Open Heart

"Open Heart" is a research project for the automatic diagnosis of Heart diseases through ECG. It is being carried out at Pakistan Institute of Engineering and Applied Sciences (PIEAS) , Islamabad, Pakistan.
The major incentive behind the development of this project is cardiac disease being one of the leading causes of death all over the world. With the inception of fast signal processing and computing hardware, techniques for the automatic detection of cardiac disorders through ECG has stemmed up as one of the most promising methodologies in Clinical Decision Support Systems. Such a system can offer rapid, accurate and reliable diagnosis to a variety of cardiac diseases and can reduce the work load for cardiac experts along with providing a facility for the simultaneous monitoring of multiple patients. In this project the objective is to develop techniques for the automatic processing and analysis of the ECG. The work is divided into three major parts: Part-I involving study and implementation of methods for removal of artifacts from the ECG. These include baseline and noise removal techniques. In this work different baseline removal techniques, such as use of digital FIR and IIR filters and 3 different polynomial fitting approaches have been compared to conclude that the use of a two stage first order polynomial fitting based method introduces least distortion in the ECG while effectively compensating the ECG baseline. For Noise removal, a comparison of three different techniques, i.e. Use of Digital filters, Independent Component Analysis (ICA) and Local Nonlinear Projective Filtering has been carried out leading to the conclusion that nonlinear projective filtering performs well in removing noise from the ECG, whereas the potential of ICA for this purpose has been explored.
Part-II involves the segmentation of different ECG components, i.e. P, QRS and T-waves using methods based on digital filters, Continuous Wavelet Transform (CWT) and the Discrete Wavelet Transform. A new method for QRS detection and delineation through CWT has been developed which compares well with existing research offering Sensitivity/Specificity of ~99.8% for detection of QRS with ~10ms error in determining its onset and offset on the QT Database available at Physionet. The accuracy of an existing DWT based method has been improved through the use of Genetic Algorithms (GA). We conclude that the use of DWT with parameter optimization through GA proves to be the most effective technique for ECG Segmentation giving equally good accuracy in terms of detection and delineation.
Part-III is concerned with the classification of different types of heart rhythms (Normal, Atrial Premature Beats, Ventricular Premature Beats, Paced Rhythms, Left and Right Bundle Branch Blocks) and the detection of ST Segment deviations connected to Ischemic Heart Disease. For the purpose of classification of different arrhythmias DWT based features have been compared with those obtained from the Discrete Fourier Transform (DFT) to conclude that DWT is more effective in the classification of different types of heart rhythms. An accuracy of 99.1% has been achieved through implementation of a DWT based technique for feature extraction and using k-Nearest Neighbor classifiers. These results have been compared with those obtained through the use of Probabilistic Neural Networks (PNN) and Learning Vector Quantization (LVQ) Neural Networks. A comparison of the performance of different types of feature extraction and classification techniques for the detection of ischemic ST deviation episodes, such as time-domain features with a rule based classifier, use of Principal Component Analysis (PCA) based features with a Backpropagation Neural Network, a Neural Network Ensemble and a Support Vector Machine (SVM) ensemble classifier, has been established. A Sensitivity/Positive Predictivity of ~90% has been achieved with the use of a novel Neural Network Ensemble which uses lead specific principal components as features. These results are highest in terms of accuracy when compared with the existing literature with the novelty lying in the use of lead specific KLT Bases and Ensemble Neural Classifiers for each lead.
The work reported in this thesis can be used to establish the foundations of a practical stand-alone system for patient monitoring and the design of a multiple patient monitoring system as required in hospitals. Currently the project focus is on the development of hardware for real-time system application.
The project team welcomes any collaborative research offers.

List of Publications:

Afsar, F.A. and M. Arif, QRS Detection and Delineation Techniques for ECG Based Robust Clinical Decision Support System Design, in National Science Conference. 2007: Lahore, Pakistan.

Afsar, F.A. and M. Arif, Detection of ST Segment Deviation Episodes in the ECG using KLT with an Ensemble Neural Classifier, in International Conference on Emerging Technologies (ICET 2007). 2007: Islamabad, Pakistan.

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--Fayyaz.A.Afsar 12:46, 3 November 2007 (UTC)

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