A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms

Design and Implementation of Information Warehouse for Manufacturing Facility Supporting Holistic Energy Management

Energy management is one of the most critical tasks, which needs to be performed in manufacturing facility, since manufacturing consumes 1/3 of world’s energy. Same time, manufacturing facilities are equipped with large amounts of field devices, which generate vast amounts of information every second. With such a huge amount of real-time data, which has a potential to provide insight information for energy management needs, capturing, storing and processing of it becomes a challenge. In this thesis an information warehouse system supporting holistic energy management is designed and implemented. The main goal is to provide a system, which can capture, store and provide information relevant for energy management purposes in manufacturing facility. The thesis consists of three main parts. In the first part current and most relevant for energy management concepts and technologies, including Big Data, NoSQL, Service Oriented Architecture and Complex Event Processing, are explored, analyzed and compared. In the second part an architectural design of information warehouse is presented. During this step a set of tools and technologies is selected for implementation. In a third part, an information warehouse system is implemented and tested in a manufacturing line test-bed. Implemented information warehouse is based on multi-layered architectural pattern, where layers are communicating with each other via services. The most important advantage of this modular architecture is an ability to use implemented solution in any manufacturing facility, as modules can be easily reconfigured in order to adjust to different context. The designed information warehouse system was tested for a manufacturing line located in premises of Tampere University of Technology. The results of this thesis demonstrate that the developed information warehouse system is capable of collection, processing and providing access to crucial for energy management information