Now showing 1 - 2 of 2
  • Publication
    Exploring Composite Dataset Biases for Heart Sound Classification
    (CEUR Workshop Proceedings, 2020-12-08) ; ;
    In the last few years, the automatic classification of heart sounds has been widely studied as a screening method for heart disease. Some of these studies have achieved high accuracies in heart abnormality prediction. However, for such models to assist clinicians in the detection of heart abnormalities, it is of critical importance that they are generalisable, working on unseen real-world data. Despite the importance of generalisability, the presence of bias in the leading heart sound datasets used in these studies has remained unexplored. In this paper, we explore the presence of potential bias in heart sound datasets. Using a small set of spectral features for heart sound representation, we demonstrate experimentally that it is possible to detect sub-datasets of PhysioNet, the leading dataset of the field, with 98% accuracy. We also show that sensors which have been used to capture recordings of each dataset are likely the main cause of the bias in these datasets. Lack of awareness of this bias works against generalised models for heart sound diagnostics. Our findings call for further research on the bias issue in heart sound datasets and its impact on the generalisability of heart abnormality prediction models.
  • Publication
    Activity recognition using temporal evidence theory
    The ability to identify the behavior of people in a home is at the core of Smart Home functionality. Such environments are equipped with sensors that unobtrusively capture information about the occupants. Reasoning mechanisms transform the technical, frequently noisy data of sensors into meaningful interpretations of occupant activities. Time is a natural human way to reason about activities. People's activities in the home often have an identifiable routine; activities place at distinct times throughout the day and last for predicable lengths of time. However, the inclusion of temporal information is still limited in the domain of activity recognition. Evidence theory is gaining increasing interest in the field of activity recognition, and is suited to the incorporation of time related domain knowledge into the reasoning process. In this paper, an evidential reasoning framework that incorporates temporal knowledge is presented. We evaluate the effectiveness of the framework using a third party published smart home dataset. An improvement in activity recognition of 70% is achieved when time patterns and activity durations are included in activity recognition. We also compare our approach with Naïve Bayes classifier and J48 Decision Tree, with temporal evidence theory achieving higher accuracies than both classifiers.
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