Using a translation-invariant neural network to diagnose heart arrhythmia
Advances in neural information processing systems 2
ACM Computing Surveys (CSUR)
Design of Microcomputer-Based Medical Instrumentation
Design of Microcomputer-Based Medical Instrumentation
Alternatives to the k-means algorithm that find better clusterings
Proceedings of the eleventh international conference on Information and knowledge management
Learning structure and concepts in data through data clustering
Learning structure and concepts in data through data clustering
EURASIP Journal on Applied Signal Processing
A new arrhythmia clustering technique based on Ant Colony Optimization
Journal of Biomedical Informatics
Computer Methods and Programs in Biomedicine
Clustering by competitive agglomeration
Pattern Recognition
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This paper presents a personalized long-term electrocardiogram (ECG) classification framework, which addresses the problem within a long-term ECG signal, known as Holter register, recorded from an individual patient. Due to the massive amount of ECG beats in a Holter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore, the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so-called master key-beats) each of which is automatically extracted from a time frame of homogeneous (similar) beats. We tested the system on a benchmark database where beats of each Holter register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and thus we used exhaustiveK-means clustering in order to find out (near-) optimal number of key-beats as well as the master key-beats. The classification process produced results that were consistent with the manual labels with over 99% average accuracy, which basically shows the efficiency and the robustness of the proposed system over massive data (feature) collections in high dimensions.