MULTISAB: A Web Platform for Analysis of Multivariate Heterogeneous Biomedical Time-Series

Jović, Alan; Kukolja, Davor; Friganović, Krešimir; Jozic, Kresimir; Cifrek, Mario

doi:10.1007/978-981-10-9035-6_76

Cited by 2 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their results indicated that the method has high recognition accuracy(up to 97.77%) in classifying noisy and skewed heartbeats. Jovic, Kukolja [86] presented a robust expert system platform for diagnosing arrhythmia from multivariate heterogenous medical time series consisting of ECG, EEG and HRV using SVM, MLP and NEAT. The approach tested on the dataset from MIT-BIH database yielded the highest accuracy of up to 77% on NEAT.…”

Section: Supervised Learning Methodsmentioning

confidence: 99%

Expert System and Decision Support System for Electrocardiogram Interpretation and Diagnosis: Review, Challenges and Research Directions

et al. 2022

View full text Add to dashboard Cite

Electrocardiography (ECG) is one of the most widely used recordings in clinical medicine. ECG deals with the recording of electrical activity that is generated by the heart through the surface of the body. The electrical activity generated by the heart is measured using electrodes that are attached to the body surface. The use of ECG in the diagnosis and management of cardiovascular disease (CVD) has been in existence for over a decade, and research in this domain has recently attracted large attention. Along this line, an expert system (ES) and decision support system (DSS) have been developed for ECG interpretation and diagnosis. However, despite the availability of a lot of literature, access to recent and more comprehensive review papers on this subject is still a challenge. This paper presents a comprehensive review of the application of ES and DSS for ECG interpretation and diagnosis. Researchers have proposed a number of features and methods for ES and DSS development that can be used to monitor a patient’s health condition through ECG recordings. In this paper, a taxonomy of the features and methods for ECG interpretation and diagnosis were presented. The significance of the features and methods, as well as their limitations, were analyzed. This review further presents interesting theoretical concepts in this domain, as well as identifies challenges and open research issues on ES and DSS development for ECG interpretation and diagnosis that require substantial research effort. In conclusion, this paper identifies important future research areas with the purpose of advancing the development of ES and DSS for ECG interpretation and diagnosis.

show abstract

Section: Supervised Learning Methodsmentioning

confidence: 99%