A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification

Fusion of feature descriptors extracted from a signal through different methods is an important issue for the exploitation of representational power of each descriptor. In this research work, a novel system which exploits multi-stage features from a trained convolutional neural network (CNN) and precisely combines these features with a selection of handcrafted features is proposed. The set of handcrafted features consists of three subsets namely, wavelet transform based morphological features representing localized signal behaviour, statistical features exhibiting overall variational characteristics of the signal and temporal features representing the signal's behaviour on the time axis. The proposed system utilizes a novel decision-level fusion of features for ECG classification by three different approaches; the first one uses normalized feature-level fusion of handcrafted global statistical and local temporal features by uniting these features into one set, the second one uses the morphological feature subset, and the third one combines features extracted from multiple layers of a CNN through using a score-level based refinement procedure. The main impact of the proposed approach is the score-level based fusion of automatically learned features extracted from multiple layers of trained CNN and the decision-level fusion of features characterising the signal in totally different representational spaces. The individual decisions of the three different classifiers are fused together based on the majority voting and a unified decision is reached for the input ECG signal classification. The results over the MIT-BIH arrhythmia benchmarks database exhibited that the proposed system achieves a superior classification accuracy compared to all of the state-of-the-art ECG classification methods.

ECG classification using three-level fusion of different feature descriptors

Clean water is an indispensable essential resource on which humans and other living beings depend. Therefore, the establishment of a water quality prediction model to predict future water quality conditions has a significant social and economic value. In this study, a model based on an artificial neural network (ANN), discrete wavelet transform (DWT), and long short-term memory (LSTM) was constructed to predict the water quality of the Jinjiang River. Firstly, a multi-layer perceptron neural network was used to process the missing values based on the time series in the water quality dataset used in this research. Secondly, the Daubechies 5 (Db5) wavelet was used to divide the water quality data into low-frequency signals and high-frequency signals. Then, the signals were used as the input of LSTM, and LSTM was used for training, testing, and prediction. Finally, the prediction results were compared with the nonlinear auto regression (NAR) neural network model, the ANN-LSTM model, the ARIMA model, multi-layer perceptron neural networks, the LSTM model, and the CNN-LSTM model. The outcome indicated that the ANN-WT-LSTM model proposed in this study performed better than previous models in many evaluation indices. Therefore, the research methods of this study can provide technical support and practical reference for water quality monitoring and the management of the Jinjiang River and other basins.

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A Hybrid Model for Water Quality Prediction Based on an Artificial Neural Network, Wavelet Transform, and Long Short-Term Memory

Electrocardiogram (ECG) beat classification is a significant application in computer-aided analysis and diagnosis technologies. This paper proposed a method to detect, extract informative features, and classify ECG beats utilizing real ECG signals available in the standard MIT-BIH Arrhythmia database, with 10,502 beats had been extracted from it. The present study classifies the ECG beat into six classes, normal beat (N), Left bundle branch block beat, Right bundle branch block beat, Premature ventricular contraction, atrial premature beat, and aberrated atrial premature, using Gaussian mixture and wavelets features, and by applying principal component analysis for feature set reduction. The classification process is implemented utilizing two classifier techniques, the probabilistic neural network (PNN) algorithm and Random Forest (RF) algorithm. The achieved accuracy is 99.99%, and 99.97% for PNN and RF respectively. The precision is 99.99%, and 99.98% for PNN and RF respectively. The sensitivity is 99.99%, and 99.81% for PNN and RF respectively, while the specificity is 99.97%, 99.96% for PNN and RF respectively. It has been shown that the combination of Gaussian mixtures coefficients and the wavelets features have provided a valuable information about the heart performance and can be used significantly in arrhythmia classification.

Developing of robust and high accurate ECG beat classification by combining Gaussian mixtures and wavelets features

Human identification using a new matching Pursuit-based feature set of ECG.

Fusion of ECG and ABP signals based on wavelet transform for cardiac arrhythmias classification.

Early and correct diagnosis of cardiac arrhythmias is an important step in the treatment of patients. In the recent decades, a wide area of bio-signal processing is allocated to cardiac arrhythmia classification. Unlike other studies, which have employed Electrocardiogram (ECG) signal as a main signal to classify the arrhythmia and sometimes they have used other vital signals as an auxiliary signal to fill missing data and robust detections. In this study, the Arterial Blood Pressure (ABP) is used to classify six types of heart arrhythmias. In other words, in this study for first time, the arrhythmias are classified according ABP signal information. Discrete Wavelet Transform (DWT) is used to de-noise and decompose ABP signal. On feature extraction stage, three types of features including frequency, power, and entropy are extracted. In classification stage, Least Square Support Vector Machine (LS-SVM) is employed as a classifier. The accuracy, sensitivity, and specificity rates of 95.75%, 96.77%, and 96.32% are achieved, respectively. Currently, the classification of cardiac arrhythmias is based on the ABP signal which has some advantages. The recording of ABP signal is done by means of one electrode and therefore it has resulted in lower costs compared with the ECG signal. Finally, it has been shown that ABP has very important and valuable information about the heart performance and can be used in arrhythmia classification.

Roghayyeh Arvanaghi

Papers

Fusion of ECG and ABP signals based on wavelet transform for cardiac arrhythmias classification.

Classification of cardiac arrhythmias using arterial blood pressure based on discrete wavelet transform

Classification cardiac beats using arterial blood pressure signal based on discrete wavelet transform and deep convolutional neural network

3D tooth segmentation in cone-beam computed tomography images using distance transform