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Periodic activity in electroencephalography (PA-EEG) is shown as comprising a series of repetitive wave patterns that may appear in different cerebral regions and are due to many different pathologies. The diagnosis based on PA-EEG is an arduous task for experts in Clinical Neurophysiology, being mainly based on other clinical features of patients. Considering this difficulty in the diagnosis it is also very complicated to establish the prognosis of patients who present PAEEG. The goal of this paper is to propose a method capable of determining patient prognosis based on characteristics of the PA-EEG activity. The approach, based on a parallel classification architecture and a majority vote system has proven successful by obtaining a success rate of 81.94% in the classification of patient prognosis of our database. [ABSTRACT FROM AUTHOR]

This paper presents the single channel speech enhancement system using subband Kalman filtering by estimating optimal Autoregressive (AR) coefficients and variance for speech and noise, using Weighted Linear Prediction (WLP) and Noise Weighting Function (NWF). The system is applied for normal and Oesophageal speech signals. The method is evaluated by Perceptual Evaluation of Speech Quality (PESQ) score and Signal to Noise Ratio (SNR) improvement for normal speech and Harmonic to Noise Ratio (HNR) for Oesophageal Speech (OES). Compared with previous systems, the normal speech indicates 30% increase in PESQ score, 4 dB SNR improvement and OES shows 3 dB HNR improvement. [ABSTRACT FROM AUTHOR]

This paper presents a shoe-integrated sensor device which collects objective information concerning the gait quality in patients' physical rehabilitation. It involves four pressure sensors, two bending sensors, an ultrasonic sensor and a 9dof IMU, an Inertial Measurement Unit with three accelerometers, three gyroscopes and three magnetometers. The device includes a SDRAMPS with the aim of storing the information for long periods of time. The collected data can be sent to the server for later visualization by the specialist and the patient on a web platform. An interface shows the data in real time, allowing it to verify the connections and to check different movements. [ABSTRACT FROM AUTHOR]

This paper describes a study dealing with a technological solution to measure gait quality in people suffering from multiple sclerosis (MS) by selecting objective parameters that focus on their step. Android mobile technology, online services and four wireless pressure sensors are used in concert for this purpose. The objective of this work is the early detection of deterioration of the patient so that a physician can quickly intervene. Tests were carried out on a group of 8 persons with MS, and these results were compared with a control a group of 6 healthy participants. The results indicated a statistical difference in 7 of 40 general step features, with a minimum σ = 0.013 and a maximum σ = 0.029. These characteristics showed differences between first and fifth metatarsals for each group. It was concluded that these parameters can be used to evaluate gait degeneration in people with MS and that further information could be obtained from measurements with sensors to monitor activities such as bending and inertial sensors. [ABSTRACT FROM AUTHOR]