Electrocardiogram stream level correlated patterns as features to classify heartbeats for arrhythmia prediction

PurposeDigital computing and machine learning-driven predictive analysis in the diagnosis of non-communicable diseases are gaining significance. Globally many research studies are focusing on developing comprehensive models for such detection. Categorically in the proposed diagnosis for arrhythmia, which is a critical diagnosis to prevent cardiac-related deaths, any constructive models can be a value proposition. In this study, the focus is on developing a holistic system that predicts the scope of arrhythmia from the given electrocardiogram report. The proposed method is using the sequential patterns of the electrocardiogram elements as features.Design/methodology/approachConsidering the decision accuracy of the contemporary classification methods, which is not adequate to use in clinical practices, this manuscript coined a new dimension of features to perform supervised learning and classification using the AdaBoost classifier. The proposed method has titled “Electrocardiogram stream level correlated patterns as features (ESCPFs),” which takes electrocardiograms (ECGs) signal streams as input records to perform supervised learning-based classification to detect the arrhythmia scope in given ECG record.FindingsFrom the results and comparative reports generated for the study, it is evident that the model is performing with higher accuracy compared to some of the earlier models. However, focusing on the emerging solutions and technologies, if the accuracy factors for the model can be improved, it can lead to compelling predictions and accurate outcome from the process.Originality/valueThe authors represent complete automatic and rapid arrhythmia as classifier, which could be applied online and examine long ECG records sequence efficiently. By releasing the needs for extraction of features, the authors project an application based on raw signals, one result to heart rates date, whose objective is to lessen computation time when attaining minimum classification error outcomes.

Dynamics of Visual Supervised Learning: A Statistical-Physics Approach

We analysed human supervised learning and classification performance for compound Gabor gray-level patterns. We found that internal visual representations for supervised learning and classification may not be constructed in a smooth process of gradual development (Jüttner and Rentschler, 1996 Vision Research in press). Rather, it seemed that certain learning states (‘stereotypes’) recur that may be considered as ‘perceptual hypotheses’. Such effects have a transient character and cannot, therefore, be studied on the basis of cumulative learning data, which allow smoothing at the expense of temporal resolution. Thus, we analyse classification behaviour in terms of the evolution of a thermodynamic system, that is a system characterised by Gibbs statistics. Here it is assumed that a classification error occurs when a noise-influenced decision process passes an ‘energy gap’ related to the distance of signals in feature space. This approach has been extended to a wide range of distance-based models, originated by different fields, such as classical psychometrics, signal detection theory, technical pattern recognition, and connectionism. We made use of the finding that all these models can be related to a uniform mathematical structure (Unzicker et al, 1995 Perception24 Supplement, 95). The subjects' performance can then be described as a cooling process that reveals adaptive feature extraction during learning.