Quantification of Diagnostic Information from Electrocardiogram Signal: A Review

Dandapat, Samarendra; Sharma, L. N.; Tripathy, Rajesh Kumar

doi:10.1007/978-81-322-2464-8_2

Cited by 13 publications

(23 citation statements)

References 43 publications

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“…The second subjective method is based on the difference in diagnosis from the original and compressed ECG [ 32 , 34 ]. However, the gold standard of the methods for the evaluation of the ECG signal quality after compression is the Mean Opinion Score (MOS) test [ 2 , 3 , 28 , 31 , 35 , 36 ]. Its output is MOS error , which informs us directly about the diagnostic distortion (in %).…”

Section: Known Methods For Evaluation Of the Ecg Signal Quality Afmentioning

confidence: 99%

“…For example, if we want to determine heart rate only, we can do this from a signal with lower quality and can therefore compress the signal much more than in case of, for example, tracking ST segment changes. Generally speaking, the quality of the signal after compression should be quantified to decide whether the signal is appropriate for further specific analysis or not [ 2 ]. For this purpose, it is advantageous to use automatized methods.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression

Němcová

Smíšek

Maršánová

et al. 2018

BioMed Research International

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The assessment of ECG signal quality after compression is an essential part of the compression process. Compression facilitates the signal archiving, speeds up signal transmission, and reduces the energy consumption. Conversely, lossy compression distorts the signals. Therefore, it is necessary to express the compression performance through both compression efficiency and signal quality. This paper provides an overview of objective algorithms for the assessment of both ECG signal quality after compression and compression efficiency. In this area, there is a lack of standardization, and there is no extensive review as such. 40 methods were tested in terms of their suitability for quality assessment. For this purpose, the whole CSE database was used. The tested signals were compressed using an algorithm based on SPIHT with varying efficiency. As a reference, compressed signals were manually assessed by two experts and classified into three quality groups. Owing to the experts' classification, we determined corresponding ranges of selected quality evaluation methods' values. The suitability of the methods for quality assessment was evaluated based on five criteria. For the assessment of ECG signal quality after compression, we recommend using a combination of these methods: PSim SDNN, QS, SNR1, MSE, PRDN1, MAX, STDERR, and WEDD SWT.

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Section: Known Methods For Evaluation Of the Ecg Signal Quality Afmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression

Němcová

Smíšek

Maršánová

et al. 2018

BioMed Research International

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“…The SBSE and SDFC features are evaluated from these sub-band signal. During pathology, the duration and shape of clinical components are different [6]. The SDFC and SBSE features capture these variations by exploiting long range correlations and irregularity.…”

Section: Resultsmentioning

confidence: 99%

“…Multilead ECG is considered the gold standard in detection of life-threatening heart pathologies [6]. The diagnostic features of multilead ECG deviate from the normal sinus rhythm during MI, VHT and CM.…”

Section: Introductionmentioning

confidence: 99%

“…The multiscale energy and eigen space (MEES) features have an advantage in classifying myocardial infarctions based on the intra-beat and inter-lead variations [12]. Non-linear analysis is widely used to estimate irregularity and longrange correlations in physiological signal [6], [13]. The sub-band wavelet coefficients of multilead ECG capture the local variation of the diagnostic features.…”

Section: Introductionmentioning

confidence: 99%

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Cardiac arrhythmia classification from multilead ECG using multiscale non-linear analysis

Chetan

Tripathy

Dandapat

2015

2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON)

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In this paper, a novel technique is proposed for detecting cardiac arrhythmias using signals obtained from a multi-lead electrocardiogram (ECG). The method employs the use of two non-linear features namely detrended fluctuation analysis and sample entropy. The features are calculated on signals obtained after performing discrete wavelet transform on the incoming raw ECG data and selecting the diagnostically relevant sub-bands. The DFC and SE features of the subband signal are computed and the performance of these features is evaluated using multilayer perceptron (MLP), radial basis function neural network (RBFNN) and probabilistic neural network (PNN) classifiers. The experimental result shows that, the combination of DFC and SE features along-with MLP classifier has a high accuracy value of 98.76%.

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Detection of Shockable Ventricular Arrhythmia using Variational Mode Decomposition

2016

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Ventricular tachycardia (VT) and ventricular fibrillation (VF) are shockable ventricular cardiac ailments. Detection of VT/VF is one of the important step in both automated external defibrillator (AED) and implantable cardioverter defibrillator (ICD) therapy. In this paper, we propose a new method for detection and classification of shockable ventricular arrhythmia (VT/VF) and non-shockable ventricular arrhythmia (normal sinus rhythm, ventricular bigeminy, ventricular ectopic beats, and ventricular escape rhythm) episodes from Electrocardiogram (ECG) signal. The variational mode decomposition (VMD) is used to decompose the ECG signal into number of modes or sub-signals. The energy, the renyi entropy and the permutation entropy of first three modes are evaluated and these values are used as diagnostic features. The mutual information based feature scoring is employed to select optimal set of diagnostic features. The performance of the diagnostic features is evaluated using random forest (RF) classifier. Experimental results reveal that, the feature subset derived from mutual information based scoring and the RF classifier produces accuracy, sensitivity and specificity values of 97.23 %, 96.54 %, and 97.97 %, respectively. The proposed method is compared with some of the existing techniques for detection of shockable ventricular arrhythmia episodes from ECG.

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Quantification of Diagnostic Information from Electrocardiogram Signal: A Review

Cited by 13 publications

References 43 publications

A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression

A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression

Cardiac arrhythmia classification from multilead ECG using multiscale non-linear analysis

Detection of Shockable Ventricular Arrhythmia using Variational Mode Decomposition

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