Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
BI'10 Proceedings of the 2010 international conference on Brain informatics
A novel emergency telemedicine system based on wireless communication technology-AMBULANCE
IEEE Transactions on Information Technology in Biomedicine
The Role of Nonlinear Dynamics in Affective Valence and Arousal Recognition
IEEE Transactions on Affective Computing
A mobile data collection platform for mental health research
Personal and Ubiquitous Computing
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Background: Bipolar disorders are characterized by a series of both depressive and manic or hypomanic episodes. Although common and expensive to treat, the clinical assessment of bipolar disorder is still ill-defined. Objective: In the current literature several correlations between mood disorders and dysfunctions involving the autonomic nervous system (ANS) can be found. The objective of this work is to develop a novel mood recognition system based on a pervasive, wearable and personalized monitoring system using ANS-related biosignals. Materials and methods: The monitoring platform used in this study is the core sensing system of the personalized monitoring systems for care in mental health (PSYCHE) European project. It is comprised of a comfortable sensorized t-shirt that can acquire the inter-beat interval time series, the heart rate, and the respiratory dynamics for long-term monitoring during the day and overnight. In this study, three bipolar patients were followed for a period of 90 days during which up to six monitoring sessions and psychophysical evaluations were performed for each patient. Specific signal processing techniques and artificial intelligence algorithms were applied to analyze more than 120h of data. Results: Experimental results are expressed in terms of confusion matrices and an exhaustive descriptive statistics of the most relevant features is reported as well. A classification accuracy of about 97% is achieved for the intra-subject analysis. Such an accuracy was found in distinguishing relatively good affective balance state (euthymia) from severe clinical states (severe depression and mixed state) and is lower in distinguishing euthymia from the milder states (accuracy up to 88%). Conclusions: The PSYCHE platform could provide a viable decision support system in order to improve mood assessment in patient care. Evidences about the correlation between mood disorders and ANS dysfunctions were found and the obtained results are promising for an effective biosignal-based mood recognition.