Algorithms and systems for home telemonitoring in biomedical applications

During the past decades, the interest of the healthcare community shifted from the simple treatment of the diseases towards the prevention and maintenance of a healthy lifestyle. This approach is associated to a reduced cost for the Health Systems, having to face the constantly increased expenditures due to the reduced mortality for chronical diseases and to the progressive population ageing. Nevertheless, the high costs related to hospitalization of patients for monitoring procedures that could be better performed at home hamper the full implementation of this approach in a traditional way. Information and Communication Technology can provide a solution to implement a care model closer to the patient, crossing the physical boundaries of the hospitals and thus allowing to reach also those patients that, for a geographical or social condition, could not access the health services as other luckier subjects. This is the case of telemonitoring systems, whose aim is that of providing monitoring services for some health-related parameters at a distance, by means of custom-designed electronic devices. In this thesis, the specific issues associated to two telemonitoring applications are presented, along with the proposed solutions and the achieved results. The first telemonitoring application considered is the fetal electrocardiography. Non-invasive fetal electrocardiography is the recording of the fetal heart electrical activity using electrodes placed on the maternal abdomen. It can provide important diagnostic parameters, such as the beat-to-beat heart rate variability, whose recurring analysis would be useful in assessing and monitoring fetal health during pregnancy. Long term electrocardiographic monitoring is sustained by the absence of any collateral effects for both the mother and the fetus. This application has been tackled from several perspectives, mainly acquisition and processing. From the acquisition viewpoint a study on different skin treatments, disposable commercial electrodes and textile electrodes has been performed with the aim of improving the signal acquisition quality, while simplifying the measurement setup. From the processing viewpoint, different algorithms have been developed to allow extracting the fetal ECG heart rate, starting from an on-line ICA algorithm or exploiting a subtractive approach to work on recordings acquired with a reduced number of electrodes. The latter, took part to the international "Physionet/Computing in Cardiology Challenge" in 2013 entering into the top ten best-performing open-source algorithms. The improved version of this algorithm is also presented, which would mark the 5th and 4th position in the final ranking related to the fetal heart rate and fetal RR interval measurements performance, reserved to the open-source challenge entries, taking into account both official and unofficial entrants. The research in this field has been carried out in collaboration with the Pediatric Cardiology Unit of the Hospital G. Brotzu in Cagliari, for the acquisition of non-invasive fetal ECG signals from pregnant voluntary patients. The second telemonitoring application considered is the telerehabilitation of the hand. The execution of rehabilitation exercises has been proven to be effective in recovering hand functionality in a wide variety of invalidating diseases, but the lack of standardization and continuous medical control cause the patients neglecting this therapeutic procedures. Telemonitoring the rehabilitation sessions would allow the physician to closely follow the patients' progresses and compliance to the prescribed adapted exercises. This application leads to the development of a sensorized telerehabilitation system for the execution and objective monitoring of therapeutic exercises at the patients' home and of the telemedicine infrastructure that give the physician the opportunity to monitor patients' progresses through parameters summarizing the patients' performance. The proposed non-CE marked medical device, patent pending, underwent a clinical trial, reviewed and approved by the Italian Public Health Department, involving 20 patients with Rheumatoid Arthritis and 20 with Systemic Sclerosis randomly assigned to the experimental or the control arm, enrolled for 12 weeks in a home rehabilitation program. The trial, carried out with the collaboration of the Rheumatology Department of the Policlinico Universitario of Cagliari, revealed promising results in terms of hand functionality recovering, highlighting greater improvements for the patients enrolled in the experimental arm, that use the proposed telerehabilitation system, with respect to those of the control arm, which perform similar rehabilitation exercises using common objects

Autonoumous Nervous System biosignal processing via EDA and HRV from a wearable device

The assessment of changes in the autonomous nervous system (ANS) with certain diseases and pathologies conditions, has been demonstrated to have important prognostic and diagnostic value, so delineating the role of autonomous activity is important to prevent health diseases. There are many approaches to directly measure the sympathetic and parasympathetic nervous system, although, most of themare invasive and unable to provide continuous monitoring, leading to inaccurate assessment of the autonomous nervous system. Heart rate variability (HRV) and Electrodermal activity (EDA) are presented has noninvasive methods to assess the ANS, by computing the spectral analysis of both HRV and EDA biosignals. The combination of these signals is necessary to correctly measure the activity of the sympathetic and parasympathetic system, due to the fact that frequency analysis of HRV only provides the level of unbalance between these two systems, while EDA reflects only activity from the sympathetic system. ANS biosignal processing via HRV and EDA from a wearable device was studied in this thesis, in order to provide continuous monitoring. A wearable device is the ideal solution, as HRV can be calculated with photoplethysmography signals from the wrist and EDA from the fingers, providing wireless and continuous monitoring of the subjects. The extraction of the HRV and EDA features, that describe the activity of the sympathetic and parasympathetic system, were obtained by submitting the subjects to a mental arithmetic stress test, and then compared to the baseline values, in order to verify changes in the autonomous nervous system between the two situations. The distinct response to stress for the subjects was then predicted usingmachine-learning classification mechanisms, with the ability to predict how the subject will respond when submitted to a situation of stress, using only time-domain features, instead of frequency-domain features, which reduces the time needed to performthe classification

Study of applications of bio-space technology to patient monitoring systems Progress report

Patient monitoring system design and equipment specifications with physiological response display device and warning syste

Novel Low Complexity Biomedical Signal Processing Techniques for Online Applications

Biomedical signal processing has become a very active domain of research nowadays. With the advent of portable monitoring devices, from accelerometer-enabled bracelets and smart-phones to more advanced vital sign tracking body area networks, this field has been receiving unprecedented attention. Indeed, portable health monitoring can help uncover the underlying dynamics of human health in a way that has not been possible before. Several challenges have emerged however, as these devices present key differences in terms of signal acquisition and processing in comparison with conventional methods. Hardware constraints such as processing power and limited battery capacity make most established techniques unsuitable and therefore, the need for low-complexity yet robust signal processing methods has appeared. Another issue that needs to be addressed is the quality of the signals captured by these devices. Unlike in clinical scenarios, in portable health monitoring subjects are constantly performing their daily activities. Moreover, signals maybe captured from unconventional locations and subsequently, be prone to perturbations. In order to obtain reliable measures from these monitoring devices, one needs to acquire dependable signal quality measures, to avoid false alarms. Indeed, hardware limitations and low-quality signals can greatly influence the performance of portable monitoring devices. Nevertheless, most devices offer simultaneous acquisition of multiple physiological parameters, such as electrocardiogram (ECG) and photoplethysmogram (PPG). Through multi-modal signal processing the overall performance can be improved, for instance by deriving parameters such as heart rate estimation from the most reliable and uncontaminated source. This thesis is therefore, dedicated to propose novel low-complexity biomedical processing techniques for real-time/online applications. Throughout this dissertation, several bio-signals such as the ECG, PPG, and electroencephalogram (EEG) are investigated. %There is an emphasis on ECG processing techniques, as most of the bio-signals recorded today reflect information about the heart. The main contribution of this dissertation consists in two signal processing techniques: 1) a novel ECG QRS-complex detection and delineation technique, and 2) a short-term event extraction technique for biomedical signals. The former is based on a processing technique called mathematical morphology (MM), and adaptively uses subject QRS-complex amplitude- and morphological attributes for a robust detection and delineation. This method is generalized to intra-cardiac electrograms for atrial activation detection during atrial fibrillation. The second method, called the Relative-Energy algorithm, uses short- and long-term signal energies to highlight events of interest and discard unwanted activities. Collectively, the results obtained by these methods suggest that while presenting low-computational costs, they can efficiently and robustly extract biomedical events of interest. Using the relative energy algorithm, a continuous non-binary ECG signal quality index is presented. The ECG quality is determined by creating a cleaned-up version of the input ECG and calculating the correlation coefficient between the cleaned-up and the original ECG. The proposed quality index is fast and can be implemented online, making it suitable for portable monitoring scenarios