Giovanna Sannino

A rule-based mHealth system for cardiac monitoring

mHealth systems are becoming very attractive for the home care monitoring and, in particular, for the monitoring of patients with heart failure. Knowledge-based technologies can be profitably used to design advanced software system able to provide efficient and dependable service to patients and physicians. In this paper we present a Rule-based Decision Support System for mHealth environments; the designed intelligent system is devised to the detection and signaling of abnormal or emergency situations by using contextual information, i.e. by correlating data coming from a wearable electrocardiography (ECG) device with information regarding patients posture and his/her physical activities. The whole system has been developed in Java.

A revised scheme for real time ECG Signal denoising based on recursive filtering

Abstract In many healthcare applications, artifacts mask or corrupt important features of Electrocardiogram (ECG) signals. In this paper we describe a revised scheme for ECG signal denoising based on a recursive filtering methodology. We suggest a suitable class of kernel functions in order to remove artifacts in the ECG signal, starting from noise frequencies in the Fourier domain. Our approach does not require high computational requirements and this feature offers the possibility of an implementation of the scheme directly on mobile computing devices. The proposed scheme allows local denoising and hence a real time visualization of the signal by means of a strategy based on boundary conditions. Experiments on real datasets have been carried out in order to test, in terms of computation and accuracy, the proposed algorithm. Finally, comparative results with other well-known denoising methods are shown.

A Novel O(n) Numerical Scheme for ECG Signal Denoising

Abstract High quality Electrocardiogram (ECG) data is very important because this signal is generally used for the analysis of heart diseases. Wearable sensors are widely adopted for physical activity monitoring and for the provision of healthcare services, but noise always degrades the quality of these signals. This paper describes a new algorithm for ECG signal denoising, applicable in the contest of the real-time health monitoring using mobile devices, where the signal processing efficiency is a strict requirement. The proposed algorithm is computationally cheap because it belongs to the class of Infinite Impulse Response (IIR) noise reduction algorithms. The main contribution of the proposed scheme is that removes the noises frequencies without the implementation of the Fast Fourier Transform that would require the use of special optimized libraries. It is composed by only few code lines and hence offers the possibility of implementation on mobile computing devices in an easy way. Moreover, the scheme allows the local denoising and hence a real time visualization of the denoised signal. Experiments on real datasets have been carried out in order to test the algorithm from accuracy and computational point of view.

A smart context-aware mobile monitoring system for heart patients

Patient context awareness is an important concept for application services in mHealth environments. In this paper we present a multisensors system that uses a rule-based DSS able to enhance the accuracy of potentially dangerous Heart Rate variability by taking into account patient context information. We have designed and implemented an intelligent system that allows receiving data from several sensors and provides the computational power for context recognition. We also show that the use of an intelligent approach relying on a rule-based DSS for the analysis of data and vital signs is better than approaches missing either DSS or context-awareness.

An Evolved eHealth Monitoring System for a Nuclear Medicine Department

The use of medical devices and wearable sensor-based systems to monitor patients health conditions are continuously increasing both internally to hospitals and at home. In this paper we propose a system that is intended to monitor patients physiological parameters to detect abnormal cardiac accelerations and patient falls in real-time. The core of this paper is the realization of a user-friendly and context-aware system that uses a rule-based Decision Support System to elaborate the captured sensors data. The paper also describes a case study where the system has revealed important benefits for both patients and medical staff.

An m-health system for the estimation of voice disorders

In recent years, the prevalence of voice disorders has been increasing dramatically, mainly due to unhealthy lifestyles and voice abuse. An impairment in the ability to produce the sound of the voice is indicated by the medical term Dysphonia. This disorder impacts on the quality of life of the population in general, but especially of professional voice users, like teachers or singers, for whom dysphonia is more common. In this paper we present an accurate and robust methodology for the estimation of the Fundamental Frequency (F0) developed in a mobile application and able to perform a simple and fast voice screening. The mobile app acquires and analyzes vocal signals requiring of the user only a vocalization of the vowel /a/. Automatically, the app indicates to the user his/her own voice status evaluating the value of F0, and discriminating a pathological voice from healthy one. To estimate the classification accuracy of the implemented methodology, we have performed several experimental tests on an available database and we have compared the results with other algorithms in the literature.

A supervised approach to automatically extract a set of rules to support fall detection in an mHealth system

A cheap and portable approach to detect fall detection in real time is proposed.Acceleration data are gathered by a wearable sensor and sent to a mobile device.A set of IF-THEN rules is automatically extracted from acceleration data.This set of rules can be used in our real-time mobile monitoring system.If occurrence of a fall is detected by a rule, an alarm is automatically produced. Automatic fall detection is a major issue in the health care of elderly people. In this task the ability to discriminate in real time between falls and normal daily activities is crucial. Several methods already exist to perform this task, but approaches able to provide explicit formalized knowledge and high classification accuracy have not yet been developed and would be highly desirable. To achieve this aim, this paper proposes an innovative and complete approach to fall detection based both on the automatic extraction of knowledge expressed as a set of IF-THEN rules from a database of fall recordings, and on its use in a mobile health monitoring system. Whenever a fall is detected by this latter, the system can take immediate actions, e.g. alerting medical personnel. Our method can easily overcome the limitations of other approaches to fall detection. In fact, thanks to the knowledge gathering, it overcomes both the difficulty faced by a human being dealing with many parameters and trying to find out which are the most suitable, and also the need to apply a laborious trial-and-error procedure to find the values of the related thresholds. In addition, in our approach the extracted knowledge is processed in real time by a reasoner embedded in a mobile device, without any need for connection to a remote server. This proposed approach has been compared against four other classifiers on a database of falls simulated by volunteers, and its discrimination ability has been shown to be higher with an average accuracy of 91.88%. We have also carried out a very preliminary experimental phase. The best set of rules found by using the previous database has allowed us to achieve satisfactory performance in these experiments as well. Namely, on these real-world falls the obtained results in terms of accuracy, sensitivity, and specificity are of about 92%, 86%, and 96%, respectively.

Wearable technology and ECG processing for fall risk assessment, prevention and detection

Falls represent one of the most common causes of injury-related morbidity and mortality in later life. Subjects with cardiovascular disorders (e.g., related to autonomic dysfunctions and postural hypotension) are at higher risk of falling. Autonomic dysfunctions increasing the risk of falling in the short and mid-term could be assessed by Heart Rate Variability (HRV) extracted by electrocardiograph (ECG). We developed three trials for assessing the usefulness of ECG monitoring using wearable devices for: risk assessment of falling in the next few weeks; prevention of imminent falls due to standing hypotension; and fall detection. Statistical and data-mining methods are adopted to develop classification and regression models, validated with the cross-validation approach. The first classifier based on HRV features enabled to identify future fallers among hypertensive patients with an accuracy of 72% (sensitivity: 51.1%, specificity: 80.2%). The regression model to predict falls due to orthostatic dropdown from HRV recorded before standing achieved an overall accuracy of 80% (sensitivity: 92%, specificity: 90%). Finally, the classifier to detect simulated falls using ECG achieved an accuracy of 77.3% (sensitivity: 81.8%, specificity: 72.7%). The evidence from these three studies showed that ECG monitoring and processing could achieve satisfactory performances compared to other system for risk assessment, fall prevention and detection. This is interesting as differently from other technologies actually employed to prevent falls, ECG is recommended for many other pathologies of later life and is more accepted by senior citizens.

New trends in Biotechnology: The point on NGS Cloud computing solutions

The advent of Cloud computing is changing the way of conceiving information and communication systems in different application fields including Biotechnology. In this context, an emerging research field is Next-Generation Sequencing (NGS) that includes several recent technologies allowing sequencing DNA and that have revolutionized the study of genomics and molecular biology. These cutting-edge sequencing systems produce big datasets that require significant scalable computing resources. In this paper, we analyse and classify the major current NGS Cloud-based solutions adopted in scientific laboratories according to different Cloud service levels. Moreover, by means of a taxonomy, we discuss the challenges and advantages of possible future NGS Cloud-based systems.

Short term Heart Rate Variability to predict blood pressure drops due to standing: a pilot study

BackgroundStanding from a bed or chair may cause a significant lowering of blood pressure (ΔBP), which may have severe consequences such as, for example, falls in older subjects. The goal of this study was to develop a mathematical model to predict the ΔBP due to standing in healthy subjects, based on their Heart Rate Variability, recorded in the 5 minutes before standing.MethodsHeart Rate Variability was extracted from an electrocardiogram, recorded from 10 healthy subjects during the 5 minutes before standing. The blood pressure value was measured before and after rising. A mathematical model aiming to predict ΔBP based on Heart Rate Variability measurements was developed using a robust multi-linear regression and was validated with the leave-one-subject-out cross-validation technique.ResultsThe model predicted correctly the ΔBP in 80% of experiments, with an error below the measurement error of sphygmomanometer digital devices (±4.5 mmHg), a false negative rate of 7.5% and a false positive rate of 10%. The magnitude of the ΔBP was associated with a depressed and less chaotic Heart Rate Variability pattern.ConclusionsThe present study showes that blood pressure lowering due to standing can be predicted by monitoring the Heart Rate Variability in the 5 minutes before standing.