PC based storage and processing of electrocardiogram tracings recorded with a HP4745A pagewriter II cardiograph

ThesisCurrently the Department of Cardiology, Universitas Hospital, keeps paper copies of ECGs filed in large filing cabinets. Access to these files is tedious during office hours, and impossible after hours, when the filing room is locked and no filing personnel are available. Commercially available systems for computerised storage of ECG data are available from a number of vendors. Some drawbacks of these systems include: • Extremely expensive. • Only a portion of the functions offered by these systems are really needed at the Department of Cardiology, Universitas Hospital. These systems are thus not economically justifiable by the Department of Cardiology, Universitas Hospital. • Some require new/different ECG machines to be used. • Some require an expensive computer system to be installed. • Additional space is needed for additional equipment. • Staff needs to be extensively trained to use the new equipment. This dissertation describes the development of a dynamic link library (DLL) which is used to acquire and decode data from a Hewlet Packard HP4745A Cardiograph II Page Writer electrocardiograph. Furthermore, the database application using the HP4745A DLL can also be expanded to accept data from other ECG machines. The acquisition and decoding DLL must be developed to produce a decoded data file conforming to the format described in this dissertation. By storing these decoded data in a database such as Hearts 32, the data can be reprocessed (drawing of ECG traces on screen or on printer). Selected leads from different ECGs can also be plotted on the same screen. Fast access to previous ECGs will help the cardiologists at the Universitas Hospital in Bloemfontein to improve patient care. The cardiac patients of the Free State community as well as the staff at the Department of Cardiology, Universitas Hospital, Bloemfontein can benefit from the results of this research

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Intelligent Pattern Analysis of the Foetal Electrocardiogram

The aim of the project on which this thesis is based is to develop reliable techniques for foetal electrocardiogram (ECG) based monitoring, to reduce incidents of unnecessary medical intervention and foetal injury during labour. World-wide electronic foetal monitoring is based almost entirely on the cardiotocogram (CTG), which is a continuous display of the foetal heart rate (FHR) pattern together with the contraction of the womb. Despite the widespread use of the CTG, there is no significant improvement in foetal outcome. In the UK alone it is estimated that birth related negligence claims cost the health authorities over £400M per-annum. An expert system, known as INFANT, has recently been developed to assist CTG interpretation. However, the CTG alone does not always provide all the information required to improve the outcome of labour. The widespread use of ECG analysis has been hindered by the difficulties with poor signal quality and the difficulties in applying the specialised knowledge required for interpreting ECG patterns, in association with other events in labour, in an objective way. A fundamental investigation and development of optimal signal enhancement techniques that maximise the available information in the ECG signal, along with different techniques for detecting individual waveforms from poor quality signals, has been carried out. To automate the visual interpretation of the ECG waveform, novel techniques have been developed that allow reliable extraction of key features and hence allow a detailed ECG waveform analysis. Fuzzy logic is used to automatically classify the ECG waveform shape using these features by using knowledge that was elicited from expert sources and derived from example data. This allows the subtle changes in the ECG waveform to be automatically detected in relation to other events in labour, and thus improve the clinicians position for making an accurate diagnosis. To ensure the interpretation is based on reliable information and takes place in the proper context, a new and sensitive index for assessing the quality of the ECG has been developed. New techniques to capture, for the first time in machine form, the clinical expertise / guidelines for electronic foetal monitoring have been developed based on fuzzy logic and finite state machines, The software model provides a flexible framework to further develop and optimise rules for ECG pattern analysis. The signal enhancement, QRS detection and pattern recognition of important ECG waveform shapes have had extensive testing and results are presented. Results show that no significant loss of information is incurred as a result of the signal enhancement and feature extraction techniques