Learn More
ITAREPS presents a mobile phone-based telemedicine solution for weekly remote patient monitoring and disease management in schizophrenia and psychotic disorders in general. The programme provides health professionals with home telemonitoring via a PC-to-phone SMS platform that identifies prodromal symptoms of relapse, to enable early intervention and(More)
The paper focuses on processing of long biological signals used during monitoring procedures like in the case of portable Holter device for arrythmia analysis (ECG), intracranial pressure monitoring (ICP) in intensive care unit or overnight electroencephalogram monitoring (EEG) for sleep apnea detection. Two methods taken from speech processing are(More)
Both animal studies and studies using deep brain stimulation in humans have demonstrated the involvement of the subthalamic nucleus (STN) in motivational and emotional processes; however, participation of this nucleus in processing human emotion has not been investigated directly at the single-neuron level. We analyzed the relationship between the neuronal(More)
This paper focuses on wrapper-based feature selection for a 1-nearest neighbor classifier. We consider in particular the case of a small sample size with a few hundred instances, which is common in biomedical applications. We propose a technique for calculating the complete bootstrap for a 1-nearest-neighbor classifier (i.e., averaging over all desired(More)
In this paper, a method to reduce the baseline wandering of an electrocardiogram signal is presented. The method described is based on wavelet approximation of the whole signal. The main advantage of this method, compared with others, is the fact that this is a non-supervised method, allowing the process to be used in an off-line automatic analysis of(More)
In this paper, a method to automatically extract the main information from a long-term electrocardiographic signal is presented. This method is based on techniques of pattern recognition applied to speech processing, like dynamic time warping, and trace segmentation. In order to fulfill this objective, a clustering process is applied to the set of beats(More)
This work introduces a new concept of supporting elderly at their homes. The whole framework is being developed under OLDES project: Older People's e-services at home. OLDES aims at developing a very low cost and easy to use entertainment and health care platform designed to ease the life of older people in their homes. The platform is based on a PC(More)
The main goal of the study is an unsupervised classification of school children dyslexia. Eye movements of 49 subjects were measured using videooculographic technique (VOG) during two non-reading and one reading tasks. A feature selection was performed obtaining data set consisting of 26 features. Next an inductive modelling technique was applied to data(More)
The oculomotor role of the basal ganglia has been supported by extensive evidence, although their role in scanning eye movements is poorly understood. Nineteen Parkinsońs disease patients, which underwent implantation of deep brain stimulation electrodes, were investigated with simultaneous intraoperative microelectrode recordings and single channel(More)
We describe a clustering algorithm based on continuous Hidden Markov Models (HMM) to automatically classify both electrocardiogram (ECG) and intracranial pressure (ICP) beats based on their morphology. The algorithm detects, classifies and labels each beat based on morphology. In order to avoid the numerical problems with classical Expectation-Maximization(More)