Among the winners of the “Psychotic and Non-Psychotic Relapse Detection using Wearable-Based Digital Phenotyping” contest was the MagCIL lab of the Institute of Informatics and Telecommunications of NCSR Demokritos winning 2nd place.
The winning team includes:
– Panagiotis Kaliosis
– Sophia Eleftheriou
– Christos Nikos
– Thodoris Giannakopoulos
Furthermore, the winning teams will present their methods at ICASSP 2024.
About the contest:
The objective of the 2nd e-Prevention challenge was to stimulate innovative research on the prediction and identification of mental health relapses via the analysis and processing of the digital phenotype of patients in the psychotic spectrum. The challenge offered participants access to long-term continuous recordings of raw biosignals captured from wearable sensors – namely accelerometers, gyroscopes and heart rate monitors embedded in a smartwatch. Supplemental data such as sleep schedules, daily step count, and demographics were also made available.
Participants were evaluated on their ability to use this data to extract digital phenotypes that can effectively quantify behavioral patterns and traits. This was assessed across two distinct tasks: 1) Detection of non-psychotic relapses, and 2) Detection of psychotic relapses, both in patients within the psychotic spectrum.
The extensive data that will be used in this challenge have been sourced from the e-Prevention project, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders.
Data subsets from the e-Prevention project were previously used in the 1st e-Prevention challenge during ICASSP 2023. The challenge saw participation from 15 teams, some of which achieved outstanding state-of-the-art results on the person identification task and encouraging outcomes on the relapse detection task. Drawing from these results, the current challenge narrows its focus on both psychotic and non-psychotic relapses. Our goal is to inspire participants to design and refine solutions for relapse detection, thereby pushing boundaries in this important area of mental health care.
Detailed Results 2024
Track1 – Non-Psychotic Relapse Detection
Position | Team | AUROC | AUPRC | Total AVG | Institution | Country |
1 | Samsung R&D Institute Poland | 0.7110 | 0.6202 | 0.6656 | Samsung | Poland |
2 | MagCIL | 0.6512 | 0.6416 | 0.6464 | NCSR ”DEMOKRITOS” | Greece |
3 | Jackalope | 0.5949 | 0.5740 | 0.5844 | Technical University of Munich | Germany |
4 | SCRB-LUL | 0.6102 | 0.5255 | 0.5678 | Samsung | China |
5 | CHI-EIHW | 0.5796 | 0.5549 | 0.5673 | Τechnical University of Munich, University of Augsburg, Ιmperial College London | Germany & UK |
6 | SLTUoS | 0.5595 | 0.5067 | 0.5331 | University of Sheffield | UK |
7 | PerCeiVE | 0.5626 | 0.5029 | 0.5328 | University of Catania | Italy |
8 | ABCDZ | 0.4848 | 0.5124 | 0.4986 | University Politechnica of Bucharest | Romania |
Baseline | 0.5606 | 0.4851 | 0.5228 | |||
Random | 0.5 | 0.4298 | 0.4649 |
Track2 – Psychotic Relapse Detection
Position | Team | AUROC | AUPRC | Total AVG | Institution | Country |
1 | Jackalope | 0.5632 | 0.4443 | 0.5037 | Technical University of Munich | Germany |
2 | CHI-EIHW | 0.4930 | 0.5053 | 0.4991 | Τechnical University of Munich, University of Augsburg, Ιmperial College London | Germany & UK |
3 | SCRB-LUL | 0.5690 | 0.4237 | 0.4964 | Samsung | China |
4 | Samsung R&D Institute Poland | 0.5110 | 0.4775 | 0.4943 | Samsung | Poland |
5 | ABCDZ | 0.5480 | 0.4111 | 0.4795 | University Politechnica of Bucharest | Romania |
6 | SLTUoS | 0.5261 | 0.3976 | 0.4618 | University of Sheffield | UK |
7 | MagCIL | 0.4497 | 0.3876 | 0.4187 | NCSR ”DEMOKRITOS” | Greece |
8 | PerCeiVE | 0.4527 | 0.3678 | 0.4103 | University of Catania | Italy |
Baseline | 0.5477 | 0.4116 | 0.4797 | |||
Random | 0.5 | 0.3471 | 0.424 |
Funding: This research has been 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 acronym: e-Prevention, code: T1EDK-02890/MIS: 5032797).