Robust detection of premature ventricular contractions using sparse signal decomposition and temporal features

Detection and classification of ECG noises using decomposition on mixed codebook for quality analysis

In this Letter, a robust technique is presented to detect and classify different electrocardiogram (ECG) noises including baseline wander (BW), muscle artefact (MA), power line interference (PLI) and additive white Gaussian noise (AWGN) based on signal decomposition on mixed codebooks. These codebooks employ temporal and spectral-bound waveforms which provide sparse representation of ECG signals and can extract ECG local waves as well as ECG noises including BW, PLI, MA and AWGN simultaneously. Further, different statistical approaches and temporal features are applied on decomposed signals for detecting the presence of the above mentioned noises. The accuracy and robustness of the proposed technique are evaluated using a large set of noise-free and noisy ECG signals taken from the Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database, MIT-BIH polysmnographic database and Fantasia database. It is shown from the results that the proposed technique achieves an average detection accuracy of above 99% in detecting all kinds of ECG noises. Furthermore, average results show that the technique can achieve an average sensitivity of 98.55%, positive productivity of 98.6% and classification accuracy of 97.19% for ECG signals taken from all three databases.

Premature Ventricular Contraction Detection from Ambulatory ECG Using Recurrent Neural Networks

Premature ventricular contraction (PVC) is usually considered to as benign arrhythmia in the absence of structural heart diseases. However, frequent premature beats may significantly increase the risk of heart failure and even death by an arrhythmia-induced cardiomyopathy. Therefore, high PVC counts have been considered as an approach to predict the risk of severe arrhythmias. Progress of wearable devices provides a convenient tool for the detection of premature contraction in casual life. Considering the huge quantities of data recorded by wearable devices, reliable and low-cost data analysis programs should be developed for real time PVC detection. In this research, we use recurrent neural networks with, long short-term memory to detect PVC. Through validating with MIT-BIH arrhythmia database, the detection accuracy of this method is 96%-99%.

Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated Myocardial Ischemia Detection

An automatic method is presented for detecting myocardial ischemia, which can be considered as the early symptom of acute coronary events. Myocardial ischemia commonly manifests as ST- and T-wave changes on ECG signals. The methods in this study are proposed to detect abnormal ECG beats using knowledge-based features and classification methods. A novel classification method, sparse representation-based classification (SRC), is involved to improve the performance of the existing algorithms. A comparison was made between two classification methods, SRC and support-vector machine (SVM), using rule-based vectors as input feature space. The two methods are proposed with quantitative evaluation to validate their performances. The results of SRC method encompassed with rule-based features demonstrate higher sensitivity than that of SVM. However, the specificity and precision are a trade-off. Moreover, SRC method is less dependent on the selection of rule-based features and can achieve high performance using fewer features. The overall performances of the two methods proposed in this study are better than the previous methods.