Analysis of electrocardiogram pre-shock waveforms during ventricular fibrillation

Rasooli, M.; Foomany, Farbod Hosseyndoust; Balasundaram, Krishnanand; Massé, Stéphane; Zamiri, Nima; Ramadeen, Andrew; Hu, Xudong; Dorian, Paul; Nanthakumar, Kumaraswamy; Krishnan, Sridhar; Beheshti, Soosan; Umapathy, Karthikeyan

doi:10.1016/j.bspc.2015.05.003

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…The other feature that was explored in this thesis was the scale distribution width (SDW) [76]. The SDW is a wavelet feature, which captures the scale dependent energy profile of the scalogram.…”

Section: Scale Distribution Widthmentioning

confidence: 99%

Analysis of electrooculogram (EOG) signals in studying myasthenia gravis

Liang¹

2021

Preprint

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Myasthenia Gravis (MG) is a neuromuscular disorder that induces muscle weakness and fatigue which can be fatal. A common precursor for severe form of MG is ocular MG. In this thesis, we explored signal processing methodologies for early stage detection of MG using electrooculogram (EOG) signals. An EOG signal database consisting of 62 control and 16 MG (mild to moderate) subjects were analyzed for eye movement characteristics and EOG signal morphologies using time domain and wavelet domain techniques. A linear discriminant analysis (LDA) based classifier was used to quantify the ability of features in separating MG from control samples. Average overall classification accuracy achieved by the proposed method for the best time domain feature (average rise rate) and best wavelet feature (scale band energy) was 82.5% (P<0.01, AUC=0.887) and 83.8% (P<0.01, AUC=0.893), respectively. The obtained results suggest EOG based analysis is a viable, non-invasive alternative MG screening method.

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Section: Scale Distribution Widthmentioning

confidence: 99%

Analysis of electrooculogram (EOG) signals in studying myasthenia gravis

Liang¹

2021

Preprint

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“…Previous techniques have been used for decomposing VF signals into modal functions for the purpose of identifying underlying behaviours. In the work mentioned earlier by Rasooli et al [30], they decomposed VF ECG signals into independent sources (IS) using BSS. These ISs are distinguished from each other based on their independence, which was ensured by ICA.…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

Classification of Ventricular Arrhythmias Based on a Data Driven Approach

Hotradat¹

2021

Preprint

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Ventricular arrhythmias (VA) are dangerous pathophysiological conditions affecting the heart which evolve over time resulting in different manifestations such as ventricular tachycardia (VT), organized VF (OVF), and disorganized VF (DVF). Success of resuscitation for patients is greatly impacted by the type of VA and swift administration of appropriate therapy options. This thesis attempts to arrive at computationally efficient, data driven approaches for classifying and tracking VAs over time for two purposes: (1) ‘in-hospital’ scenarios for planning long-term therapy options, and (2) ‘out-of-hospital’ scenarios for tracking progression/segregation of VAs in near real-time. Using a database of 61 60-s ECG VA segments, maximum classification accuracies of 96.7% (AUC=0.993) and 87% (AUC=0.968) were achieved for VT vs. VF and OVF vs. DVF classification for ‘in-hospital’/offline analysis. Two near real-time approaches were also developed for ‘out-of-hospital’ VA incidents with results demonstrating the high potential to track VA progression and segregation over time.

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“…When we demonstrated the trend for SG, Yaman et al [34] indicated that the wavelet SG analysis would be preferred because of its capability of screening time, frequency, and energy concurrently. Rasooli et al [35] used SG for frequency and entropy-based feature and Acharya et al [36] used the resized (128 ×128 pixels) 2D-SG to improve performance. 3-D SG for feature extraction is used in Mourad and Fethi [37], Awal et al [38], Ziani et al [39], Sukiennik and Białasiewicz [40], Bukkapatnam et al [41], and Hagiwara et al [42].…”

Section: Introductionmentioning

confidence: 99%

Personal Identification Using a Robust Eigen ECG Network Based on Time-Frequency Representations of ECG Signals

Lee

Kwak

2019

IEEE Access

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This paper is concerned with personal identification using a robust EigenECG network (REECGNet) based on time-frequency representations of electrocardiogram (ECG) signals. For this purpose, we use a robust principal component analysis network (RPCANet) and wavelet analysis. In general, PCA performance and applicability in real case scenarios is limited by the lack of robustness to outliers and corrupted observations. However, in a real nonstationary ECG noise environment, RPCA shows good performance when the method is applied with variable dimensions of local signal subspaces. That is why RPCA-based ECG identification is extremely robust with nonlinear data. Also, a REECGNet performs well without back-propagation to obtain features from the visual content. We constructed a Chosun University ECG Database (CU-ECG DB) and compared with the Physikalisch-Technische Bundesanstalt ECG database (PTB-ECG DB), which is shared data. Finally, the experimental results show the advantages and effectiveness of the applied recognition scheme with 98.25% performance. In addition, to demonstrate the superiority of REECGNet, we experimented with adding noise and the experimental result showed 97.5% recognition rate. INDEX TERMS Robust EigenECG network, scalogram, ECG biometrics, person identification.

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Analysis of electrocardiogram pre-shock waveforms during ventricular fibrillation

Cited by 8 publications

References 28 publications

Analysis of electrooculogram (EOG) signals in studying myasthenia gravis

Analysis of electrooculogram (EOG) signals in studying myasthenia gravis

Classification of Ventricular Arrhythmias Based on a Data Driven Approach

Personal Identification Using a Robust Eigen ECG Network Based on Time-Frequency Representations of ECG Signals

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