Real-time detection of pathological cardiac events in the electrocardiogram

Abstract

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.

Online Digital Filter and QRS Detector Applicable in Low Resource ECG Monitoring Systems

“…It is again designed with considerations of simplified computations applied over single channel, low-resolution ECGs. Although the QRS detector has adopted some elements from our previous QRS detector, 12 it embodies additional smart details, leading to improvements of its performance with about 0.5% (Se from 99.01% becomes 99.65%, PPV from 99.11% becomes 99.57%- Table 2). The present version of the QRS detector, relies on dynamically updated amplitude/slope thresholds, history of mean amplitudes/slopes calculated over the past beats, and linear logical rules for identification of the significant R-peak.…”

“…In our previous work, 12 we described a pilot version of a real-time QRS detector based on the general concept for dynamic update of amplitude and slope thresholds, investigated previously by Dotsinsky and Stoyanov,8 and Christov. 5 In this work, the QRS detector have been elaborated which considerably improved its performance.…”

Identification of Premature Ventricular Contraction in ECG Signals – A Review

“…This algorithm proved effective in accurate peak detection under noisy conditions but at the cost of computational complexity. Ivo Iliev 3 et al devoted his work for development of a QRS detector with less memory requirement to identify the actual QRS peak position from the deviated one. …”

Wearable ECG System for Health and Sports Monitoring

“…The second filter is a low pass filter aimed to remove the high frequency noise caused from motion artifacts. The QRS detection algorithm is based upon digital analyses of slope, amplitude, and width [9,10]. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate.…”