Automated recognition of cardiac arrhythmias using sparse decomposition over composite dictionary.

Background And Objective: Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. Due to an increase in the rate of global mortalities, biopathological signal processing and evaluation are widely used in the ambulatory situations for healthcare applications. For decades, the processing of pathological electrocardiogram (ECG) signals for arrhythmia detection has been thoroughly studied for diagnosis of various cardiovascular diseases.

Development of robust, fast and efficient QRS complex detector: a methodological review.

The basis and reliability for timely diagnosis of cardiovascular diseases depend on the robust and accurate detection of QRS complexes along with the fiducial points in the electrocardiogram (ECG) signal. Despite, the several QRS detection algorithms reported in the literature, the development of an efficient QRS detector remains a challenge in the clinical environment. Therefore, this article summarizes the performance analysis of various QRS detection techniques depending upon three assessment factors which include robustness to noise, computational load, and sensitivity validated on the benchmark MIT-BIH arrhythmia database.

A Personalized Arrhythmia Monitoring Platform.

Arrhythmia detection is the core of cardiovascular disease diagnosis. Though, there is no such generic solution for detecting the arrhythmias at the moment they occur which is due to the non-stationary nature and inter-patient variations of ECG signals. The feature extraction and classification techniques are significant tools widely used in the automated classification of arrhythmias.

Cardiac arrhythmia beat classification using DOST and PSO tuned SVM.

Background And Objective: The increase in the number of deaths due to cardiovascular diseases (CVDs) has gained significant attention from the study of electrocardiogram (ECG) signals. These ECG signals are studied by the experienced cardiologist for accurate and proper diagnosis, but it becomes difficult and time-consuming for long-term recordings. Various signal processing techniques are studied to analyze the ECG signal, but they bear limitations due to the non-stationary behavior of ECG signals.