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.

This chapter presents recents advances in digital health technologies Promising applications using Internet of Things (IoT) for health are presented How Artificial Intelligence (AI) can bring innovation in digital healthcare

Auteur
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 medical biomedical signal and image processing field.

Chapter 1: L. Chaari: Introduction

Chapter 2: J. Diaz. Ricardo, J. M. L. Veronica and B. M. Alejandra: Artificial Neuroplasticity by Deep Learning Reconstruc-tion Signal to Reconnect Motion signal for Spinal Cord.

Chapter 3: M. Kamali and A. Cherif: Improved Massive MIMO Cylindrical Adaptive Antenna Array*.*

Chapter 4: I. Slim, H. Bettaieb, A. Ben Abdallah, I Bhouri and M. H. Bedoui: Multifractal analysis with lacunarity for microcalcifications segmentation.

Chapter 5: D. Ben Ali, I. Ghorbel, N. Gharbi, K. Belhaj Hmida and F. Gargouri: Consolidated Clinical Document Architecture: Analysis and Evaluation to Support the Interoperability of Tunisian Health Systems.

Chapter 6: I. Ghorbel, W. Gharbi, L. Chaari and A. Benazza: Bayesian compressed sensing for IoT: application to EEG recording*.*

Chapter 7: C. Karray, N. Gharbi and M. Jmaiel: Patients Stratification in Imbalanced Datasets: A Roadmap*.*

Chapter 8: I. Bani, B. Akrout and W. Mahdi: Real-Time Driver Fatigue Monitoring with

Dynamic Bayesian Network Model*.*

Chapter 9: B. Bouaziz, L. Chaari, H. Batatia and A. Quintero-Rincon: Epileptic seizure detection using a Convolutional Neural Network*.*

Chapter 10: A. Quintero-Rincon, C. D'Giano and H. Batatia: Seizure onset detection in EEG signals based on entropy from generalized Gaussian PDF modeling and ensemble bagging classifier*.*

Texte du rabat
This collection, entitled « Digital Health for Predictive, Preventive, Personalized and Participatory Medicine » contains the proceedings of the first International conference on digital health technologies (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 isalso 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.

Contenu
Preface.- Introduction.- Seizure onset detection in EEG signals based on entropy from generalized Gaussian 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.