Shivajirao M. Jadhav

Artificial Neural Network based cardiac arrhythmia classification using ECG signal data

In this paper we proposed a automated Artificial Neural Network (ANN) based classification system for cardiac arrhythmia using standard 12 lead ECG recordings. In this study, we are mainly interested in producing high confident arrhythmia classification results to be applicable in diagnostic decision support systems. In arrhythmia analysis, it is unavoidable that some attribute values of a person would be missing. Therefore we have replaced these missing attributes by closest column value of the concern class. Multilayer percepron (MLP) feedforward neural network model with static backpropagation algorithm is used to classify arrhythmia cases into normal and abnormal classes. Networks models are trained and tested for UCI ECG arrhythmia data set. This data set is a good environment to test classifiers as it is incomplete and ambiguous bio-signal data collected from total 452 patient cases. The classification performance is evaluated using six measures; sensitivity, specificity, classification accuracy, mean squared error (MSE), receiver operating characteristics (ROC) and area under curve (AUC). Our experimental results give 86.67% testing classification accuracy.

ECG arrhythmia classification using modular neural network model

This research is on presenting a new approach for cardiac arrhythmia disease classification. The proposed method uses Modular neural network (MNN) model to classify arrhythmia into normal and abnormal classes. We have performed experiments on UCI Arrhythmia data set. Missing attribute values of this data set are replaced by closest column value of the concern class. We have constructed neural network model by varying number of hidden layers from one to three and are trained by varying training percentage in data set partitions. In this study, we are mainly interested in producing high confident arrhythmia classification results to be applicable in diagnostic decision support systems. This data set is a good environment to test classifiers as it is incomplete and ambiguous bio-signal data collected from total 452 patient cases. The classification performance is evaluated using six measures; sensitivity, specificity, classification accuracy, mean squared error (MSE), receiver operating characteristics (ROC) and area under curve (AUC). The experimental results presented in this paper show that up to 82.22% testing classification accuracy can be obtained.

Artificial Neural Network Models based Cardiac Arrhythmia Disease Diagnosis from ECG Signal Data

Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately causes irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this paper we proposed an Artificial Neural Network (ANN) based cardiac arrhythmia disease diagnosis system using standard 12 lead ECG signal recordings data. In this study, we are mainly interested in classifying disease in normal and abnormal classes. We have used UCI ECG signal data to train and test three different ANN models. In arrhythmia analysis, it is unavoidable that some attribute values of a person would be missing. Therefore we have replaced these missing attributes by closest column value of the concern class. ANN models are trained by static backpropagation algorithm with momentum learning rule to diagnose cardiac arrhythmia. The classification performance is evaluated using measures such as mean squared error (MSE), classification specificity, sensitivity, accuracy, receiver operating characteristics (ROC) and area under curve (AUC). Out of three different ANN models Multilayer perceptron ANN model have given very attractive classification results in terms of classification accuracy and sensitivity of 86.67% and 93.75% respectively while Modular ANN have given 93.1% classification specificity. General Terms Machine Learning, Pattern Classification.

Modular neural network model based foetal state classification

Cardiotocography (CTG) is a simultaneous recording of foetal heart rate (FHR) and uterine contractions (UC) and it is one of the most common diagnostic techniques to evaluate maternal and foetal well-being during pregnancy and before delivery. Assessment of the foetal state can be verified only after delivery using the foetal (newborn) outcome data. One of the most important features defining the abnormal foetal outcome is low birth weight. This paper proposes a multi-class classification algorithm using Modular neural network (MNN) models. It tries to boost two conflicting main objectives of multi-class classifiers: a high correct classification rate level and a high classification rate for each class. Using a Cardiotocography database of normal, suspect and pathological cases, we trained MNN classifiers with 23 real valued diagnostic features collected from total 2126 foetal CTG signal recordings data from UCI Machine Learning Repository. We used the classification in a detection process. The proposed methodology is presented, which then is tested on UCI Cardiotocography unseen testing data sets. Experimental results are promising paving the way for further research in that direction.

Artificial Neural Network Based Cardiac Arrhythmia Disease Diagnosis

Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this paper we proposed an Artificial Neural Network (ANN) based cardiac arrhythmia disease diagnosis system using standard 12 lead ECG signal recordings data. In this study, we are mainly interested in classifying disease in normal and abnormal classes. We have used UCI ECG signal data to train and test three different ANN models. In arrhythmia analysis, it is unavoidable that some attribute values of a person would be missing. Therefore we have replaced these missing attributes by closest column value of the concern class. ANN models are trained by static backpropagation algorithm with momentum learning rule to diagnose cardiac arrhythmia. The classification performance is evaluated using measures such as mean squared error (MSE), classification specificity, sensitivity, accuracy, receiver operating characteristics (ROC) and area under curve (AUC). Out of three different ANN models Multilayer perceptron ANN model have given very attractive classification results in terms of classification accuracy and sensitivity of 86.67% and 93.75% respectively while Modular ANN have given 93.1% classification specificity.

Arrhythmia disease classification using Artificial Neural Network model

In this paper we proposed an automated Artificial Neural Network (ANN) based classification system for cardiac arrhythmia disease using standard 12 lead ECG signal recordings. In this study, we are mainly interested in classifying different arrhythmia types (classes) using multilayer peceptron (MLP) model. We have used UCI ECG signal data to train and test MLP network model. For this multi class classification we used one arrhythmia class against normal arrhythmia class. Different arrhythmia types include coronary artery disease, old anterior myocardial infarction, old inferior myocardial infarction, sinus tachycardia, sinus bradycardia, right bundle branch block etc. In arrhythmia analysis, it is unavoidable that some attribute values of a person would be missing. Therefore we have replaced these missing attributes by closest column value of the concern class. MLP feedforward neural network model is trained by static backpropagation algorithm with momentum learning rule to classify cardiac arrhythmia classes. The classification performance is evaluated using measures such as classification accuracy, training, testing and cross validation mean squared error (MSE), percentage correct, receiver operating characteristics (ROC) and area under curve (AUC). From careful and exhaustive experimentation, we reached to the conclusion that proposed classifier gives best classification results in terms of classification accuracy of 100 % for classes 1 and 2, 98.72%, 97.4%, 94.25%, 92.1% for classes 4, 5, 2 and 10 respectively.