BIOSIGNAL 2002 Automatic Extraction Of Significant Beats From A Holter

BIOSIGNAL 2002

Automatic Extraction Of Significant Beats From A Holter

Register

Cuesta-Frau David 1, Pérez-Cortés Juan Carlos 1, Andreu-García Gabriela 1, Novák Daniel 2 1 Polytechnic University of Valencia, 2 Czech Technical University in Prague

dcuesta@disca.upv.es

Abstract. Holter signals correspond to long-term electrocardiograph registers. Manual

inspection of such signals is difficult because of the enormous quantity of beats involved. In

this paper, a method to automatically detect and separate the significant beats from the

whole register is presented. The method is based on techniques applied to speech processing,

such as dynamic programming, trace segmentation, and clustering algorithms. In order to

validate the procedure, an experimental comparative study is carried out, utilizing records

from the MIT database.

1 Introduction

Holter signals are ambulatory long-term electrocardiographic registers used to detect heart

diseases which are difficult to find in normal electrocardiograms. These signals normally

include a quantity of beats greater than 100000. It is obvious that the task of examining every

beat present within Holter registers takes a lot of time, and it is quite likely some beats could

be omitted in the visual inspection because of subjective reasons. Nevertheless, these signals

include many similar beats, and only a few are different. Therefore, it would be very useful to

have a method to simplify Holter registers prior to their visual inspection. This process

should yield a beat set in such a way that redundant beats are discarded and only those

significant from the diagnosis point of view are included in the set finally examined. So, the

quantity of beats presented to the doctors for their inspection would be meaningfully lower.

In this work, a method to extract significant beats from a Holter signal is presented. The

method is not supervised, and different approaches have been tested in order to perform a

comparative study. The experiments have been carried out using registers from the MIT

Database, obtained through the Physionet web server [2]. Manual labelling of some of these

registers have been carried out by a cardiologist of the Alcoi Hospital.

2 Methods

The signals are segmented using a QRS detection algorithm. This algorithm consists of

filtering the input signal to avoid noise, baseline wandering, etc. Subsequently, the first

derivative is calculated to emphasize the QRS complex. In order to increase the differences

between waves, the derivative is also squared. Finally, a threshold is applied, and according

to the position found, each beat is extracted from the signal. This algorithm is based on those

described in [1]. Next, each beat is normalized in amplitude. The segmentation process itself

normalizes the differences in time shift, and finally, a time scale normalization is needed to

calculate the dissimilarity among beats.

This time scale normalization is due to the changes in beat duration. In order to find groups

of similar beats within the register, a distance measure must be used. Since each beat can

have different time length, the space of discrete sequences doesn’t form an Euclidean space,

but a pseudo-metric space. Thus, prior to distance calculation, a temporal alignment is

necessary in order to get the same length in both sequences under analysis, and then,

calculate the distance. Two techniques of temporal alignment have been tested in this work:

linear and non-linear, based the last one on dynamic time warping (DTW). BIOSIGNAL 2002

In the first case, linear temporal alignment, a uniform downsampling or upsampling

process is applied to the beats in order to have the same length than the other one under

comparison. This is a very simple method, but the major withdraw is main features can be

misaligned, and so the distance measure does not represent the effective shape similarity.

In the second case, non-linear temporal alignment, an elastic matching algorithm, is used to

obtain a dissimilarity measure between two discrete sequences. In this case we will employ

the term dissimilarity since the value obtained does not comply with the rules to be

considered a distance.

Both techniques of temporal alignment are used with discrete sequences corresponding to

pairs of beats under comparison. Such discrete sequences can be obtained in different ways,

namely, depending on the feature extraction procedure utilized, the values can correspond to

amplitude, length, Fourier coefficients, etc.

In this work, four feature extraction techniques have been used: signal sampling, obtaining