David Springer

An open access database for the evaluation of heart sound algorithms

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 literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.

Logistic Regression-HSMM-Based Heart Sound Segmentation

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, probabilistic models, such as hidden Markov models, have recently been shown to surpass the capabilities of previous methods. Segmentation performance is further improved when apriori information about the expected duration of the states is incorporated into the model, such as in a hidden semiMarkov model (HSMM). This paper addresses the problem of the accurate segmentation of the first and second heart sound within noisy real-world PCG recordings using an HSMM, extended with the use of logistic regression for emission probability estimation. In addition, we implement a modified Viterbi algorithm for decoding the most likely sequence of states, and evaluated this method on a large dataset of 10 172 s of PCG recorded from 112 patients (including 12 181 first and 11 627 second heart sounds). The proposed method achieved an average F1 score of 95.63 ± 0.85%, while the current state of the art achieved 86.28 ± 1.55% when evaluated on unseen test recordings. The greater discrimination between states afforded using logistic regression as opposed to the previous Gaussian distribution-based emission probability estimation as well as the use of an extended Viterbi algorithm allows this method to significantly outperform the current state-of-the-art method based on a two-sided paired t-test.

Mobile Phone Text Messages to Support Treatment Adherence in Adults With High Blood Pressure (SMS-Text Adherence Support [StAR]) A Single-Blind, Randomized Trial

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, 3-arm, randomized trial (SMS-Text Adherence Support [StAR]) undertaken in South Africa, patients treated for high blood pressure were randomly allocated in a 1:1:1 ratio to information only, interactive SMS text messaging, or usual care. The primary outcome was change in systolic blood pressure at 12 months from baseline measured with a validated oscillometric device. All trial staff were masked to treatment allocation. Analyses were intention to treat. Between June 26, 2012, and November 23, 2012, 1372 participants were randomized to receive information-only SMS text messages (n=457), interactive SMS text messages (n=458), or usual care (n=457). Primary outcome data were available for 1256 participants (92%). At 12 months, the mean adjusted change in systolic blood pressure compared with usual care was −2.2 mm Hg (95% confidence interval, −4.4 to −0.04) with information-only SMS and −1.6 mm Hg (95% confidence interval, −3.7 to 0.6) with interactive SMS. Odds ratios for the proportion of participants with a blood pressure <140/90 mm Hg were 1.42 (95% confidence interval, 1.03–1.95) for information-only messaging and 1.41 (95% confidence interval, 1.02–1.95) for interactive messaging compared with usual care. Conclusions— In this randomized trial of an automated adherence support program delivered by SMS text message in a general outpatient population of adults with high blood pressure, we found a small reduction in systolic blood pressure control compared with usual care at 12 months. There was no evidence that an interactive intervention increased this effect. Clinical Trial Registration— URL: http://www.clinicaltrials.gov. Unique identifier: NCT02019823. South African National Clinical Trials Register, number SANCTR DOH-27-1212-386; Pan Africa Trial Register, number PACTR201411000724141.

Heart beat detection in multimodal physiological data using a hidden semi-Markov model and signal quality indices.

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. However, due to noisy and missing data, these alarms are frequently deemed to be false positives, and therefore ignored by clinical staff. The fusion of features derived from other signals, such as the arterial blood pressure (ABP) or the photoplethysmogram (PPG), has the potential to reduce such false alarms. In order to leverage the highly correlated temporal nature of the physiological signals, a hidden semi-Markov model (HSMM) approach, which uses the intra- and inter-beat depolarization interval, was designed to detect heart beats in such data. Features based on the wavelet transform, signal gradient and signal quality indices were extracted from the ECG and ABP waveforms for use in the HSMM framework. The presented method achieved an overall score of 89.13% on the hidden/test data set provided by the Physionet/Computing in Cardiology Challenge 2014: Robust Detection of Heart Beats in Multimodal Data.

Low-Cost Blood Pressure Monitor Device for Developing Countries

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 automated BP measurement devices are scarce because supply channels are limited and relative costs are high. Consequently, routine screening for and monitoring of hypertension is not common place. In this project we aim to offer an alternative strategy to measure BP and Heart Rate (HR) in developing countries. Given that mobile phones are becoming increasingly available and affordable in these regions, we designed a system that comprises low-cost peripherals with minimal electronics, offloading the main processing to the phone. A simple pressure sensor passes information to the mobile phone and the oscillometric method is used to determine BP and HR. Data are then transmitted to a central medical record to reduce errors in time stamping and information loss.

Automated signal quality assessment of mobile phone-recorded heart sound signals

Abstract 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 recordings since non-experts are more likely to make poor-quality recordings. This paper investigates the automated signal quality assessment of heart sound recordings performed using both mobile phone-based and commercial medical-grade electronic stethoscopes. The recordings, each 60 s long, were taken from 151 random adult individuals with varying diagnoses referred to a cardiac clinic and were professionally annotated by five experts. A mean voting procedure was used to compute a final quality label for each recording. Nine signal quality indices were defined and calculated for each recording. A logistic regression model for classifying binary quality was then trained and tested. The inter-rater agreement level for the stethoscope and mobile phone recordings was measured using Conger’s kappa for multiclass sets and found to be 0.24 and 0.54, respectively. One-third of all the mobile phone-recorded phonocardiogram (PCG) signals were found to be of sufficient quality for analysis. The classifier was able to distinguish good- and poor-quality mobile phone recordings with 82.2% accuracy, and those made with the electronic stethoscope with an accuracy of 86.5%. We conclude that our classification approach provides a mechanism for substantially improving auscultation recordings by non-experts. This work is the first systematic evaluation of a PCG signal quality classification algorithm (using a separate test dataset) and assessment of the quality of PCG recordings captured by non-experts, using both a medical-grade digital stethoscope and a mobile phone.

Signal quality classification of mobile phone-recorded phonocardiogram signals

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 3M Littmann 3200 electronic stethoscope and an iPhone 3G). These recordings were professionally annotated and classified using a support vector machine (SVM) and a combination of ten signal quality metrics computed from each recording as input features. One third of all mobile phone-recorded PCGs were found to be of high quality. The classifier was able to distinguish good and bad-quality iPhone recordings with 87.0% accuracy, the Littmann recordings with accuracy of 76.4% and the combined set with accuracy of 85.6% on unseen test data. Therefore, the quality of PCGs made with a range of stethoscopes can be accurately classified using this technique.

Mobile Phone Text Messages to Support Treatment Adherence in Adults With High Blood Pressure (StAR): A Single-Blind, Randomized Trial

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, 3-arm, randomized trial (SMS-Text Adherence Support [StAR]) undertaken in South Africa, patients treated for high blood pressure were randomly allocated in a 1:1:1 ratio to information only, interactive SMS text messaging, or usual care. The primary outcome was change in systolic blood pressure at 12 months from baseline measured with a validated oscillometric device. All trial staff were masked to treatment allocation. Analyses were intention to treat. Between June 26, 2012, and November 23, 2012, 1372 participants were randomized to receive information-only SMS text messages (n=457), interactive SMS text messages (n=458), or usual care (n=457). Primary outcome data were available for 1256 participants (92%). At 12 months, the mean adjusted change in systolic blood pressure compared with usual care was −2.2 mm Hg (95% confidence interval, −4.4 to −0.04) with information-only SMS and −1.6 mm Hg (95% confidence interval, −3.7 to 0.6) with interactive SMS. Odds ratios for the proportion of participants with a blood pressure <140/90 mm Hg were 1.42 (95% confidence interval, 1.03–1.95) for information-only messaging and 1.41 (95% confidence interval, 1.02–1.95) for interactive messaging compared with usual care. Conclusions— In this randomized trial of an automated adherence support program delivered by SMS text message in a general outpatient population of adults with high blood pressure, we found a small reduction in systolic blood pressure control compared with usual care at 12 months. There was no evidence that an interactive intervention increased this effect. Clinical Trial Registration— URL: http://www.clinicaltrials.gov. Unique identifier: NCT02019823. South African National Clinical Trials Register, number SANCTR DOH-27-1212-386; Pan Africa Trial Register, number PACTR201411000724141.

The SMS-text adherence support (StAR) study: hardware and software infrastructure

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 hypertension treatment adherence compared to usual care. The intervention is a structured program of clinic appointment and medication pick-up reminders, medication adherence support and hypertension-related education delivered remotely using an automated system of semi-tailored informational or interactive SMS-text messages delivered to the 1372 recruited participants. The technical infrastructure includes mobile device-based electronic data collection at the point of care with secure remote upload of the data; the intervention delivery system; real-time query identification for screening, enrolment and participant management procedures; and monitoring and evaluation of trial processes. We are using several open-source software platforms in conjunction with off-the-shelf hardware to set-up and run a randomised clinical trial of a novel intervention in a low-resource setting.

Using the Medical Research Council framework for development and evaluation of complex interventions in a low resource setting to develop a theory-based treatment support intervention delivered via SMS text message to improve blood pressure control.

BackgroundSeveral frameworks now exist to guide intervention development but there remains only limited evidence of their application to health interventions based around use of mobile phones or devices, particularly in a low-resource setting. We aimed to describe our experience of using the Medical Research Council (MRC) Framework on complex interventions to develop and evaluate an adherence support intervention for high blood pressure delivered by SMS text message. We further aimed to describe the developed intervention in line with reporting guidelines for a structured and systematic description.MethodsWe used a non-sequential and flexible approach guided by the 2008 MRC Framework for the development and evaluation of complex interventions.ResultsWe reviewed published literature and established a multi-disciplinary expert group to guide the development process. We selected health psychology theory and behaviour change techniques that have been shown to be important in adherence and persistence with chronic medications. Semi-structured interviews and focus groups with various stakeholders identified ways in which treatment adherence could be supported and also identified key features of well-regarded messages: polite tone, credible information, contextualised, and endorsed by identifiable member of primary care facility staff. Direct and indirect user testing enabled us to refine the intervention including refining use of language and testing of interactive components.ConclusionsOur experience shows that using a formal intervention development process is feasible in a low-resource multi-lingual setting. The process enabled us to pre-test assumptions about the intervention and the evaluation process, allowing the improvement of both. Describing how a multi-component intervention was developed including standardised descriptions of content aimed to support behaviour change will enable comparison with other similar interventions and support development of new interventions. Even in low-resource settings, funders and policy-makers should provide researchers with time and resources for intervention development work and encourage evaluation of the entire design and testing process.Trial registrationThe trial of the intervention is registered with South African National Clinical Trials Register number (SANCTR DOH-27-1212-386; 28/12/2012); Pan Africa Trial Register (PACTR201411000724141; 14/12/2013); ClinicalTrials.gov (NCT02019823; 24/12/2013).