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Health Early Alarm Recognition And Telemonitoring System

Deliverables

Inside the HEARTS framework, biometric signals such as continuous ECG are gathered by means of wearable sensors. It has to be highlighted that developed technology can potentially be applied to a wide range of health related data. The developed wearable sensors features ECG monitoring through the use of up to 9 analog inputs per ECG in two modules, respiratory monitoring (respiratory waistband), auxiliary monitoring (body temperature or a second respiratory waistband), various analog and serial digital inputs for extern instrumentation. Other integrated sensors are the accelerometer ones, for environmental temperature and air pollution level measurement. ECG monitoring: - 1- 9 analog inputs per ECG (1-12 lead) in 2 modules (3 + 6 channels); - Signal -50 to 50 mV AC with -1V to 1V offset; - Impedance > 10 Mohm; - Analog Filter BP 0,1- 100Hz (30dB) and digital notch filter 50/60Hz (up to 100dB); - Digital configurable post-acquisition filter for de-noising and/or BandPass; - Acquisition 22bits 600Hz max; - Post digital processing data 12bit real - 500Hz (typical). Respiratory monitoring: - 1 analog input for waistband respiratory activity measurement (piezoelectric); - Resistive sensor input: with a current generator up to 200uA; - Voltage input piezoelectric sensor: maximum voltage 1,2Volt; - Acquisition up to 24 bit with programmable frequency (200Hz typical); - Digital notch filter (programmable) (typical 50Hz); Auxiliary monitoring (body temperature or second respiratory waistband) - 1 analog input for measurement (body temperature or second respiratory waistband); - Voltage input 1,2V max; - 24bits /200Hz max; - Digital notch filter (programmable). Optional input - 1 analog optional input, shared with internal measurement; - Voltage input 3V max; - 24 bit 200Hz; - 30Kohm input impedance (typical); - 3 serial digital inputs for extern independent instrumentation (Oximetry, AP, etc.); - up to 64Kbit/s Full Duplex (isolated 5 Volt). More information on the Hearts project can be found at: http://heartsproject.datamat.it/hearts
The HEARTS (Health Early Alarm Recognition and Telemonitoring System). Research, the current project and other future actions, wants to create a new Family of Tele-Health Systems being: - Non-invasive: information will be acquired through “wearable” sensors. - Dynamic: the systems will be able to continuously monitor information and about the health status and relate it to the specific context in which the person is acting (activity, environmental conditions, history), individually adapting its behaviour. - Intelligent: capable to offer dynamic intelligent alert functions directly to the citizen. - With advanced and adaptive decision support capability: integrating classical analysis techniques with new techniques and approaches, such as Neural Network Analysis and Non-Linear Analysis to reflect the specific health behaviour of each person. - Interoperable: services must be available for people moving in a trans-national context. - Based on an open architecture: to be enhanced or expanded with the simple addition or replacement of existing components with the new ones available in the market. - Focused on disease prediction rather that diagnosis: offering classical and innovative services not only to ill and high-risk people, but also healthy ones making for instance highly stressing jobs. More information on the Hearts project can be found at: http://heartsproject.datamat.it/hearts
The following items have been realised: Dual role Smart Card for both patients and health practitioners, Dedicated Smart Card Reader, PC attached, Set of software drivers and applications for the personalization (initial activation) and standard operation of the Smart Cards and Reader Smart Cards are equipped with microprocessors featuring cryptographic capabilities where the whole framework is security is based. Cryptography is of asymmetric type (Elliptic Curves Cryptosystem) and it is part of a Public Key Infrastructure. It is worth to briefly describe the role of the two different types of smart card, Health Professional Smart Card and Patient Smart Card: - The first one provides access to decision support system and access privileges according to specialty defined. Mainly it is used as an ID and consequently as a token of registration to HEARTS statistics archive. - The Patient Smart Card, instead, is mainly a static data carrier with some memory areas that can be upgraded according to patient's medical history, providing - in this way - a cross-sector interoperability by merging insurance and medical data. An important characteristics of the smart card approach within the central management system is the multilevel authentication function based on cryptography by using a Security Module (SM) installed in the Reader, verifying the Card Holder's ID by PIN insertion already encoded and digitally signed in the card's memory area. The data involved are both visual (such as photo, name) and encoded (as serial number, name, PIN, secret keys and others). More information on the HEARTS project can be found at: http://heartsproject.datamat.it/hearts.
HEARTS adopted two different ways to focus and analyse heart disease diagnosis: the disease-centric and the patient-centric approaches. Based on the concept of “adaptive behavioural analysis”, the decision support module analyses data through innovative neuro/fuzzy processing technologies, obtaining anytime information about health status related to the specific subject and the specific context. The main objective was to develop techniques for early detection of myocardial ischaemia starting from the ECG signal. This result has been achieved through the integration of the disease-centric and the patient centric approach, trying to give immediate warning to the users when there are signs of myocardial ischaemia, together with easy interpretability and reasoning capabilities. The architectural structure of the decision support subsystem is modular, hierarchical and aimed to make the process understandable to the medical experts. The so implemented components have much potential in the hospital or medical school settings. The patient-centric approach is based on the ECG beat recognition method and it’s based on a supervised neural network-based algorithm. Several experiments have been deployed both on data gathered during the validation and test phases and on the European ECG database. The results of experiments have shown the good efficiency of this second solution. Indeed, with the novel approach, the accuracy of beat classification for several patient records taken from European ECG database was over 94%. The investigation shows that the proposed beat classifier is very reliable, and hence it may be a useful practical tool for automatic detection of ischemic episodes. The added value given by the integration of the above approaches is furthermore significant, making the final integrated decision support module able to be very high for both sensitivity and specificity in the identification of ischemic beats (ST segment deviation) for the specific patient. The decision support modules developed by HEARTS can be used not only as part of the complete telemedicine system but also as a standalone module in the hospital or embedded in medical devices. They can also be integrated to other decision support systems in the hospital, especially in the operating theatre intensive care units and coronary care units where the patient’s ECG and other biomedical data are continuously monitored and the early detection of myocardial ischaemia is important. Good results have been achieved both on European ST-T database segments and on ECG segments collected during verification and validation phases. More information on the Hearts project can be found at: http://heartsproject.datamat.it/hearts.

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