Real-time Neonatal Chest Sound Separation using Deep Learning

Auscultation for neonates is a simple and non-invasive method of providing diagnosis for cardiovascular and respiratory disease. Such diagnosis often requires high-quality heart and lung sounds to be captured during auscultation. However, in most cases, obtaining such high-quality sounds is non-trivial due to the chest sounds containing a mixture of heart, lung, and noise sounds. As such, additional preprocessing is needed to separate the chest sounds into heart and lung sounds. This paper proposes a novel deep-learning approach to separate such chest sounds into heart and lung sounds. Inspired by the Conv-TasNet model, the proposed model has an encoder, decoder, and mask generator. The encoder consists of a 1D convolution model and the decoder consists of a transposed 1D convolution. The mask generator is constructed using stacked 1D convolutions and transformers. The proposed model outperforms previous methods in terms of objective distortion measures by 2.01 dB to 5.06 dB in the artificial dataset, as well as computation time, with at least a 17-time improvement. Therefore, our proposed model could be a suitable preprocessing step for any phonocardiogram-based health monitoring system

Accessible audio-visual system for neonatal health monitoring

There is an urgent need to make health monitoring, particularly cardio-respiratory monitoring, easily accessible to parents and newborns not covered by professional medical care and equipment in the critical first month of life. Hence, the overall aim of this thesis is the development of supporting software for an affordable, non-invasive solution for monitoring neonatal cardio-respiratory health automatically. This is achieved through audio analysis of digital stethoscope-recorded chest sounds and video analysis of camera-recorded neonates. For audio analysis, chest sounds are processed through state-of-the-art methods of automated signal quality analysis and sound separation to obtain high-quality heart and lung sounds. Heart and breathing rate estimation methods were then developed. Utilising these methods, automated health assessment was explored in the context of (1) clinical outcome identification, (2) murmur detection, (3) respiratory distress prediction, and (4) lung aeration assessment. For video analysis, extraction of photoplethysmogram and heart rate were demonstrated in clinically obtained neonatal videos. This was achieved through the development of a state-of-the-art face detector, in combination with an adapted region of interest tracker. Skin regions were then identified from the tracked face, from which photoplethysmogram and subsequently heart rate were extracted. Overall this thesis proposes state-of-the-art methods in the fields of neonatal audio and video analysis applied to cardio-respiratory monitoring.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat

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. 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. [Abstract copyright: © 2022. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.

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. 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