Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine

This collection, entitled Digital Health for Predictive, Preventive, Personalized and Participatory Medicine contains the proceedings of the first International conference on digital healthtechnologies (ICDHT 2018). Ten recent contributions in the fields of Artificial Intelligence (AI) and machine learning, Internet of Things (IoT) and data analysis, all applied to digital health. This collection enables researchers to learn about recent advances in the above mentioned fields. It brings a technological viewpoint of P4 medicine. Readers will discover how advanced Information Technology (IT) tools can be used for healthcare. For instance, the use of connected objects to monitor physiological parameters is discussed. Moreover, even if compressed sensing is nowadays a common acquisition technique, its use for IoT is presented in this collection through one of the pioneer works in the field.

In addition, the use of AI for epileptic seizure detection is also discussed as being one of the major concerns of predictive medicine both in industrialized and low-income countries.

This work is edited by Prof. Lotfi Chaari, professor at the University of Sfax, and previously at the University of Toulouse. This work comes after more than ten years of expertise in the biomedical signal and image processing field.

Introduction

PDF modeling and ensemble bagging classifer

Arti_cial Neuroplasticity by Deep Learning Reconstruc-tion Signal to Reconnect Motion

signal for Spinal Cord

Improved Massive MIMO Cylindrical Adaptive Anten-na Array

Multifractal Analysis With Lacunarity for Microcalci_cations Segmentation

Consolidated Clinical Document Architecture: Analysis and Evaluation to Support the

Interoperability of Tunisian Health

Bayesian compressed sensing for IoT: application to EEG recording

Patients Strati_cation in Imbalanced Datasets: A Roadmap

Real-Time Driver Fatigue Monitoring with Dynamic Bayesian Network Model

Epileptic seizure detection using a Convolutional Neural Network

Index