E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures

Abstract

Wearable technologies and digital phenotyping foster unique opportunities for designing novel intelligent electronic services that can address various well-being issues in patients with mental disorders (i.e., schizophrenia and bipolar disorder), thus having the potential to revolutionize psychiatry and its clinical practice. In this paper, we present e-Prevention, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders. The technologies offered through e-Prevention include: (i) long-term continuous recording of biometric and behavioral indices through a smartwatch; (ii) video recordings of patients while being interviewed by a clinician, using a tablet; (iii) automatic and systematic storage of these data in a dedicated Cloud server and; (iv) the ability of relapse detection and prediction. This paper focuses on the description of the e-Prevention system and the methodologies developed for the identification of feature representations that correlate with and can predict psychopathology and relapses in patients with mental disorders. Specifically, we tackle the problem of relapse detection and prediction using Machine and Deep Learning techniques on all collected data. The results are promising, indicating that such predictions could be made and leading eventually to the prediction of psychopathology and the prevention of relapses.

Person Identification and Relapse Detection From Continuous Recordings of Biosignals Challenge: Overview and Results

“…During the course of the e-Prevention project [1] (https: //eprevention.gr/), a total of 60 people (37 patients in the psychotic spectrum and 23 healthy controls) were recruited at the University Mental Health, Neurosciences and Precision Medicine Research Institute "Costas Stefanis" (UMHRI) in Greece, and the protocol of the project was approved by the Ethics Committee of the Institution. All participants were provided with a Samsung Gear S3 smartwatch that monitored the user's linear acceleration and angular velocity (m/s 2 and deg/s, sampled at 20Hz), heart rate variability and RR intervals (sampled at 5Hz), sleeping schedule and steps.…”

“…The collected data were anonymized, and each participant in the study was assigned a unique ID as an identifier. The clinicians annotated the patients' relapse periods according to their monthly assessments and communication with the attending physician or the family (more information about the recruitment and the monthly in-person clinical assessment can be found in [1]).…”

“…Another approach used for relapse detection is non-supervised sensor-based anomaly detection, the importance of which has been highlighted during recent years and the COVID-19 pandemic, through the clinical mass adoption of telehealth [26]. This approach is especially suitable for mental health monitoring, where the availability of data corresponding to relapsing states is scarce, and has been applied on data collected from various passive sensors [1], [21], [27]- [30]. Finally, with the increase in unlabeled amounts of data, self-supervised approaches are also emerging [31].…”

Psychotherapy 2.0 - Application Context and Effectiveness of Sensor Technology in Psychotherapy with Children and Adolescents: a Systematic Review (Preprint)

“…Sensors can be used to monitor mood states of affective disorders [32,38] or to investigate the relationship between depression and circadian rhythm disruption in patients [35] using an actigraphy device. Information resulting from sensors can also be used to assess the risk of relapse in major depressive disorder [36,37] by using digital questionnaires via smartphone in combination with measuring the patient's activity behavior to determine the risk of relapse.…”