e-Prevention

Project Details

Co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code:T1EDK-02890).

About the Project

The goal of the e-Prevention project is to develop innovative and advanced remote electronic services for medical support that will facilitate effective treatment monitoring and relapse prevention in patients with psychotic disorders (i.e., bipolar disorder and schizophrenia).

e-Prevention will develop a novel intelligent system which will offer the possibility for timely diagnosis of psychotic symptom’s relapses and adverse medicine side effects by combining:

1. long-term continuous recordings of biometric indexes through simple wearable sensors (i.e., smartwatches),
2. a portable device (tablet) that is used to record short-term audio-visual videos of the patient while communicating with the clinical personnel on weekly basis,
3. parallel studies, medical diagnosis and decisions taken by the psychiatric research group, and
4. development of an intelligent data processing and recognition system, which will be based on Cloud computing and processing of large-scale (big) data, providing statistical measurements, detections and estimates of changes and patterns that will facilitate the prediction of clinical symptoms and side effects of the patient’s medication.

Work Plan

• WP1: Data collection from healthy volunteers and patients. Patient Monitoring-Treatment. Model Evaluation
• WP2: Monitoring sensors and cloud computing infrastructure
• WP3: Multimodal data processing for recognition of changes and trends
• WP4: Development of the integrated e-Prevention System
• WP5: Commercial exploitation of results

Areas / Keywords

Psychotic relapse, prevention of psychotic episode, antipsychotic medication, mood stabilized induced tremor, schizophrenia, bipolar disorder.

Signal processing, pattern recognition and machine learning, computer vision and image processing, multimodal human-computer interaction, multi-sensory processing, parallel and distributed processing, cloud systems/computer architecture, biomedical information systems.

• 
  National Technical University of Athens
  School of Electrical and Computer Engineering, Intelligent Robotics
  and Automation Laboratory
  (IRAL)
  P.I. & Scientific Director: Prof. Petros Maragos
  (NTUA)
  

  Team
  Prof. Panagiotis Tsanakas
  Prof. Ilias Maglogiannis (Dept. of Digital Systems, Univ. of
  Piraeus)
  Assoc. Prof Gerasimos Potamianos (Dept. of ECE, Univ. of
  Thessaly)
  Dr. Athanasia Zlatintsi
  Dr. Georgios Retsinas
  Dr. Andreas Menychtas
  Dr. Dimitra Georgiou
  Niki Efthymiou
  Panagiotis-Paraskevas Filntisis
  Melina Tziomaka
  Dr. Vrettos Moulos

• 
  University Mental Health, Neurosciences and Precision Medicine Research
  Institute “COSTAS STEFANIS”
  Laboratory of Cognitive Neuroscience and Sensoriomotor Control
  P. I.: Prof Nikolaos Smyrnis (School of Medicine, National
  Kapodistrian Univ. Athens)
  

  Team
  Dr. Thomas Karantinos
  Vasia Garyfalli
  MD Manolis Kalisperakis
  MD Makis Mantas
  MD Marina Lazaridi

• 
  Blockachain
  Blockachain, a custom software engineering company, operating as a
  full-service software development company. Blockachain exploits modern
  design principles, along with the latest blockchain, cloud, mobile and
  desktop technologies to deliver software of best-in-class performance at
  affordable prices.
  P. I.: Thomas Sounapoglou
  

  Team
  Athanasios Gkoumas
  Evaggelia Mpogiatzi
  Gianna Papaplioura
  Kostas Romanidis

Publications

• G. Retsinas et al., Person Identification Using Deep Convolutional Neural Networks On Short-term Signals From Wearable Sensors, ICASSP-2020
• I. Maglogiannis et al., An intelligent cloud-based platform for effective monitoring of patients with psychotic disorders, AIAI-2020
• P. P. Filntisis et al., Wearable-based Long-term Digital Phenotyping and Relevant Markers Identification in Patients with Mental Disorders, JBHI-2021 (Under review)
• P. P. Filntisis et al., Physical activity and autonomic function patterns between psychotic patients and controls over longitudinal wearable sensors recording, ICHI-2021 (Submitted)
• N. Dimitriadis et al., Advances in Morphological Neural Networks: Training, Pruning and Enforcing Shape Constraints, ICASSP-2021
• N. Tsilivis et al., Sparsity in Max-Plus Algebra and Applications in Multivariate Convex, ICASSP 2021
• E. Theodosis et al., Tropical Modeling Of Weighted Transducer Algorithms On Graphs, ICASSP-2019
• M. Tziomaka et al., Evaluating mental patients utilizing video analysis of facial expressions, AIAI-2021
• C. Garoufis et al., An Unsupervised Learning Approach for Detecting Relapses from Spontaneous Speech in Patients with Psychosis, BHI-2021

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