Trigger Learning and ECG Parameter Customization for Remote Cardiac Clinical Care Information System

Abstract

Coronary heart disease is being identified as the largest single cause of death along the world. The aim of a cardiac clinical information system is to achieve the best possible diagnosis of cardiac arrhythmias by electronic data processing. Cardiac information system that is designed to offer remote monitoring of patient who needed continues follow up is demanding. However, intra- and interpatient electrocardiogram (ECG) morphological descriptors are varying through the time as well as the computational limits pose significant challenges for practical implementations. The former requires that the classification model be adjusted continuously, and the latter requires a reduction in the number and types of ECG features, and thus, the computational burden, necessary to classify different arrhythmias. We propose the use of adaptive learning to automatically train the classifier on up-to-date ECG data, and employ adaptive feature selection to define unique feature subsets pertinent to different types of arrhythmia. Experimental results show that this hybrid technique outperforms conventional approaches and is, therefore, a promising new intelligent diagnostic tool.

A New Ensemble Method with Feature Space Partitioning for High-Dimensional Data Classification

“…A number of pairs of ( , ) values were attempted, and the pair with the best 1 0 1 1 1 1 3 1 0 1 0 1 1 1 1 4 1 0 1 0 1 1 1 1 5 1 0 1 0 1 1 1 1 6 1 0 1 0 1 1 1 1 7 1 0 1 0 1 1 1 1 8 1 0 1 0 1 1 1 1 9 1 0 1 0 1 1 1 1 10 10 1 0 1 1 1 1 Average 1 0 1 1 1 1 Tables 2 through 7 show the performance of our proposed method in terms of TP rate, FP rate, precision, recall,measure, and ROC area, respectively. TP rate and FP rate refer to the proportion of actual positive instances correctly predicted as positive and the proportion of actual negative instances wrongly predicted as positive [34][35][36], respectively. Precision is computed as the number of true positive instances divided by the total number of instances labelled as belonging to the positive class.…”

Real-Time Automated Cardiac Health Monitoring by Combination of Active Learning and Adaptive Feature Selection

“…The classifier model maps the input features to required output classes on the basis of features specified during training. Several data mining techniques are used for this purpose, with one of the most famous being the decision-tree technique [38,39]. Several efforts have been made to apply artificial neural networks (ANNs) as well.…”

“…The classifier performance is then evaluated (check) and updated (improve) for consistency. Finally, low-quality data is removed to avoid poor results [39]. The double impact is used to substitute the partial or complete modification of the current active training data set.…”

Pro-Detection of Atrial Fibrillation Using Mixture of Experts

“…There are some methods developed to quantify and detect AF regarding features related to one of the three main ECG parameters: the absence of P wave, the atrial activity in fluctuating waveforms, and the abnormality of RR intervals [8]. Such algorithms should accurately be able to detect episodes of AF and at the same time have a low computational complexity in order to analyze the ECG signals in real time.…”