Add to ORKGIn this paper, the performance analysis of a clustering algorithm applied to group electrocardiograph beats is presented. This clustering algorithm will be based on different distance measures between pairs of beats. The aim of this study is to reduce the amount of data presented to doctors without loss of information. Distance measures used are linear and non-linear. Variations of each kind are tested. In order to quantify the performance of the methods proposed, each one is applied to real electrocardiograph signals. The results are compared using a misclassification ratio
The proposed approach applies current unsupervised clustering approaches in a different dynamic mann...
Background: Despite advancements in digital health, it remains challenging to obtain precise time sy...
This Paper describes a clustering approach to be used for incoming data under computional constraint...
In this paper, a method to automatically extract the main information from a long-term electrocardio...
A number of methods for temporal alignment, feature extraction, and clustering of electrocardiograph...
Introduction. The most common method for diagnosing cardiovascular diseases is the method of ECG mon...
Signal processing can be used to condition medical signals to facilitate their interpretation, and t...
Holter electrocardiographic (ECG) signals are ambulatory long-term registers used to detect heart di...
A reliable diagnosis of cardiac diseases can sometimes only be obtained by observing the heart of a ...
This thesis deals with methods of cluster analysis and their applications to short-term recording of...
This work deals with the classification of cardiac cycles, which uses a method of dynamic time warpi...
Abstract-This paper presents a multi-stage algorithm for multi-channel ECG beat classification into ...
The study is focused on a design of a reliable approach for ECG cycles clustering. It would be helpf...
In this work, an efficient non-supervised algorithm for clustering of ECG signals is presented. The ...
The proposed approach applies current unsupervised clustering approaches in a different dynamic mann...
The proposed approach applies current unsupervised clustering approaches in a different dynamic mann...
Background: Despite advancements in digital health, it remains challenging to obtain precise time sy...
This Paper describes a clustering approach to be used for incoming data under computional constraint...
In this paper, a method to automatically extract the main information from a long-term electrocardio...
A number of methods for temporal alignment, feature extraction, and clustering of electrocardiograph...
Introduction. The most common method for diagnosing cardiovascular diseases is the method of ECG mon...
Signal processing can be used to condition medical signals to facilitate their interpretation, and t...
Holter electrocardiographic (ECG) signals are ambulatory long-term registers used to detect heart di...
A reliable diagnosis of cardiac diseases can sometimes only be obtained by observing the heart of a ...
This thesis deals with methods of cluster analysis and their applications to short-term recording of...
This work deals with the classification of cardiac cycles, which uses a method of dynamic time warpi...
Abstract-This paper presents a multi-stage algorithm for multi-channel ECG beat classification into ...
The study is focused on a design of a reliable approach for ECG cycles clustering. It would be helpf...
In this work, an efficient non-supervised algorithm for clustering of ECG signals is presented. The ...
The proposed approach applies current unsupervised clustering approaches in a different dynamic mann...
The proposed approach applies current unsupervised clustering approaches in a different dynamic mann...
Background: Despite advancements in digital health, it remains challenging to obtain precise time sy...
This Paper describes a clustering approach to be used for incoming data under computional constraint...