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Detection of variations in holter ECG recordings based on dynamic cluster analysis

  • The proposed approach applies current unsupervised clustering approaches in a different dynamic manner. Instead of taking all the data as input and finding clusters among them, the given approach clusters Holter ECG data (longterm electrocardiography data from a holter monitor) on a given interval which enables a dynamic clustering approach (DCA). Therefore advanced clustering techniques based on the well known Dynamic TimeWarping algorithm are used. Having clusters e.g. on a daily basis, clusters can be compared by defining cluster shape properties. Doing this gives a measure for variation in unsupervised cluster shapes and may reveal unknown changes in healthiness. Embedding this approach into wearable devices offers advantages over the current techniques. On the one hand users get feedback if their ECG data characteristic changes unforeseeable over time which makes early detection possible. On the other hand cluster properties like biggest or smallest cluster may help a doctor in making diagnoses or observing several patients. Further, on found clusters known processing techniques like stress detection or arrhythmia classification may be applied.

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Metadaten
Author of HS ReutlingenMartínez Madrid, Natividad
DOI:https://doi.org/10.1007/978-3-319-19857-6_19
ISBN:978-331-91985-7-6
Erschienen in:Intelligent decision Technologies : Proceedings of the 7th KES International Conference on Intelligent Decision Technologies (KES-IDT 2015)
Publisher:Springer
Place of publication:Cham
Editor:Rui Neves-Silva
Document Type:Conference proceeding
Language:English
Publication year:2015
Tag:ECG holter; OPTICS clustering; dynamic cluster analysis; dynamic time warping
Page Number:9
First Page:209
Last Page:217
PPN:Im Katalog der Hochschule Reutlingen ansehen
DDC classes:006 Spezielle Computerverfahren
Open access?:Nein
Licence (German):License Logo  In Copyright - Urheberrechtlich geschützt