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In resource-constrained environments, supply chains for consumables, repairs and calibration of diagnostic equipment are generally poor. To obviate this issue, we propose the use of widely available hardware with a strong supply chain: a cellphone with a hands-free kit. In particular, we focus on the use of the audio channel to determine heart rate (HR) and heart rate variability (HRV) in order to provide a first level screening system for infection. This article presents preliminary work performed on a gold standard database and a cellphone platform. Results indicate that HR and HRV can be accurately assessed from acoustic recordings of heart sounds using only a cellphone and hands-free kit. Heart sound analysis software, which can run on a standard cellphone in real time, has been developed that detects S1 heart sounds with a sensitivity of 92.1% and a positive predictivity of 88.4%. Evaluation of data recorded from cellphones demonstrates that the low-frequency response (<100 Hz) is key to the success of heart sound analysis on cellphones. Noise rejection is also shown to be important. © 2010, Association for the Advancement of Artificial Intelligence.
Placing emphasis on the selection, modeling, classification, and interpretation of data based on advanced signal processing and artificial intelligence techniques, the book helps you design, implement, and evaluate software systems used for ECG ...
ECGSYN, a dynamical model that faithfully reproduces the main features of the human electrocardiogram (ECG), including heart rate variability (HRV), RR intervals and QT intervals is presented. Details of the underlying algorithm and an open-source software implementation in Matlab, C and Java are described. An example of how this model facilitates comparisons of signal processing techniques is provided.
Due to a lack of integration between different sensors, false alarms (FA) in the intensive care unit (ICU) are frequent and can lead to reduced standard of care. We present a novel framework for FA reduction using a machine learning approach to combine up to 114 signal quality and physiological features extracted from the electrocardiogram, photoplethysmograph, and optionally the arterial blood pressure waveform. A machine learning algorithm was trained and evaluated on a database of 4107 expert-labeled life-threatening arrhythmias, from 182 separate ICU visits. On the independent test data, FA suppression results with no true alarm (TA) suppression were 86.4% for asystole, 100% for extreme bradycardia and 27.8% for extreme tachycardia. For the ventricular tachycardia alarms, the best FA suppression performance was 30.5% with a TA suppression rate below 1%. To reduce the TA suppression rate to zero, a reduction in FA suppression performance to 19.7% was required. © 2012 Elsevier Inc.
Healthcare information, and to some extent patient management, is progressing toward a wireless digital future. This change is driven partly by a desire to improve the current state of medicine using new technologies, partly by supply-and-demand economics, and partly by the utility of wireless devices. Wired technology can be cumbersome for patient monitoring and can restrict the behavior of the monitored patients, introducing bias or artifacts. However, wireless technologies, while mitigating some of these issues, have introduced new problems such as data dropout and “information overload” for the clinical team. This review provides an overview of current wireless technology used for patient monitoring and disease management. We identify some of the major related issues and describe some existing and possible solutions. In particular, we discuss the rapidly evolving fields of telemedicine and mHealth in the context of increasingly resource-constrained healthcare systems.