New types of patient health records aim to help physicians shift from a medical practice, often based on their personal experience, towards one of evidence based medicine, thus improving the communication among patients and care providers and increasing the availability of personal medical information. These new records, allowing patients and care providers to share medical data and clinical information, and access them whenever they need, can be considered enabling Ambient Assisted Living technologies. Furthermore, new personal disease monitoring tools support specialists in their tasks, as an example allowing acquisition, transmission and analysis of medical images. The growing interest around these new technologies poses serious questions regarding data integrity and transaction security. The huge amount of sensitive data stored in these new records surely attracts the interest of malicious hackers, therefore it is necessary to guarantee the integrity and the maximum security of servers and transactions. Blockchain technology can be an important turning point in the development of personal health records. This paper discusses some issues regarding the management and protection of health data exchanged through new medical or diagnostic devices.

The automated detection of suspicious anomalies in electrocardiogram (ECG) recordings allows frequent personal heart health monitoring and can drastically reduce the number of ECGs that need to be manually examined by the cardiologists, excluding those classified as normal, facilitating healthcare decision-making and reducing a considerable amount of time and money. In this paper, we present a system able to automatically detect the suspect of cardiac pathologies in ECG signals from personal monitoring devices, with the aim to alert the patient to send the ECG to the medical specialist for a correct diagnosis and a proper therapy. The main contributes of this work are: (a) the implementation of a binary classifier based on a 1D-CNN architecture for detecting the suspect of anomalies in ECGs, regardless of the kind of cardiac pathology; (b) the analysis was carried out on 21 classes of different cardiac pathologies classified as anomalous; and (c) the possibility to classify anomalies even in ECG segments containing, at the same time, more than one class of cardiac pathologies. Moreover, 1D-CNN based architectures can allow an implementation of the system on cheap smart devices with low computational complexity. The system was tested on the ECG signals from the MIT-BIH ECG Arrhythmia Database for the MLII derivation. Two different experiments were carried out, showing remarkable performance compared to other similar systems. The best result showed high accuracy and recall, computed in terms of ECG segments and even higher accuracy and recall in terms of patients alerted, therefore considering the detection of anomalies with respect to entire ECG recordings.

Anemia is a global public health problem with major consequences for human health. About a quarter of the world population shows a hemoglobin concentration that is below the recommended thresholds. Non-invasive methods for monitoring and identifying potential risk of anemia and smartphone-based devices to perform this task are promising in addressing this pathology. We have considered some well-known studies carried out on this topic since the main purpose of this work was not to produce a review. The first group of papers describes the approaches for the clinical evaluation of anemia focused on different human exposed tissues, while we used a second group to overview some technologies, basic methods, and principles of operation of some devices and highlight some technical problems. Results extracted from the second group of papers examined were aggregated in two comparison tables. A growing interest in this topic is demonstrated by the increasing number of papers published recently. We believe we have identified several critical issues in the published studies, including those published by us. Just as an example, in many papers the dataset used is not described. With this paper we wish to open a discussion on these issues. Few papers have been sufficient to highlight differences in the experimental conditions and this makes the comparison of the results difficult. Differences are also found in the identification of the regions of interest in the tissue, descriptions of the datasets, and other boundary conditions. These critical issues are discussed together with open problems and common mistakes that probably we are making. We propose the definition of a road-map and a common agenda for research on this topic. In this sense, we want to highlight here some issues that seem worthy of common discussion and the subject of synergistic agreements. This paper, and in particular, the discussion could be the starting point for an open debate about the dissemination of our experiments and pave the way for further updates and improvements of what we have outlined.