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G Passariello

Publications and source records attributed to G Passariello.

7 recordsLinked to original sources
PubMed1999

Multisensor fusion for atrial and ventricular activity detection in coronary care monitoring.

Information management for critical care monitoring is still a very difficult task. Medical staff is often overwhelmed by the amount of data provided by the increased number of specific monitoring devices and instrumentation, and the lack of an effective automated system. Specifically, a basic task such as arrhythmia detection still produce an important amount of undesirable alarms, due in part to the mechanistic approach of current monitoring systems. In this work, multisensor and multisource data fusion schemes to improve atrial and ventricular activity detection in critical care environments are presented. Applications of these schemes are quantitatively evaluated and compared with current methods, showing the potential advantages of data fusion techniques for event detection in noise corrupted signals.

Coronary Care Units↗
PubMed1999

[High resolution ECG signals in Chagas patients: SEARCH project].

This work presents contributions to the study of a public health problem known as Chagasic Myocarditis. The results of the efforts done in Venezuela to understand the evolution of this disease through a novel technique called High Resolution Electrocardiography (HRECG) are discussed. A review of this methodology is presented and also its potential as a tool to study Chagas disease and to diagnose its different myocardial manifestations is discussed in detail. Several research approaches are presented as well as the results obtained, new techniques for HRECG interpretation such as the analysis of signals from the P-R segment, the intra QRS potentials, and late potentials. This method contributes to early detection and follow up of Chagas myocarditis. The collection and the organization of a HRECG data base that served as the basis for SEARCH, which is a valuable resource for new research lines is also described.

Arrhythmias, Cardiac↗
PubMed1995

An approach to intelligent ischaemia monitoring.

The paper describes an approach to intelligent ischaemia event detection based on ECG ST-T segment analysis. ST-T trends are processed by means of a Bayesian forecasting approach using the multistate Kalman filter. A complete procedure, intended for use in CCU/ICU monitoring areas, is proposed, in order to give the clinician an intelligent monitoring tool. The approach serves to describe trends and their changes in a symbolic way. A novel aspect is its ability to observe certain features of ST-T elevation/depression not detected by other means, and to reject artefacts and erroneous events. A sensitivity of 89.58% and a predictivity of 84.31% are obtained on selected records of the European ST-T database. Using a restriction on event amplitude, the predictivity is raised to 95.55%. An ischaemia sensitivity index of 1.2 was determined. The method has been shown to be a robust and practical trend analysis tool, and seems to be appropriate for numeric/symbolic transformations in next-generation intelligent monitoring systems.

Bayes Theorem↗
PubMed1991

Knowledge-based approach to the management of serious arrhythmia in the CCU.

An expert system (SETA) for the management of patients in the CCU environment has been developed. SETA suggests therapeutic actions for the treatment of serious arrhythmias that complicate the pathophysiological state of patients recovering from acute and suspected myocardial infarction. The prototype begins by reasoning from the arrhythmia, diagnosed from the ECG signal, and progresses through its inference process by considering ECG changes (i.e. heart rate, QRS width), patient clinical data, patient history and therapeutic drug data, to reach the most appropriate actions for each particular patient. The system was implemented on production rules using M1 as a development tool. SETA uses a multiknowledge base (KB) architecture, one for each particular arrhythmia and relevant complication, and a decision board that controls the firing of the KBs and keeps track of patient status through time. The system takes into consideration aspects that are very important for the human expert, e.g. sequence of arrhythmia appearance, drug contraindications and priority in the case of simultaneous arrhythmia. The development of this system has given insight into the management of critical CCU patients, that should influence the specification and design of intelligent instruments for this clinical environment.

Arrhythmias, Cardiac↗
PubMedDate not supplied

Microcomputer-based coronary care unit central station.

A four-bed central station that can be connected to any commercial intensive-care bedside monitor was developed. The system is based on a personal computer (IBM-AT compatible) as a local unit and on a microcontroller Intel 8031 as a remote unit. Four ECG signals are low-pass filtered, multiplexed, sampled at 256-Hz per channel, 8-bit A/D converted, preprocessed, and converted to a serial format RS-232 by the remote unit. The real-time display of the signals is at the standard speed of 25 and 50 mm/sec. Heartrate, alarms, trend plots, and general patient data are shown on an Olivetti M280 and EGA 13'' color monitor as the local unit. The communication speed was set at 57.6 Kbaud full duplex. Additionally, to reach standard monitoring sweep rates using a 13'' screen with 640 x 350 pixels, an ECG data-compression algorithm was implemented in the remote unit. This unit can support up to eight input channels and can work with any personal computer, via RS-232, with the appropriate software. It also allows other signal preprocessing software that could be developed, such as QRS detection or ST segment quantification, to be loaded into its random access memory and to be run under PC command. The development of this system demonstrated the use of a widespread piece of commercial equipment, the PC, in a very specific application, CCU monitoring, assuring low-cost system implementation. This feature is particularly attractive in upgrading existing CCU units in less developed countries.

Biomedical Engineering↗