A Neural Network Approach to Phonocardiography Ian Cathers Master of Biomedical Engineering The University of New South Wales 1991 Declaration I hereby declare that this submission is my own work and that, to the best of my knowledge and belief, it contains no material previously published or written by another person nor material which to a substantial extent has been accepted for the award of any other degree or diploma of a university or other institute of higher learning, except where due acknowledgement is made in the text. Signed: Dated: 1 Acknowledgements I wish to express my thanks to the following people for their assistance in a variety of ways:- Dr Albert Avolio (Centre for Biomedical Engineering, University of New South Wales) for his supervision of the project and his ability to encourage in the broader educational and professional objectives that such a task can catalyse. Mr John Telec and Mr Robert Mannell (School of Linguistics, Macquarie University) for their time spent in helping with the learning curve associated with Kay Sonograph computers. Dr Phillip Harris and Mr David Hardy (Department of Cardiology, Royal Prince Alfred Hospital, Sydney), for their time spent in resurrecting a moth-balled M-mode echocardiograph for phonocardiographic use. · Dr Michael Feneley, Sue and Meli (Cardiology Department, St Vincent's Hospital, Sydney) for their willingness to adjust schedules to allow access to equipment and subjects. Dr Walter Ivanstoff (Macquarie University) for his English translation of a Russian paper. Anne, the only person to have had an overview of the whole project, and who was encouraging in every aspect. Miriam and Timothy, who put up with weeks of RF! in the AM broadcast band while sluggish neural networks struggled with seemingly simple tasks. 2 Abstract This project is a pilot study of the feasibility of automated heart sound recognition using neural network computing techniques. In the past human heart sounds have proven to be an important diagnostic tool, for valvular disease in particular. More recently, their importance has been eclipsed by direct visualisation techniques. Despite these current emphases in diagnostic methodologies, a cheap tool for the automated recognition and classification of heart sounds may prove a useful primary screening device, considering the high capital investment required for direct visualisation hardware. Heart sounds from a variety of cardiovascular pathologies were digitised, pre­ processed, and characterised before being used as input data for software-implemented multi-layer neural networks. The neural networks were trained by backpropagation under a variety of input data, network topology and learning rate parameters. Trained networks were also used to classify heart sounds not previously encountered in the training data, in order to quantify their ability to generalise. The rate of the networks' approach to correct classification of the training data was found to be highly dependent on the gain term, and less strongly dependent on the size of the momentum term. On the other hand, the likelihood of reaching the correct classification of the training data was dependent on the nature of the input data. Of particular significance was the implementation of a small scale normaVabnormal heart sound classifier, which showed excellent accuracy in classifying a small range of untested heart sounds. While the task of training networks with even the limited output requirements studied here proved to be highly computationally expensive, the classifications from an implemented network were fast and accurate. A viable diagnostic tool would require a far wider range of input data, and significant computing facilities for training. It may prove to be more effective as a front-end pre-processor for a rules-based Artificial Intelligence system, considering the complexities and limitations of differential cardiac diagnosis from heart sounds alone. 3 Preface This project report is structured in the following way:- Chapter 1 Neural Networks - A Review. Introduction to neural networks and a technical review of the literature. Chapter 2 Heart Sounds and Phonocardiography - A Review. General background and review of auscultation and phonocardiography. Chapter 3 The Heart Sound Signal - Pre-Processing and Characterisation. Materials, methods and computational strategies followed in obtaining and pre-processing of heart sounds which were used as input data for neural networks. Chapter 4 Network Training. Investigations into some of the parameters affecting the training of neural networks in heart sound recognition. Chapter 5 Heart Sound Recognition. Investigations into some heart sound recognition tasks using neural networks. Chapter 6 Conclusions. Conclusions from this study and future directions of work in this area. To provide a more intuitive structure to Chapters 4 and 5, materials and methods of general applicability are grouped together, such as in Chapter 3 and Sections 4.2. Experimental results are organised by topic, rather than Materials and Methods, Results, Discussion and Conclusions. Methodology specific to the particular investigation is discussed together with results under these topic headings. 4 Contents 1. Neural Networks - A Review ................................................................... 9 1.1. Introduction ................................................................................... 9 1 .1 .1 . Classifiers ........................................................................ 1O 1 .1 .1 .1 . Neuron Element ............................................ 11 1.1.1.2. Training Procedure ....................................... 12 1 .1 .1 .3. Classification Process for New Data ............. 12 1.2. Neural Network Taxa .................................................................... 13 1 .2.1 . Nodal Types ..................................................................... 14 1.2.1 .1 . Method of combination of weights and inputs ................. 14 1.2.1.2. Linearity ............................................................... 15 1.2.1.3.Number Types ..................................................... 15 1.2.1 .4. Determinism ........................................................ 16 1.2.2. Topologies ....................................................................... 16 1.2.2.1. Network Size and Capacity ............................... 19 1.2.2.2. Direction of Information Flow ............................ 20 1.2.3. Heuristics ......................................................................... 20 1.2.3.1. Supervision ....................................................... 21 1.2.3.2. Delta Rule ......................................................... 21 1.2.3.3. Generalised Delta Rule - Backpropogation ....... 24 1.2.3.3.1. Convergence ........................................ 25 1.2.3.3.1.1. Visualisation ........................... 27 1.2.3.4. Other Algorithms ............................................... 27 1.2.3.5. Energy ............................................................... 28 1.2.3.6. Coding in the Hidden Layers ............................. 29 1.2.3.7. Comparison with Traditional Classifiers ............ 30 1.3. Advantages and Disadvantages ................................................... 30 1.3.1. Generalisation .................................................................. 31 1.3.2. Graceful Degradation ....................................................... 32 1.3.3. Speed, Scaling and System Requirements ...................... 32 1.3.4. Hardware Implementation ................................................ 34 1.4. Applications .................................................................................. 35 1.4.1 . Speech Recognition & Synthesis ..................................... 35 1.4.2. ECG ................................................................................. 36 1.4.3. EEG ................................................................................. 36 1.4.4. Image Recognition ........................................................... 36 5 1.4.5. Al and Expert Systems ..................................................... 36 1.4.6. Filtering ............................................................................ 37 1.4.7. Other ................................................................................ 37 2. Heart Sounds and Phonocardiography - A Review ............................. 38 2.1. Synopsis ....................................................................................... 38 2.2. Introduction ................................................................................... 38 2.3. Cardiac Anatomy .......................................................................... 39 2.4. The Cardiac Cycle ........................................................................ 40 2.5. Heart Sounds and Murmurs ......................................................... 42 2.5.1. Production Mechanisms ................................................... 42 2.5.2. Factors Affecting Auscultated Sounds ............................. 45 2.5.2.1. Regions for Auscultation ................................... 45 2.5.2.2. Sound Transmission ......................................... 46 2.5.3. The First Heart Sound ...................................................... 46 2.5.4. The Second Heart Sound ............................................... .47 2.5.5. The Third Heart Sound .................................................... 48 2.5.6. The Fourth Heart Sound