Objective: This study aimed to produce a novel Deep Learning (DL) model for the classification of subjects with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) subjects and Healthy Ageing (HA) subjects using resting-state scalp EEG signals.Approach: The raw EEG data were pre-processed to remove unwanted artefacts and sources of noise. The data were then processed with the Continuous Wavelet Transform (CWT), using the Morse mother wavelet, to create time-frequency graphs with a wavelet coefficient scale range of 0 to 600. The graphs were combined into tiled topographical maps governed by the 10-20 system orientation for scalp electrodes. The application of this processing pipeline was used on a data set of resting-state EEG samples from age-matched groups of 52 AD subjects (82.3 ± 4.7 years of age), 37 MCI subjects (78.4 ± 5.1 years of age) and 52 HA subjects (79.6 ± 6.0 years of age). This resulted in the formation of a data set of 16,197 topographical images. This image data set was then split into training, validation and test images and used as input to an AlexNet DL model. This model was comprised of 5 hidden convolutional layers and optimised for various parameters such as learning rate, learning rate schedule, optimiser, and batch size.Main Results: The performance was assessed by a 10-fold cross-validation strategy, which produced an average accuracy result of 98.9% ± 0.4% for the three-class classification of AD vs. MCI vs. HA. The results showed minimal overfitting and bias between classes, further indicating the strength of the model produced.Significance: These results provide significant improvement for this classification task compared to previous studies in this field and suggest that DL could contribute to the diagnosis of AD from EEG recordings.

Hip-worn triaxial accelerometers are widely used to assess physical activity in terms of energy expenditure. Methods for classification in terms of different types of activity of relevance to the skeleton in populations at risk of osteoporosis are not currently available. This publication aims to assess the accuracy of four machine learning models on binary (standing and walking) and tertiary (standing, walking, and jogging) classification tasks in postmenopausal women. Eighty women performed a shuttle test on an indoor track, of which thirty performed the same test on an indoor treadmill. The raw accelerometer data were pre-processed, converted into eighteen different features and then combined into nine unique feature sets. The four machine learning models were evaluated using three different validation methods. Using the leave-one-out validation method, the highest average accuracy for the binary classification model, 99.61%, was produced by a k-NN Manhattan classifier using a basic statistical feature set. For the tertiary classification model, the highest average accuracy, 94.04%, was produced by a k-NN Manhattan classifier using a feature set that included all 18 features. The methods and classifiers within this study can be applied to accelerometer data to more accurately characterize weight-bearing activity which are important to skeletal health.

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