Clustering of electrocardiograph signals in computer-aided Holter analysis

Cuesta–Frau, David; Pérez-Cortés, Juan-Carlos; Andreu-Garcı́a, Gabriela

doi:10.1016/s0169-2607(02)00145-1

Cited by 45 publications

(37 citation statements)

References 10 publications

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“…Thus, it is necessary to invoke dissimilarity measures based on nonuniform temporal alignment, to remove the heartbeats time shifts, and therefore to improve the performance of the proposed method. We use in this work (DTW), that finds an optimal alignment function between two sequences of different length [4].…”

Section: Clusteringmentioning

confidence: 99%

“…An additional first stage of preclustering is also used for removing redundant information, where a heartbeat is considered as such if its dissimilarity measure to any other in the final set is below a conservative threshold, which fixed a priori value should not remove significant events of heartbeats [4]. Thus, being P the set of l heartbeats of the register, the main goal is addressed to find a subset M∈P , with r heartbeats, where r ≪ l, in such a way that all P heartbeat types are represented into M, and only redundant ones are discarded.…”

Section: Clusteringmentioning

confidence: 99%

“…In order to assess the performance of the method, two error measures are defined [4]: -Clustering error: number or percentage of heartbeats in a cluster, but that do not correspond to the class of such cluster.…”

Section: Performancementioning

confidence: 99%

“…Factors such as signal length, noise, dynamic behavior of signal and variability in the waveform by patient's physiology and pathology have to be considered, as well [3]. In this sense, unsupervised computer-aided analysis of Holter registers is a suitable choice regarding off-line analysis because it does not require a previous knowledge of heartbeat classes [4]. However, there are still some open problems because of the heartbeat duration and morphology variability.…”

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confidence: 99%

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An improved method for unsupervised analysis of ECG beats based on WT features and J-means clustering

Rodriguez-Sotelo

Cuesta–Frau

Castellanos-Domínguez

2007

2007 Computers in Cardiology

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Clustering is advisable technique for analysis and interpretation of long-term ECG Holter records. As a nonsupervised method, several challenges are posed due to factors such as signal length (very long duration), noise presence, dynamic behavior and morphology variability (different patient physiology and/or pathology). This work describes an improved version of the k-means IntroductionLong-term analysis of the ECG signals is a sound technique to assess patient state and evolution in a number of diseases: cardiac arrhythmias, transient ischemic episodes and silent myocardial ischemia, which are not readily detected in a short-time electrocardiogram [1,2]. In this case, record analysis is performed off-line due to their long duration (hundreds of thousands of beats to examine), keeping in mind not to skip any beat, since the diagnosis might depend on just a few of them. Factors such as signal length, noise, dynamic behavior of signal and variability in the waveform by patient's physiology and pathology have to be considered, as well [3]. In this sense, unsupervised computer-aided analysis of Holter registers is a suitable choice regarding off-line analysis because it does not require a previous knowledge of heartbeat classes [4]. However, there are still some open problems because of the heartbeat duration and morphology variability.Recently, some solutions have been proposed. Namely, [5] studies the ECG heartbeat dynamics, which are represented by non-parametric feature extraction methods and compared by a dissimilarity measure, based on DTW.I n [6], ECG variability is considered regarding its morphology and duration, by means of a time-frequency representation of the signal using several WT families. Although, this work is aimed at classifying some cardiac pathologies, there is no clear way to extend the results to other common pathologies. Clustering techniques are among the most used non-supervised processing techniques, but selection of an specific clustering algorithm has to take into account several issues: computational cost, partition optimality, outlier detection, local or global convergence, and simplicity. It has also to be robust against highly unbalanced partitions, for instance, set of objects with a great difference in the number of instances of each class, etc.The main goal of present work is to improve the performance of a heartbeat clustering method for ECG registers, using WT coefficients as features. In this case, the nonstationarity nature of the WT allows to extract discriminant data from signal morphology [6]. The unsupervised analysis is performed by the heuristic search method Jmeans, which solves the minimization problem with a solution close to the global optimum [7]. A comparison is carried out between features, obtained by the WT coefficients. In order to remove time length differences, heartbeats are previously nonuniformly resampled by means of trace segmentation. Computational cost is relatively low, thus because a preprocessing stage removes obvious redundant heartbeats...

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Section: Clusteringmentioning

confidence: 99%

Section: Clusteringmentioning

confidence: 99%

Section: Performancementioning

confidence: 99%

mentioning

confidence: 99%

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An improved method for unsupervised analysis of ECG beats based on WT features and J-means clustering

Rodriguez-Sotelo

Cuesta–Frau

Castellanos-Domínguez

2007

2007 Computers in Cardiology

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“…Missing data are not unusual in biomedical signals. Time series are vulnerable to uncontrolled factors such as noise [30] or outliers, but other technical processes may cause missing points such as wireless or network data transmission [31,32], signal compression [33,34], non-uniform sampling, trace segmentation [35], or resampling.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Entropy Sensitivity to Missing Biosignal Data

Cirugeda-Roldán

Cuesta–Frau

Miró-Martínez

et al. 2014

Entropy

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Entropy estimation metrics have become a widely used method to identify subtle changes or hidden features in biomedical records. These methods have been more effective than conventional linear techniques in a number of signal classification applications, specially the healthy-pathological segmentation dichotomy. Nevertheless, a thorough characterization of these measures, namely, how to match metric and signal features, is still lacking. This paper studies a specific characterization problem: the influence of missing samples in biomedical records. The assessment is conducted using four of the most popular entropy metrics: Approximate Entropy, Sample Entropy, Fuzzy Entropy, and Detrended Fluctuation Analysis. The rationale of this study is that missing samples are a signal disturbance that can arise in many cases: signal compression, non-uniform sampling, or data transmission stages. It is of great interest to determine if these real situations can impair the capability of segmenting signal classes using such metrics. The experiments employed several biosignals: electroencephalograms, gait records, and RR time series. Samples of these signals were systematically removed, and the entropy computed for each case. The results showed that these metrics are robust against missing samples: With a data loss percentage of 50% or even higher, the methods were still able to distinguish among signal classes.

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