Serafim Dimitrov Tabakov

Real-time detection of pathological cardiac events in the electrocardiogram.

The development of accurate and fast methods for real-time electrocardiogram (ECG) analysis is mandatory in handheld fully automated monitoring devices for high-risk cardiac patients. The present work describes a simple software method for fast detection of pathological cardiac events. It implements real-time procedures for QRS detection, interbeat RR-intervals analysis, QRS waveform evaluation and a decision-tree beat classifier. Two QRS descriptors are defined to assess (i) the RR interval deviation from the mean RR interval and (ii) the QRS waveform deviation from the QRS pattern of the sustained rhythm. The calculation of the second parameter requires a specific technique, in order to satisfy the demand for straight signal processing with minimum iterations and small memory size. This technique includes fast and resource efficient estimation of a histogram matrix, which accumulates dynamically the amplitude-temporal distribution of the successive QRS pattern waveforms. The pilot version of the method is developed in Matlab and it is tested with internationally recognized ECG databases. The assessment of the online single lead QRS detector showed sensitivity and positive predictivity of above 99%. The classification rules for detection of pathological ventricular beats were defined empirically by statistical analysis. The attained specificity and sensitivity are about 99.5% and 95.7% for all databases and about 99.81% and 98.87% for the noise free dataset. The method is applicable in low computational cost systems for long-term ECG monitoring, such as intelligent holters, automatic event/alarm recorders or personal devices with intermittent wireless data transfer to a central terminal.

Multipoint video control and fall detection system applicable in assistance of the elderly and people with disabilities

The population of elderly people keeps increasing rapidly, which becomes a predominant aspect of our societies. Ambient assisted living (AAL) is new research area which promises to address the needs of the elderly and people with disabilities. AAL has a lot of barriers and challenges that impede its development and more universal deployment. These could be summarised as: technological, ethical, legal and psychological challenges. An approach allowing overcoming part of these barriers, mostly related to the integration of a video control system in the assistive process, is presented by the authors in this paper.

LabVIEW based ECG signal acquisition and analysis

This paper presents a LabVIEW based system of acquisition, processing and analysis of ECG (electrocardiogram) signals. Biomedical signal acquisition has made great advances in recent years due to the introduction of modern hardware and software technologies. Computer based signal processing systems are becoming an efficient approach for acquisition and analyzing of such signals. In our implementation an AD8232 integrated signal conditioning front end of Analog Devices is used for measurement and preprocessing of the ECG signals. Battery powered data acquisition system, which avoids 50Hz noise and allows for safety measurement without additional isolation is employed. LabVIEW Biomedical Toolkit time-domain analysis is applied to study heart rate variability together with additional parameters.