Computer Aided ECG Analysis - State of the Art and Upcoming Challenges

In this paper we present current achievements in computer aided ECG analysis and their applicability in real world medical diagnosis process. Most of the current work is covering problems of removing noise, detecting heartbeats and rhythm-based analysis. There are some advancements in particular ECG segments detection and beat classifications but with limited evaluations and without clinical approvals. This paper presents state of the art advancements in those areas till present day. Besides this short computer science and signal processing literature review, paper covers future challenges regarding the ECG signal morphology analysis deriving from the medical literature review. Paper is concluded with identified gaps in current advancements and testing, upcoming challenges for future research and a bullseye test is suggested for morphology analysis evaluation.Comment: 7 pages, 3 figures, IEEE EUROCON 2013 International conference on computer as a tool, 1-4 July 2013, Zagreb, Croati

Novel hybrid extraction systems for fetal heart rate variability monitoring based on non-invasive fetal electrocardiogram

This study focuses on the design, implementation and subsequent verification of a new type of hybrid extraction system for noninvasive fetal electrocardiogram (NI-fECG) processing. The system designed combines the advantages of individual adaptive and non-adaptive algorithms. The pilot study reviews two innovative hybrid systems called ICA-ANFIS-WT and ICA-RLS-WT. This is a combination of independent component analysis (ICA), adaptive neuro-fuzzy inference system (ANFIS) algorithm or recursive least squares (RLS) algorithm and wavelet transform (WT) algorithm. The study was conducted on clinical practice data (extended ADFECGDB database and Physionet Challenge 2013 database) from the perspective of non-invasive fetal heart rate variability monitoring based on the determination of the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and harmonic mean between SE and PPV (F1). System functionality was verified against a relevant reference obtained by an invasive way using a scalp electrode (ADFECGDB database), or relevant reference obtained by annotations (Physionet Challenge 2013 database). The study showed that ICA-RLS-WT hybrid system achieve better results than ICA-ANFIS-WT. During experiment on ADFECGDB database, the ICA-RLS-WT hybrid system reached ACC > 80 % on 9 recordings out of 12 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 6 recordings out of 12. During experiment on Physionet Challenge 2013 database the ICA-RLS-WT hybrid system reached ACC > 80 % on 13 recordings out of 25 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 7 recordings out of 25. Both hybrid systems achieve provably better results than the individual algorithms tested in previous studies.Web of Science713178413175

Hybrid methods based on empirical mode decomposition for non-invasive fetal heart rate monitoring

This study focuses on fetal electrocardiogram (fECG) processing using hybrid methods that combine two or more individual methods. Combinations of independent component analysis (ICA), wavelet transform (WT), recursive least squares (RLS), and empirical mode decomposition (EMD) were used to create the individual hybrid methods. Following four hybrid methods were compared and evaluated in this study: ICA-EMD, ICA-EMD-WT, EMD-WT, and ICA-RLS-EMD. The methods were tested on two databases, the ADFECGDB database and the PhysioNet Challenge 2013 database. Extraction evaluation is based on fetal heart rate (fHR) determination. Statistical evaluation is based on determination of correct detection (ACC), sensitivity (Se), positive predictive value (PPV), and harmonic mean between Se and PPV (F1). In this study, the best results were achieved by means of the ICA-RLS-EMD hybrid method, which achieved accuracy(ACC) > 80% at 9 out of 12 recordings when tested on the ADFECGDB database, reaching an average value of ACC > 84%, Se > 87%, PPV > 92%, and F1 > 90%. When tested on the Physionet Challenge 2013 database, ACC > 80% was achieved at 12 out of 25 recordings with an average value of ACC > 64%, Se > 69%, PPV > 79%, and F1 > 72%.Web of Science8512185120

Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems

Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls short of the performance attained by state-of-the-art wavelet based algorithms. In this paper, we propose to exploit the structure of the wavelet representation of the ECG signal to boost the performance of CS-based methods for compression and reconstruction of ECG signals. More precisely, we incorporate prior information about the wavelet dependencies across scales into the reconstruction algorithms and exploit the high fraction of common support of the wavelet coefficients of consecutive ECG segments. Experimental results utilizing the MIT-BIH Arrhythmia Database show that significant performance gains, in terms of compression rate and reconstruction quality, can be obtained by the proposed algorithms compared to current CS-based methods.Comment: Accepted for publication at IEEE Journal of Biomedical and Health Informatic