Fast Classification of Electrocardiograph Signals via Instance Selection

Abstract

Abstract-In clinical practice, electrocardiographs (ECG) are used in various ways. In the most simple case, directly after the ECG has been recorded, the doctor analyses it and makes the diagnosis. In other cases, e.g. when the abnormality can only be observed occasionally, at a previously unknown time, the ECG is being recorded continuously. Fast automatic recognition of abnormalities of ECG signals may substantially support doctors' work in both cases: either by immediately displaying a warning or calling the emergency service in case of danger or by pointing to the abnormal parts of a long ECG-signal in order to support analysis and diagnosis. In this paper, we focus on the (semi-)automated recognition of abnormal ECG signals. We formulate the task as a time-series classification problem, point out that state-of-the-art solutions are capable to solve this problem with a high accuracy. The recognition time is, however, crucial in our case. Therefore, as major contribution, we aim at speeding up the recognition by a new instance selection technique. We describe this technique and discuss its theoretical background. In our experiments on publicly available real ECG-data, we empirically evaluate our approach and show that it outperforms a state-of-the-art instance selection technique.

Subject identification via ECG fiducial-based systems: Influence of the type of QT interval correction

“…Specifically, since it is regarded as highly effective in ECG-related verification or identification tasks [16,17], we employed a 1 Nearest Neighbour (1-NN) classifier using the Dynamic Time Warping (DTW) as distance measure. The DTW public domain implementation provided by [39] was adopted here.…”

“…To further extend our analyses, we compared the results obtained with our fiducial-based approaches with respect to a non fiducialbased solution. Since some authors [16,17] proposed the use of Dynamic Time Warping (DTW) based approaches for ECG Identification, we compared our best finding with the performance of a Nearest Neighbour classifier using the DTW as distance measure.…”

Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

“…Therefore, many false alarm results often occur when these works are applied to real ECG data, as shown in Figure 3 . Many works [ 21 , 35 , 36 , 48 ] required a fixed length of result as an input parameter from users. To determine the length, it is, in fact, very difficult to know what the proper length is.…”

Instance Selection Techniques in Reduction of Data Streams Derived from Medical Devices

“…the percentage of abnormal values in relation to the whole dataset. Instance selection aims to reject most of the data stream elements and preserve only the most informative elements [11]. In comparison to basic sampling techniques, it depends on the task, while sampling process can be generalized [12].…”