First steps in adaptation of an evidential network for data fusion in the framework of medical remote monitoring.

This paper presents a medical remote monitoring application which aims at detecting falls. The detection system is based on three modalities: a wearable sensor, infrared sensors and a sound analysis module. The sound analysis is presented briefly. The multimodal fusion is made using the Dempster Schaffer theory through Evidential Network. A first evaluation of the use of data mining techniques in order to extract blindly data representatives is proposed. These representatives are used to continuously increase the system performances. The system is evaluated on a local recorded data base.

Ischemia detection based on HMM beat segmentation and ST segment analysis

Thispaper describes an algorithm for ischemia monitoring in ambulatory electrocardiography. It relies on an automatic ST-segment anlysis. Orecise information about ST segment changes are provided by an original hidden Markov models (HMM) aproach for on line beat segmentation. The performance was evaluated on the two-channel European ST T database, according to its ST episode definitions ...

Neural correlates of learning in the prefrontal cortex of the monkey: a predictive model.

The principles underlying the organization and operation of the prefrontal cortex have been addressed by neural network modeling. The involvement of the prefrontal cortex in the temporal organization of behavior can be defined by processing units that switch between two stable states of activity (bistable behavior) in response to synaptic inputs. Long-term representation of programs requiring short-term memory can result from activity-dependent modifications of the synaptic transmission controlling the bistable behavior. After learning, the sustained activity of a given neuron represents the selective memorization of a past event, the selective anticipation of a future event, and the predictability of reinforcement. A simulated neural network illustrates the abilities of the model (1) to learn, via a natural step-by-step training protocol, the paradigmatic task (delayed response) used for testing prefrontal neurons in primates, (2) to display the same categories of neuronal activities, and (3) to predict how they change during learning. In agreement with experimental data, two main types of activity contribute to the adaptive properties of the network. The first is transient activity time-locked to events of the task and its profile remains constant during successive training stages. The second is sustained activity that undergoes nonmonotonic changes with changes in reward contingency that occur during the transition between stages.