J Med Syst
-
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.
-
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.