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

An Extended Framework for Default Reasoning

In this paper, we investigate the proof theory of default reasoning. We generalize Reiter's framework to a monotonic reasoning system, and in particular allow formulae with nested defaults. We give proof rules for this extended default logic, called default ionic logic, and give deduction theorems. We also give examples of applications of our framework to some well-known problems: weak implication, disjunctive information, default transformation, and normal versus non-normal defaults. 1 Introduction In this paper we investigate the proof theory of default reasoning. We have as a goal a general theory for combining defaults, and a logical tool for choosing among defaults for implementation purposes. The calculus on extensions developed by Reiter [6] has yielded a kind of reasoning that has been called non-monotonic. This non-monotonicity is the source of some major problems for implementers. The applicability of modus tollens, a fundamental tool in resolution, is unclear. For example ..

An Extended Framework for Default Reasoning

In this paper, we investigate the proof theory of default reasoning. We generalize Reiter's framework to a monotonic reasoning system, and in particular allow formulae with nested defaults. We give proof rules for this extended default logic, called default ionic logic, and give deduction theorems. We also give examples of applications of our framework to some well-known problems: weak implication, disjunctive information, default transformation, and normal versus non-normal defaults. 1 Introduction In this paper we investigate the proof theory of default reasoning. We have as a goal a general theory for combining defaults, and a logical tool for choosing among defaults for implementation purposes. The calculus on extensions developed by Reiter [6] has yielded a kind of reasoning that has been called non-monotonic. This non-monotonicity is the source of some major problems for implementers. The applicability of modus tollens, a fundamental tool in resolution, is unclear. For example ..