Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding-An Interdisciplinary Project.

Artificial intelligence (AI) is gaining increasing interest in the field of medicine because of its capacity to process big data and pattern recognition. Cardiotocography (CTG) is widely used for the assessment of foetal well-being and uterine contractions during pregnancy and labour. It is characterised by inter- and intraobserver variability in interpretation, which depends on the observers' experience.

Artificial intelligence and machine learning in cardiotocography: A scoping review.

Introduction: Artificial intelligence (AI) is gaining more interest in the field of medicine due to its capacity to learn patterns directly from data. This becomes interesting for the field of cardiotocography (CTG) interpretation, since it promises to remove existing biases and improve the well-known issues of inter- and intra-observer variability.

Material And Methods: The objective of this study was to map current knowledge in AI-assisted interpretation of CTG tracings and thus, to present different approaches with their strengths, gaps, and limitations.

Evaluation of a Wearable System for Fetal ECG Monitoring Using Cooperative Sensors.

Fetal electrocardiography (fECG) has gotten widespread interest in the last years as technology for fetal monitoring. Compared to cardiotocography (CTG), the current state of the art, it can be designed in smaller formfactor and is thus suited for long-term and unsupervised monitoring. In the present study we evaluated a wearable system which is based on CSEM's cooperative sensors, a versatile technology that allows for the measurement of multiple biosignals and an easy integration into a garment or patch.

Ultra-low-power Physical Activity Classifier for Wearables: From Generic MCUs to ASICs.

In the era of Internet of Things (IoT), an increasing amount of sensors is being integrated into intelligent wearable devices. These sensors have the potential to produce a large quantity of physiological data streams to be analyzed in order to produce meaningful and actionable information. An important part of this processing is usually located in the device itself and takes the form of embedded algorithms which are executed into the onboard microcontroller (MCU).

Atrial Fibrillation Detection on Low-Power Wearables using Knowledge Distillation.

The increasing complexity and memory requirements of neural networks have been slowing down the adoption of AI in low-power wearable devices, which impose important restrictions in computational power and memory footprint. These low-power systems are the key to obtain 24/7 monitoring systems necessary for the current personalized healthcare trend since they do not require constant charging. In this work, we apply Knowledge Distillation to our previously published convolutional-recurrent neural network for cardiac arrhythmia detection and classification.