Tourism time series forecast

In this chapter four combinations of input features and the feedforward, cascade forward and recurrent architectures are compared for the task of forecast tourism time series. The input features of the ANNs consist in the combination of the previous 12 months, the index time modeled by two nodes used to the year and month and one input with the daily hours of sunshine (insolation duration). The index time features associated to the previous twelve values of the time series proved its relevance in this forecast task. The insolation variable can improved results with some architectures, namely the cascade forward architecture. Finally, the experimented ANN models/architectures produced a mean absolute percentage error between 4 and 6%, proving the ability of the ANN models based to forecast this time series. Besides, the feedforward architecture behaved better considering validation and test sets, with 4.2% percentage error in test set.

Ambulatory electrocardiogram prototype

The use of electrocardiogram nowadays, is very important in diagnosis of heart disease. The emergent increase of portable technology provides medica] monitoring of vital signs allowing freedom ofmovement and watching during normal activity of the patient. In this shidy, it is described the development of a prototype of an ambulatory cardiac monitoring system using 3 leads. The systems consists on conversion of an analog signal, having been previously processed and conditioned, into digital ECG signal and after processed with a microcontroller (MCU). The heartbeat rate can be observed in an LCD display. The LCD display is also used as the interface during the setup process. Ali digital data stream can be stored on a SD memory card llowing the ECG signa] to be accessed later on a PC.

Electroencephalogram cepstral distances in alzheimer’s disease diagnosis

Alzheimer's disease (AD) represents one ofthe greatest public health challenges worldwide nowadays, because it affects millions of people ali o ver the world and it is expected that the disease will increase considerably in the near future. This study is the first application attempt of cepstral analysis on Electroencephalogram (EEG) signals to find new parameters in arder to achieve a better differentiation belween EEGs of AD patients and Control subjects. The results show that the methodology that uses a combined Wavelet (WT) Biorthogonal (Bior) 3.5 and cepstrum analysis was able to describe the EEG dynamics with a higher discriminative power than the other WTs/spectmm methodologies m previous studies. The most important significance figures were found in cepstral distances between cepstrums oftheta and alpha bands (p=0. 00006<0. 05).