A real-time algorithm for ECG diagnostics is considered as a clinical decision support software (CDSS) and is usually designed to provide with important clinical decision support in various areas of ECG applications. In this work, a full-automatic and high reliable ECG analysis system in real-time is presented. It can provide relevant important ECG biomarkers and values for many ECG application including stress ECG, cardiac drug safety and monitoring ECG, including home-monitoring, ambulatory monitoring and bed-side monitoring. The algorithms employed in this system perform reliable quality-assessment of ECG signals, robust detection of the QRS complexes based on time-frequency analysis , highly precise classification of QRS complex based on machine-learning methodologies , highly accurate full-scale beat-to-beat delineation of heart beats , as well as numerous statements and measures for ECG rhythm analysis and heart-rate variability, respectively. The system can handle either single-channel and multi-channel ECG lead systems. The ECG signals can be streamed in and feed into the algorithms from any ECG acquisition hardware unit. A superior performance with a sensitivity and specificity over than 99 % has been observed by the beat detection and classification algorithms using MIT-BIH Arrhythmia Database, MIT-BIH Noise Stress Test Database, AHA series1 and AHA series2. Furthermore, all relevant requirements of the standard 60601-2-25 have been fulfilled by the delineation algorithm . Due to its high degree of performance and accuracy, the system can be used in critical cardiac applications including cardiac drug safety minimizing the hazard and risk to patients and increasing the cardiac safety and security in total. Moreover, statements about ischemia and myocardial infarction can be provided in real-time by using the validated diagnostic algorithm as an add-on to this system .
 ISSN: 2325-887X DOI: 10.22489/CinC.2018.229
 ISSN: 2325-887X DOI: 10.22489/CinC.2018.209