A neuron, with axon, dendrites and the cell body (soma). 

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... neuron has a very simple computational capability in itself, but by working together in vast neural networks they together form the complex system that is the human brain. The neuron generally has several different incoming connections, called dendrites, and one outgoing connection, called an axon ( Figure 1). The axon of a neuron usually connects to dendrites of other neurons through synapses. ...

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... the case of classifying patterns, the threshold in the one-dimensional case becomes a hyperplane in an N-dimensional space, where N is the number of features used to describe the signal. This is exemplified in Figure 10 for the cases of two and three dimensions. ...

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... ML classifier was implemented by using training data to estimate the pdfs by e.g. calculating and smoothing the histograms for the two classes ( Figure 11). The areas of the histograms were then normalized to one. ...

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... S-shape gives the functions the property that they act linearly around the origin, while large inputs are squeezed into the interval zero to one. In the implementation used in this project, the hyperbolic tangent function was used (Figure 12). The neuron can graphically be visualized as in Figure 13. ...

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... the implementation used in this project, the hyperbolic tangent function was used (Figure 12). The neuron can graphically be visualized as in Figure 13. In this example, it has three inputs and one output, but the model allows for any number of inputs. ...

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... S-shape inside the neuron illustrates the sigmoid activation function that is applied to the output. The multilayer perceptron (Figure 14) consists of any number of layers of neurons, although two (or three, if inputs are considered to be neurons) is the most common. The outputs from the first layer are connected to the inputs of the next layer in a strictly forward manner, hence the designation "feed- forward" network. ...

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... was avoided by using early stopping, meaning that the error on the evaluation set was monitored and a training length that resulted in a reasonable trade-off for all different patients was chosen. Figure 15 shows an example of how the sensitivity, specificity and AUC evolve during training. Initially, all samples are classified as suppression, resulting in 100% specificity and zero sensitivity. ...

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... contradiction between the gradual increase in sensitivity and the very quick increase in AUC arises because the AUC calculation does not depend on a specific threshold value. Figure 16 shows a visualization of the network weights. These determine how large an influence the inputs have on each hidden neuron, and how much influence each hidden neuron has on the final summation in the output neuron. ...

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... Maximizing the margin of classification In short, the first part means that the SVM uses a function to transform the problem nonlinearly and then construct a nonlinear decision boundary using linear techniques. The second part means that while a classifier such as an ANN settles for any boundary that separates two classes in the training data from each other, the SVM finds the one that maximizes the margin ( Figure 17), a property that often will increase the generalization ability of the classifier. The SVM also has the property that only the samples near the decision boundary are involved in the computations. ...

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... the ANN is trained by updating its weights with an amount proportional to any randomly chosen misclassified training sample, the SVM can be trained by choosing the current worst-classified pattern. When the training is finished, these patterns would be the support vectors, the patterns on the margins that define the optimal separating hyperplane (Figure 18). This method of training the SVM, however, is feasible only for very small datasets, because it would mean that all training samples have to be searched in each iteration in order to find the worst-classified sample. ...

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... performance values varied significantly for the different patients, so the final values were chosen to give decent performance for all cases, rather than selecting a parameter setting that would have given almost perfect performance in some cases and zero performance in others. Figure 19 shows an example of AUC values plotted in a grid of parameter values. Note that the surfaces for the different patients are located at different heights, and that the gradients for increasing performance point in different directions. ...

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... results depend on what parameters are chosen when training the classifier, as described in the previous chapters concerning the different classification methods. Figure 21 shows the same type of example as Figure 20, but is based on the feature data in Figure 8, from another patient. In this case there is one burst that is completely missed, and there are two artifacts that are classified as bursts. ...