TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Convolutional Neural Networks for patient-specific ECG classification

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Sivut2608-2611
Sivumäärä4
DOI - pysyväislinkit
TilaJulkaistu - 1 elokuuta 2015
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY -
Kesto: 1 tammikuuta 1900 → …

Conference

ConferenceANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY
Ajanjakso1/01/00 → …

Tiivistelmä

We propose a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system using an adaptive implementation of 1D Convolutional Neural Networks (CNNs) that can fuse feature extraction and classification into a unified learner. In this way, a dedicated CNN will be trained for each patient by using relatively small common and patient-specific training data and thus it can also be used to classify long ECG records such as Holter registers in a fast and accurate manner. Alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. The experimental results demonstrate that the proposed system achieves a superior classification performance for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB).