Sandeep Raj

ECG Signal Analysis Using DCT-Based DOST and PSO Optimized SVM

Signal processing techniques are an obvious choice for real-time analysis of electrocardiography (ECG) signals. However, classical signal processing techniques are unable to deal with the nonstationary nature of the ECG signal. In this context, this paper presents a new approach, i.e., discrete orthogonal stockwell transform using discrete cosine transform for efficient representation of the ECG signal in time–frequency space. These time–frequency features are further reduced in lower dimensional space using principal component analysis, representing the morphological characteristics of the ECG signal. In addition, the dynamic features (i.e., RR-interval information) are computed and concatenated to the morphological features to constitute the final feature set, which is utilized to classify the ECG signals using support vector machine (SVM). In order to improve the classification performance, particle swarm optimization technique is employed for gradually tuning the learning parameters of the SVM classifier. In this paper, ECG data exhibiting 16 classes of the most frequently occurring arrhythmic events are taken from the benchmark MIT-BIH arrhythmia database for the validation of the proposed methodology. The experimental results yielded an improved overall accuracy, sensitivity (Sp), and positive predictivity (Pp) of 98.82% in comparison with the existing approaches available in the literature.

ARM-based arrhythmia beat monitoring system

This paper aims for accurate diagnosis of arrhythmia beats in real time to enhance the health care service for cardiovascular diseases. The proposed methodology for the diagnosis involves the integration of the R-peak detection algorithm, FFT (fast fourier transform) based discrete wavelet transform for feature extraction and feedforward based Neural Network Architecture to classify generic cardiac beat classes into eight categories namely Right Bundled Block, Left Bundled Block, Preventricular Contraction (PVC), Atrial Premature Contraction (APC), Ventricular Flutter wave (VF), Paced Beat, Ventricular Escape (VE) and Normal beat. The paper contributes the development, prototyping and analysis of proposed methodology on ARM (Advanced RISC Machine) based SoC (System-on-Chip) in laboratory setup. This system is validated by generating real-time ECG signals using MIT-BIH database while the output of the system is monitored on the displaying device. The performance analysis of the proposed methodology implemented on the microcontroller based system is computed by performing the experiment which achieves a high overall accuracy of 97.4% with average sensitivity ( S e ) of 97.57%, specificity ( S p ) of 99.59% and positive predictivity ( P p ) of 97.93%. The system provides an assistive diagnostic solution to the users to lead a healthy lifestyle. Moreover, the ARM-based system can be fabricated into a handheld device for reliable automatic monitoring of the condition of heart by patients.

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. Hence, this study aims to improve the classification accuracy rate and provide an automated diagnostic solution for the detection of cardiac arrhythmias. METHODS The proposed methodology consists of four stages, i.e. filtering, R-peak detection, feature extraction and classification stages. In this study, Wavelet based approach is used to filter the raw ECG signal, whereas Pan-Tompkins algorithm is used for detecting the R-peak inside the ECG signal. In the feature extraction stage, discrete orthogonal Stockwell transform (DOST) approach is presented for an efficient time-frequency representation (i.e. morphological descriptors) of a time domain signal and retains the absolute phase information to distinguish the various non-stationary behavior ECG signals. Moreover, these morphological descriptors are further reduced in lower dimensional space by using principal component analysis and combined with the dynamic features (i.e based on RR-interval of the ECG signals) of the input signal. This combination of two different kinds of descriptors represents each feature set of an input signal that is utilized for classification into subsequent categories by employing PSO tuned support vector machines (SVM). RESULTS The proposed methodology is validated on the baseline MIT-BIH arrhythmia database and evaluated under two assessment schemes, yielding an improved overall accuracy of 99.18% for sixteen classes in the category-based and 89.10% for five classes (mapped according to AAMI standard) in the patient-based assessment scheme respectively to the state-of-art diagnosis. The results reported are further compared to the existing methodologies in literature. CONCLUSIONS The proposed feature representation of cardiac signals based on symmetrical features along with PSO based optimization technique for the SVM classifier reported an improved classification accuracy in both the assessment schemes evaluated on the benchmark MIT-BIH arrhythmia database and hence can be utilized for automated computer-aided diagnosis of cardiac arrhythmia beats.

Sparse representation of ECG signals for automated recognition of cardiac arrhythmias

Abstract As per the report of the World Health Organization (WHO), the mortalities due to cardiovascular diseases (CVDs) have increased to 50 million worldwide. Therefore, it is essential to have an efficient diagnosis of CVDs to enhance the healthcare in the clinical cardiovascular domain. The ECG signal analysis of a patient is a very popular tool to perform diagnosis of CVDs. However, due to the non-stationary nature of ECG signal and higher computational burden of the existing signal processing methods, the automated and efficient diagnosis remains a challenge. This paper presents a new feature extraction method using the sparse representation technique to efficiently represent the different ECG signals for efficient analysis. The sparse method decomposes an ECG signal into elementary waves using an overcomplete gabor dictionary. Four features such as time delay, frequency, width parameter, and square of expansion coefficient are extracted from each of the significant atoms of the dictionary. These features are concatenated and analyzed to determine the optimal length of discriminative feature vector representing each of the ECG signal. These extracted features representing the ECG signals are further classified using machine learning techniques such as least-square twin SVM, k-NN, PNN, and RBFNN. Further, the learning parameters of the classifiers are optimized using ABC and PSO techniques. The experiments are carried out for the proposed methods (i.e. feature extraction along with all classifiers) using benchmark MIT-BIH data and evaluated under category and personalized analysis schemes. Experimental results show that the proposed ECG signal representation using sparse decomposition technique with PSO optimized least-square twin SVM (best classifier model among k-NN, PNN and RBFNN) reported higher classification accuracy of 99.11% in category and 89.93% in personalized schemes respectively than the existing methods to the state-of-art diagnosis.

A comparative study of multivariate approach with neural networks and support vector machines for arrhythmia classification

This paper presents a comparative study of multivariate approach i.e. principal component analysis (PCA) for ECG signal analysis with support vector machine (SVM) and back propagation neural network for classification. Here, the combination of different sets of feature extraction and classification algorithms are analyzed and compared with each other to yield the best performance in terms of accuracy and other performance metrics. The experiment is performed to classify six classes of ECG beats and evaluated using the MIT-BIH database. The results show that the kernel based PCA with support vector machine performs better with an average overall accuracy, sensitivity, specificity and positive predictivity of 98.96%, 98.90%, 99.79% and 98.98% respectively.

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. 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.

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. Apart from these studies, efficient diagnosis of ECG signals remains a challenge in the clinical cardiovascular domain due to its non-stationary nature. The classical signal processing methods are widely employed to analyze the ECG signals, but they exhibit certain limitations and hence, are insufficient to achieve higher accuracy. METHODS This study presents a novel technique for an efficient representation of electrocardiogram (ECG) signals using sparse decomposition using composite dictionary (CD). The dictionary consists of the stockwell, sine and cosine analytical functions. The technique decomposes an input ECG signal into stationary and non-stationary components or atoms. For each of these atoms, five features i.e., permutation entropy, energy, RR-interval, standard deviation and kurtosis are extracted to determine the feature sets representing the heartbeats that are classified into different categories using the multi-class least-square twin support vector machines. The artificial bee colony (ABC) technique is used to determine the optimal classifier parameters. The proposed method is evaluated under category and personalized schemes and its validation is performed on MIT-BIH data. RESULTS The experimental results reported a higher overall accuracy of 99.21% and 90.08% in category and personalized schemes respectively than the existing techniques reported in the literature. Further a sensitivity, positive predictivity and F-score of 99.21% each in the category based scheme and 90.08% each in the personalized schemes respectively. CONCLUSIONS The proposed methodology can be utilized in computerized decision support systems to monitor different classes of cardiac arrhythmias with higher accuracy for early detection and treatment of cardiovascular diseases.

Power quality analysis using modified S-transform on ARM processor

The extensive usage of non-linear loads and electronic devices has resulted in increased vulnerability to the power quality (PQ) disturbances in the power system. Hence, the analysis of PQ disturbances becomes crucial to maintain the reliability of the distributed generation (DG). This paper presents the analysis of various disturbances like voltage swell, voltage sag, notch, flicker, spike, harmonics, momentary interruption and oscillatory transients by using a signal processing technique i.e. modified Stockwell transform (MST). The technique is employed to provide sufficient time-frequency characteristics and retain the phase information of input to detect the different PQ disturbances. Moreover, the localization of Gaussian window is exploited by providing different scaling parameters which correspond to the linear phase of frequency and provides better resolution. The voltage signal is utilized for the detection of the disturbances at a point of common coupling. The time-frequency features are re-transformed into the time space (original signal) by using the inverse modified S-transform to visualize the different PQ disturbances in real-time. In this study, the methodology is implemented on commercially available ARM (Advanced RISC Machine) processor due to its features such low cost and low power consumption for real-time power quality analysis.