David B. Springer

David B. Springer
University of Oxford | OX · Department of Engineering Science

PhD

About

20
Publications
15,085
Reads
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1,431
Citations
Citations since 2017
5 Research Items
1324 Citations
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2017201820192020202120222023050100150200250
Introduction

Publications

Publications (20)
Article
Full-text available
Background Failure to take medicines for diabetes as prescribed contributes to poor outcomes from the condition. Mobile phones are ubiquitous and short message service (SMS) texts have shown promise as a low-cost intervention. We tested the effectiveness of SMS-text messaging in improving outcomes in adults with type 2 diabetes. Methods StAR2D was...
Article
Full-text available
Background: Health outcomes for people treated for type 2 diabetes could be substantially improved in sub-Saharan Africa. Failure to take medicine regularly to treat diabetes has been identified as a major problem. Resources to identify and support patients who are not making the best use of medicine in low- and middle-income settings are scarce....
Preprint
Full-text available
UNSTRUCTURED Background: Health outcomes for people treated for type 2 diabetes could be substantially improved in sub-Saharan Africa. Failure to take medicines regularly to treat diabetes has been identified as a major problem. Resources to identify and support patients who are not making best use of medicine in low and middle-income settings are...
Article
Objective: Heart sound segmentation is a prerequisite step for the automatic analysis of heart sound signals, facilitating the subsequent identification and classification of pathological events. Recently, hidden Markov model-based algorithms have received increased interest due to their robustness in processing noisy recordings. In this study we...
Conference Paper
Full-text available
In the past few decades heart sound signals (i.e., phono-cardiograms or PCGs) have been widely studied. Automated heart sound segmentation and classification techniques have the potential to screen for pathologies in a variety of clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of a...
Article
Full-text available
In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the litera...
Article
Full-text available
Mobile phones, due to their audio processing capabilities, have the potential to facilitate the diagnosis of heart disease through automated auscultation. However, such a platform is likely to be used by non-experts, and hence, it is essential that such a device is able to automatically differentiate poor quality from diagnostically useful recordin...
Article
Full-text available
Background: -We assessed the effect of automated treatment adherence support delivered via mobile-phone short message system (SMS) text-messages on blood pressure. Methods and results: -In this pragmatic single-blind, three-arm randomized trial (StAR), undertaken in South Africa, patients treated for high blood pressure were randomly allocated i...
Article
The identification of the exact positions of the first and second heart sounds within a phonocardiogram (PCG), or heart sound segmentation, is an essential step in the automatic analysis of heart sound recordings, allowing for the classification of pathological events. While threshold-based segmentation methods have shown modest success, probabilis...
Article
Accurate heart beat detection in signals acquired from intensive care unit (ICU) patients is necessary for establishing both normality and detecting abnormal events. Detection is normally performed by analysing the electrocardiogram (ECG) signal, and alarms are triggered when parameters derived from this signal exceed preset or variable thresholds....
Conference Paper
The segmentation of the primary heart sounds within a phonocardiogram (PCG) is an essential step in the classification of pathological cardiac events. Recently, probabilistic models, such as hidden Markov models, have been shown to surpass the segmentation capabilities of previous methods. These models are further improved when a priori information...
Conference Paper
There is potential for the use of mobile phones to remotely identify patients with a high risk of heart conditions using automated auscultation. However, accurate heart sound analysis is dependent on the quality of heart sound recordings. This paper investigates the signal quality classification of phonocardiograms (PCGs) recorded on two devices (a...
Article
Full-text available
Background: Interventions to support people with hypertension in attending clinics and taking their medication have potential to improve outcomes, but delivery on a wide scale and at low cost is challenging. Some trials evaluating clinical interventions using short message service (SMS) text-messaging systems have shown important outcomes, althoug...
Article
The automatic detection of heartbeats within physiological signals collected from patients connected to bedside monitors is an important task as it allows the detection of pathological conditions. Heartbeat detection is traditionally performed using the ECG. However, all bedside monitors are prone to missing data, yet it is rare for any system to i...
Article
Accurate heart rate estimation is a fundamental process when analysing phonocardiograms (PCGs). While this is trivial in noise-free recordings, it becomes a difficult task in PCGs corrupted by various noise sources. While numerous PCG-based heart rate estimation techniques have been explored in the literature, no comparison between these techniques...
Article
The segmentation of the primary heart sounds within a phonocardiogram (PCG) is an essential step in the classification of pathological cardiac events. Recently, probabilistic models, such as hidden Markov models, have been shown to surpass the segmentation capabilities of previous methods. These models are further improved when a priori information...
Article
It is estimated that between 15.6 and 19.6 million people are living with rheumatic heart disease (RHD) worldwide, accounting for about one million deaths annually and 60% of Africa's open heart surgeries. As RHD results in heart murmurs that are almost always audible during auscultation, a mobile phone-based automatic auscultation device has the p...
Conference Paper
This paper details the hardware and software infrastructure used to collect data and deliver a novel intervention in an on-going individually randomised three-arm parallel group trial in a resource-limited setting. The SMS-text Adherence support trial (StAR) tests the efficacy of a behavioural intervention delivered by SMS-text to support hypertens...
Conference Paper
Taking the Blood Pressure (BP) with a traditional sphygmomanometer requires a trained user. In developed countries, patients who need to monitor their BP at home usually acquire an electronic BP device with an automatic inflate/deflate cycle that determines the BP through the oscillometric method. For patients in resource constrained regions automa...

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Project (1)