; Computer Simulation ; Female ; Heart Rate/physiology ; Humans ; Male ; Middle Aged ; Models, Theoretical ; Nervous System Physiological Phenomena ; Neural Pathways/physiopathology ; Respiration
Language	English
Publishing date	2020-10-28
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-020-75466-y
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Article ; Online: Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection.

Sitaula, Chiranjibi / Shahi, Tej Bahadur / Aryal, Sunil / Marzbanrad, Faezeh

Scientific reports

2021 Volume 11, Issue 1, Page(s) 23914

Abstract: Chest X-ray (CXR) images have been one of the important diagnosis tools used in the COVID-19 disease diagnosis. Deep learning (DL)-based methods have been used heavily to analyze these images. Compared to other DL-based methods, the bag of deep visual ... ...

Abstract	Chest X-ray (CXR) images have been one of the important diagnosis tools used in the COVID-19 disease diagnosis. Deep learning (DL)-based methods have been used heavily to analyze these images. Compared to other DL-based methods, the bag of deep visual words-based method (BoDVW) proposed recently is shown to be a prominent representation of CXR images for their better discriminability. However, single-scale BoDVW features are insufficient to capture the detailed semantic information of the infected regions in the lungs as the resolution of such images varies in real application. In this paper, we propose a new multi-scale bag of deep visual words (MBoDVW) features, which exploits three different scales of the 4th pooling layer's output feature map achieved from VGG-16 model. For MBoDVW-based features, we perform the Convolution with Max pooling operation over the 4th pooling layer using three different kernels: [Formula: see text], [Formula: see text], and [Formula: see text]. We evaluate our proposed features with the Support Vector Machine (SVM) classification algorithm on four CXR public datasets (CD1, CD2, CD3, and CD4) with over 5000 CXR images. Experimental results show that our method produces stable and prominent classification accuracy (84.37%, 88.88%, 90.29%, and 83.65% on CD1, CD2, CD3, and CD4, respectively).
MeSH term(s)	Algorithms ; COVID-19/diagnostic imaging ; Databases, Factual ; Deep Learning ; Humans ; Radiographic Image Interpretation, Computer-Assisted/methods ; Support Vector Machine
Language	English
Publishing date	2021-12-13
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-03287-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Investigating myocardial performance in normal and sick fetuses by abdominal Doppler signal derived indices.

Khandoker, Ahsan H / Al-Angari, Haitham M / Marzbanrad, Faezeh / Kimura, Yoshitaka

Current research in physiology

2021 Volume 4, Page(s) 29–38

Abstract: Introduction: Fetal myocardial performance indices are applied to assess aspects of systolic and diastolic function in developing fetal heart. The aim of this study was to determine normal values of Tei Index (TI) and modified TI (KI) for systolic and ... ...

Abstract	Introduction: Fetal myocardial performance indices are applied to assess aspects of systolic and diastolic function in developing fetal heart. The aim of this study was to determine normal values of Tei Index (TI) and modified TI (KI) for systolic and diastolic performance in early (<30 weeks), Mid (30-35 weeks) and late (36-41 weeks) relating to both normal fetuses as well as fetuses carrying a variety of fetal abnormalities, which do not call for precise anatomic imaging. Material and methods: Fetal Electrocardiogram Signals (FES) and Doppler Ultrasound Signal (DUS) were simultaneously documented from 55 normal and 25 abnormal fetuses with a variety of abnormalities including Congenital Heart Diseases (CHDs) and a variety of non-CHDs. The isovolumic contraction time (ICT), isovolumic relaxation time (IRT), ventricular ejection time (VET) and ventricular filling time (VFT) were estimated from continuous DUS signals by a hybrid of Hidden Markov and Support Vector Machine based automated model. The TI and the KI were calculated by using the formula (ICT + IRT)/VET and (ICT + IRT)/VFT respectively. Results: The TI was not found to show any significant change from early to late fetuses, nor between normal and abnormal cases. On the other hand, KI was shown to significantly decline in values from early to late normal cases and from normal to abnormal groups. Significant correlation (r = -0.36; p < 0.01) of gestational ages with only KI (not TI) was found in this study. Conclusion: Modified TI (KI) may be a useful index to monitor the normal development of fetal myocardial function and identify fetuses with a variety of CHD and non-CHD cases.
Language	English
Publishing date	2021-02-05
Publishing country	Netherlands
Document type	Journal Article
ISSN	2665-9441
ISSN (online)	2665-9441
DOI	10.1016/j.crphys.2021.02.002
Database	MEDical Literature Analysis and Retrieval System OnLINE

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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: Comparison of simultaneous auscultation and ultrasound for clinical assessment of bowel peristalsis in neonates.

Priyadarshi, Archana / Tracy, Mark / Kothari, Pankhuri / Sitaula, Chiranjibi / Hinder, Murray / Marzbanrad, Faezeh / Morakeas, Stephanie / Trivedi, Amit / Badawi, Nadia / Rogerson, Sheryl

Frontiers in pediatrics

2023 Volume 11, Page(s) 1173332

Abstract: Introduction: Assessment of bowel health in ill preterm infants is essential to prevent and diagnose early potentially life-threatening intestinal conditions such as necrotizing enterocolitis. Auscultation of bowel sounds helps assess peristalsis and is ...

Abstract	Introduction: Assessment of bowel health in ill preterm infants is essential to prevent and diagnose early potentially life-threatening intestinal conditions such as necrotizing enterocolitis. Auscultation of bowel sounds helps assess peristalsis and is an essential component of this assessment. Aim: We aim to compare conventional bowel sound auscultation using acoustic recordings from an electronic stethoscope to real-time bowel motility visualized on point-of-care bowel ultrasound (US) in neonates with no known bowel disease. Methods: This is a prospective observational cohort study in neonates on full enteral feeds with no known bowel disease. A 3M™ Littmann® Model 3200 electronic stethoscope was used to obtain a continuous 60-s recording of bowel sounds at a set region over the abdomen, with a concurrent recording of US using a 12l high-frequency Linear probe. The bowel sounds heard by the first investigator using the stethoscope were contemporaneously transferred for a computerized assessment of their electronic waveforms. The second investigator, blinded to the auscultation findings, obtained bowel US images using a 12l Linear US probe. All recordings were analyzed for bowel peristalsis (duration in seconds) by each of the two methods. Results: We recruited 30 neonates (gestational age range 27-43 weeks) on full enteral feeds with no known bowel disease. The detection of bowel peristalsis (duration in seconds) by both methods (acoustic and US) was reported as a percentage of the total recording time for each participant. Comparing the time segments of bowel sound detection by digital stethoscope recording to that of the visual detection of bowel movements in US revealed a median time of peristalsis with US of 58%, compared to 88.3% with acoustic assessment ( Conclusion: Our study demonstrates disconcordance between the detection of bowel sounds by auscultation and the detection of bowel motility in real time using US in neonates on full enteral feeds and with no known bowel disease. Better innovative methods using artificial intelligence to characterize bowel sounds, integrating acoustic mapping with sonographic detection of bowel peristalsis, will allow us to develop continuous neonatal bowel sound monitoring devices.
Language	English
Publishing date	2023-09-19
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2711999-3
ISSN	2296-2360
ISSN	2296-2360
DOI	10.3389/fped.2023.1173332
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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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