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.

[Experimentation of an e-platform to detect situations at risk of cardiac impairment (platform E-care) in an internal medicine unit]

INTRODUCTION

Monitoring patients with heart failure by telemedicine systems is a potential means susceptible to optimize the management of these patients and avoid life-threatening emergencies. In this context, we experimented in internal medicine unit an e-platform E-care dedicated to automated, intelligent detection of situations at risk of heart failure.

METHODS

The E-care platform based on medical sensors (blood pressure, heart rate, O2, weight), communicating (Bluetooth), to go up, in real time, to an intelligent physiological information and an analysis of the ontology medical, leading ultimately to the generation of alerts. After a development phase (proof of concept), the E-care platform has been deployed and tested by health professionals and patients in an internal medicine unit with 20 beds, opened on emergencies to the Strasbourg University Hospitals.

RESULTS

One hundred and eighty patients were included and 1500 measurements were obtained. The patient profile included in this experiment was an elderly patient, with comorbidity in 90% of cases, with a loss of autonomy in 25%. Health professionals were using E-care platform every day to their great satisfaction. This experiment made it possible to validate the technology choices, to consolidate the system, and to test the robustness of the platform E-care. The collection continuously allowed us to have the critical number of patients for more detailed analysis of the relevance of alerts related to heart impairment. A preliminary analysis showed the relevance of the generated alerts.

CONCLUSION

Preliminary results following the deployment of E-care platform in hospitals appear to show the relevance of technological choices, tools and solutions developed and adopted. This telemedicine system allows automatic, non-intrusive, generate alerts related to the detection of situations at risk for heart failure. Ultimately, E-care was capable of preventing hospitalization. A home deployment is currently underway.

QRS detection using S-Transform and Shannon energy

This paper presents a novel method for QRS detection in electrocardiograms (ECG). It is based on the S-Transform, a new time frequency representation (TFR). The S-Transform provides frequency-dependent resolution while maintaining a direct relationship with the Fourier spectrum. We exploit the advantages of the S-Transform to isolate the QRS complexes in the time-frequency domain. Shannon energy of each obtained local spectrum is then computed in order to localize the R waves in the time domain. Significant performance enhancement is confirmed when the proposed approach is tested with the MIT-BIH arrhythmia database (MITDB). The obtained results show a sensitivity of 99.84%, a positive predictivity of 99.91% and an error rate of 0.25%. Furthermore, to be more convincing, the authors illustrated the detection parameters in the case of certain ECG segments with complicated patterns.