Automatic quality classification of entire electrocardiographic recordings obtained with a novel patch type recorder

TY - JOUR

T1 - Automatic quality classification of entire electrocardiographic recordings obtained with a novel patch type recorder

AU - Saadi, Dorthe B

AU - Hoppe, Karsten

AU - Egstrup, Kenneth

AU - Jennum, Poul

AU - Iversen, Helle K

AU - Jeppesen, Jørgen L

AU - Sorensen, Helge B D

PY - 2014/11/2

Y1 - 2014/11/2

N2 - Recently, new patch type electrocardiogram (ECG) recorders have reached the market. These new devices possess a number of advantages compared to the traditional Holter recorders. This forms the basis of questions related to benefits and drawbacks of different ambulatory ECG recording techniques. One of the important questions is the ability to obtain high clinical quality of the recordings during the entire monitoring period. It is thus desirable to be able to obtain an automatic estimate of the global quality of entire ECG recordings. The purpose of this pilot study is therefore to design an algorithm for automatic classification of entire ECG recordings into the groups "noisy" and "clean" recordings. This novel algorithm is based on three features and a simple Bayes classifier. The algorithm was tested on 40 ECG recordings in a five-fold cross validation scheme and it obtained an average accuracy of 90% on the test data.

AB - Recently, new patch type electrocardiogram (ECG) recorders have reached the market. These new devices possess a number of advantages compared to the traditional Holter recorders. This forms the basis of questions related to benefits and drawbacks of different ambulatory ECG recording techniques. One of the important questions is the ability to obtain high clinical quality of the recordings during the entire monitoring period. It is thus desirable to be able to obtain an automatic estimate of the global quality of entire ECG recordings. The purpose of this pilot study is therefore to design an algorithm for automatic classification of entire ECG recordings into the groups "noisy" and "clean" recordings. This novel algorithm is based on three features and a simple Bayes classifier. The algorithm was tested on 40 ECG recordings in a five-fold cross validation scheme and it obtained an average accuracy of 90% on the test data.

U2 - 10.1109/embc.2014.6944906

DO - 10.1109/embc.2014.6944906

M3 - Journal article

C2 - 25571274

SN - 0589-1019

VL - 2014

SP - 5639

EP - 5642

JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

ER -