Fusing actigraphy signals for outpatient monitoring

Abstract

ElsevierFuster García, E.; Bresó Guardado, A.; Martínez Miranda, JC.; Rosell-Ferrer, J.; Matheson, C.; García Gómez, JM. (2015) AbstractActigraphy devices have been successfully used as effective tools in the treatment of diseases such as sleep disorders or major depression. Although several efforts have been made in recent years to develop smaller and more portable devices, the features necessary for the continuous monitoring of outpatients require a less intrusive, obstructive and stigmatizing acquisition system. A useful strategy to overcome these limitations is based on adapting the monitoring system to the patient lifestyle and behavior by providing sets of different sensors that can be worn simultaneously or alternatively. This strategy offers to the patient the option of using one device or other according to his/her particular preferences. However this strategy requires a robust multi-sensor fusion methodology capable of taking maximum profit from all of the recorded information. With this aim, this study proposes two actigraphy fusion models including centralized and distributed architectures based on artificial neural networks. These novel fusion methods were tested both on synthetic datasets and real datasets, providing a parametric characterization of the models' behavior, and yielding results based on real case applications. The results obtained using both proposed fusion models exhibit good performance in terms of robustness to signal degradation, as well as a * good behavior in terms of the dependence of signal quality on the number of signals fused. The distributed and centralized fusion methods reduce the mean averaged error of the original signals to 44% and 46% respectively when using simulated datasets. The proposed methods may therefore facilitate a less intrusive and more dependable way of acquiring valuable monitoring information from outpatients.

Feasibility and Conceptualization of an e-Mental Health Treatment for Depression in Older Adults: Mixed-Methods Study

“…In 17 of the 19 analyzed studies, positive evidence of effectiveness was found. Averaged across all disorder patterns and forms of intervention, the average treatment effect was 0.53, which is quite similar to effect sizes for traditional outpatient treatment [25].…”

“…Similarly, a study by Spek and colleagues [23] that specifically looked at elderly (aged 50 years and older) showed that cCBT was at least as effective as a commonly used group CBT intervention for subthreshold depression. In addition to the efficacy studies of individual cCBT interventions, there are now several international meta-analyses that provide sufficient evidence that computer-assisted cognitive behavioral interventions are generally effective [25]. In 17 of the 19 analyzed studies, positive evidence of effectiveness was found.…”

E-Mental Health Anwendungen für depressive und suizidale Menschen: eine Übersicht

“…In ähnlicher Weise zeigte die Studie von Spek et al (2007), die sich speziell mit älteren Menschen (50 Jahre und älter) befasste, dass cCBT mindestens genauso wirksam war wie eine häufig verwendete Gruppe-CBT-Intervention bei Depression. Neben den Wirksamkeitsstudien einzelner cCBT-Interventionen gibt es inzwischen mehrere internationale Metaanalysen, die hinreichend belegen, dass computergestützte kognitive Verhaltensinterventionen im Allgemeinen effektiv sind (Fuster-Garcia et al 2015). In 17 der 19 untersuchten Studien wurde ein positiver Wirksamkeitsnachweis gefunden.…”

A novel approach to improve the planning of adaptive and interactive sessions for the treatment of Major Depression

“…The Data Processing Layer is composed of four modules (their main features are listed in Table 1) that generates the adapted sessions' content: 1. Fusion of the actigraphy signals obtained by multiple sensors using a novel multi-sensor fusion methodology described in [36].…”