J Med Syst
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In a world where the industry of mobile applications is continuously expanding and new health care apps and devices are created every day, it is important to take special care of the collection and treatment of users' personal health information. However, the appropriate methods to do this are not usually taken into account by apps designers and insecure applications are released. This paper presents a study of security and privacy in mHealth, focusing on three parts: a study of the existing laws regulating these aspects in the European Union and the United States, a review of the academic literature related to this topic, and a proposal of some recommendations for designers in order to create mobile health applications that satisfy the current security and privacy legislation. This paper will complement other standards and certifications about security and privacy and will suppose a quick guide for apps designers, developers and researchers.
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Clinical Trial
Determining the appropriate amount of anesthetic gas using DWT and EMD combined with neural network.
The spectrum of EEG has been studied to predict the depth of anesthesia using variety of signal processing methods up to date. Those standard models have used the full spectrum of EEG signals together with the systolic-diastolic pressure and pulse values. As it is generally agreed today that the brain is in stable state and the delta-theta bands of the EEG spectrum remain active during anesthesia. ⋯ Discrete wavelet transformation (DWT) and empirical mode decomposition (EMD) were applied to the EEG signals to extract delta-theta bands. The power density spectrum (PSD) values of target bands were presented as inputs to multi-layer perceptron (MLP) neural network (NN), which predicted the gas level. The present study has practical implications in terms of using less data, in an effective way and also saves time as well.
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This study aimed to assess the accuracy of time-of-flight magnetic resonance angiography, computed tomography, and conventional angiography in depicting the actual length of the blood vessels. Three-dimensional time-of-flight magnetic resonance angiography and computed tomography angiography were performed using a flow phantom model that was 2.11 mm in diameter and had a total area of 0.26 cm(2). After this, volume rendering technique and the maximum intensity projection method as well as two-dimensional digital subtraction angiography and three-dimensional rotational angiography based on conventional angiography were conducted. ⋯ Although this image was magnified similar to the image reconstructed by 100 % in the 256 matrix of three-dimensional rotational angiography (P < 0.05), it was closest to the actual image among the images compared in this study. In conclusion, images obtained by two-dimensional digital subtraction angiography were closer to the actual images compared to the images obtained by other procedures. It can be concluded that vascular images obtained by magnetic resonance angiography, CT angiography, and conventional angiography were larger than the actual images.
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Recently, mass casualty incidents (MCIs) have been occurring frequently and have gained international attention. There is an urgent need for scientifically proven and effective emergency responses to MCIs, particularly as the severity of incidents is continuously increasing. The emergency response to MCIs is a multi-dimensional and multi-participant dynamic process that changes in real-time. ⋯ In this study, we have designed a mobile-based system to collect medical and temporal data produced during an emergency response to an MCI. Using this information, our system's decision-making model can provide personal evacuation suggestions that improve the overall outcome of an emergency response. The effectiveness of our system in reducing overall mortality has been validated by an agent-based simulation model established to simulate an emergency response to an MCI.