Add to ORKGIn this work, an efficient non-supervised algorithm for clustering of ECG signals is presented. The method is assessed over a set of records from MIT/BIH arrhythmia database with different types of heartbeats, including normal (N) heartbeats, as well as the arrhythmia heartbeats recommended by the AAMI, usually found in Holter recordings: ventricular extra systoles (VE), left and right branch bundles blocks (LBBB and RBBB) and atrial premature beats (APB). The results are assessed by means the sensitivity and specificity measures, taking advantage of the database labels. Also, unsupervised performance measures are used. Finally, the performance of the algorithm is in average 95%, improving results reported by previous works of the literatur...
Introduction. The most common method for diagnosing cardiovascular diseases is the method of ECG mon...
In this paper we present a personalized long-term electrocardiogram (ECG) classification framework, ...
In this study, six types of arrhythmia beats observed in ECG signals have been analysed by using clu...
Cardiac arrhythmia analysis on Holter recordings is an important issue in clinical settings, however...
Abstract — Processing of the long-term ECG Holter record-ings for accurate arrhythmia detection is a...
Holter electrocardiographic (ECG) signals are ambulatory long-term registers used to detect heart di...
This Paper describes a clustering approach to be used for incoming data under computional constraint...
An arrhythmia is a pathology that consists on altering the heartbeat. Although, the 12-lead electroc...
The proposed approach applies current unsupervised clustering approaches in a different dynamic mann...
This thesis deals with methods of cluster analysis and their applications to short-term recording of...
In this work, a nonsupervised algorithm for feature se-lection and a non-parametric density-based cl...
The proposed approach applies current unsupervised clustering approaches in a different dynamic mann...
The paper deals with application of cluster analysis to different ECG records in order to identify p...
A number of methods for temporal alignment, feature extraction, and clustering of electrocardiograph...
Holter signals correspond to long-term electrocardiograph (ECG) registers. Manual inspection of such...
Introduction. The most common method for diagnosing cardiovascular diseases is the method of ECG mon...
In this paper we present a personalized long-term electrocardiogram (ECG) classification framework, ...
In this study, six types of arrhythmia beats observed in ECG signals have been analysed by using clu...
Cardiac arrhythmia analysis on Holter recordings is an important issue in clinical settings, however...
Abstract — Processing of the long-term ECG Holter record-ings for accurate arrhythmia detection is a...
Holter electrocardiographic (ECG) signals are ambulatory long-term registers used to detect heart di...
This Paper describes a clustering approach to be used for incoming data under computional constraint...
An arrhythmia is a pathology that consists on altering the heartbeat. Although, the 12-lead electroc...
The proposed approach applies current unsupervised clustering approaches in a different dynamic mann...
This thesis deals with methods of cluster analysis and their applications to short-term recording of...
In this work, a nonsupervised algorithm for feature se-lection and a non-parametric density-based cl...
The proposed approach applies current unsupervised clustering approaches in a different dynamic mann...
The paper deals with application of cluster analysis to different ECG records in order to identify p...
A number of methods for temporal alignment, feature extraction, and clustering of electrocardiograph...
Holter signals correspond to long-term electrocardiograph (ECG) registers. Manual inspection of such...
Introduction. The most common method for diagnosing cardiovascular diseases is the method of ECG mon...
In this paper we present a personalized long-term electrocardiogram (ECG) classification framework, ...
In this study, six types of arrhythmia beats observed in ECG signals have been analysed by using clu...