The proposed DOST and ABC optimized LSTSVM algorithm achieved an overall classification accuracy of 96.29% in the class-oriented scheme and 96.08% in the personalized scheme for detecting cardiac arrhythmias.
Does a personalized arrhythmia monitoring platform using DOST and ABC-optimized LSTSVM improve the accuracy of real-time arrhythmia detection from ECG signals?
A novel personalized arrhythmia monitoring platform using DOST and ABC-optimized LSTSVM achieved high accuracy (>96%) in real-time ECG classification on an embedded system.
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. This study aims to develop a personalized arrhythmia monitoring platform allowing real-time detection of arrhythmias from the subject's electrocardiogram (ECG) signal for point-of-care usage. A novel method, i.e. discrete orthogonal stockwell transform (DOST) technique for feature extraction is employed to capture the significant time-frequency coefficients to constitute the feature set representing each of the ECG signals. These coefficients or features are classified using artificial bee colony (ABC) optimized twin least-square support vector machine (LSTSVM) for classifying the different categories of ECG signals. The ABC optimizes the dimension of the feature set and the learning parameters of the classifier. The proposed method is prototyped on the commercially available ARM-based embedded platform and validated on the benchmark MIT-BIH arrhythmia database. Further, the prototype is evaluated under two schemes, i.e. class and personalized schemes which reported a higher overall accuracy of 96.29% and 96.08% in the respective schemes than the existing works to the state-of-art CVDs diagnosis.
Raj et al. (Tue,) conducted a other in Cardiac arrhythmias (n=48). Discrete orthogonal stockwell transform (DOST) and artificial bee colony (ABC) optimized least-square twin support vector machine (LSTSVM) vs. Existing classification methods was evaluated on Overall classification accuracy (class-oriented scheme). The proposed DOST and ABC optimized LSTSVM algorithm achieved an overall classification accuracy of 96.29% in the class-oriented scheme and 96.08% in the personalized scheme for detecting cardiac arrhythmias.