A robust QRS complex detection using regular grammar and deterministic automata. (February 2018)

• Record Type: Journal Article
• Authors: Hamdi, Salah; Abdallah, Asma Ben; Bedoui, Mohamed Hedi
• Is Part Of: Biomedical signal processing and control. Volume 40(2018)
• Page Start: 263
• Page End: 274
• Publication Date: 2018-02
• Subjects: DFA -- Grammar -- ECG -- QRS -- R peak -- RR -- σRR -- σQRS; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.09.032
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A novel grammar-based approach for ECG signals analysis is proposed. Deterministic automata with the addition of some requirements for the extraction of QRS complexes. The method is applied on the standard MIT-BIH arrhythmia database. The proposed method provides competitive results. Two metrics are added to quantify the RR distances and the QRS durations regularity. Abstract: A novel approach is proposed for medical analysis and clinical decision support of the Electrocardiogram (ECG) signals based on the deterministic finite automata (DFA) with the addition of some requirements. This paper proves regular grammar is effective in the extraction of QRS complex and interpretation of ECG signals. The DFA will be used to represent a normalized QRS complex as a sequence of negative and positive peaks. A QRS is considered as a set of adjacent peaks that satisfy certain criteria of standard deviation and duration. The proposed method is applied on several kinds of ECG signals collected from the standard MIT-BIH arrhythmia database. Several metrics are calculated including QRS durations, RR distances and peak amplitudes. Furthermore, σRR and σQRS metrics were added to quantify RR distances regularity and QRS durations, respectively. Regular grammar with the addition of some requirements and deterministic automata proved functional for both biomedical signals and ECG signal diagnosis. The suggested method provided a sensitivity rate of 99.74% and the positive predictivity rate of 99.86%. The algorithm was compared to other works in the literature and the quality performance detection was compared with several algorithms tested and validated on the MIT-BIH database. A head-to-head comparison in terms of sensitivity and CPU runtime was provided with the wavelet method.