ECG Parameters for Malignant Ventricular Arrhythmias: A Comprehensive Review

Mandala, Satria; Di, Tham Cai

doi:10.1007/s40846-017-0281-x

Cited by 47 publications

(34 citation statements)

References 52 publications

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“…The second and perhaps more important drawback of the existing ECG classification systems is their incapability of providing predictive alarms before the occurrence of abnormalities. Nevertheless, it has been proven by researchers that certain features of ECG signals reflect underlying cardiac abnormalities before the occurrences of cardiac disease [4,5]. Due to the large variety of ECG parameters, the selection of optimal predictor set is a demanding task if performed manually.…”

Section: Ecg-based Heart Monitoringmentioning

confidence: 99%

“…Indeed, some disorders are diagnosed only after they are so severe that leave the patients with little time to take therapeutic actions [3]. An early detection of abnormal cardiac behaviors through ECG signals prior to occurrences of CVD can save lives by enabling timely preventive actions [4,5]. This is an important issue noting that sudden cardiac death (SCD), which often associates with no obvious signs and symptoms, accounts for 250, 000 to 300, 000 mortality per year in the US [6].…”

Section: Introductionmentioning

confidence: 99%

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A Deviation Analysis Framework for ECG Signals Using Controlled Spatial Transformation

Chen

Valehi

Afghah

et al. 2019

2019 IEEE EMBS International Conference on Biomedical &Amp; Health Informatics (BHI)

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An important paradigm in smart health is developing diagnosis tools and monitoring a patient's heart activity through processing Electrocardiogram (ECG) signals is a key example, sue to high mortality rate of heart-related disease. However, current heart monitoring devices suffer from two important drawbacks: i) failure in capturing inter-patient variability, and ii) incapability of identifying heart abnormalities ahead of time to take effective preventive and therapeutic interventions.This paper proposed a novel predictive signal processing method to solve these issues. We propose a two-step classification framework for ECG signals, where a global classifier recognizes severe abnormalities by comparing the signal against a universal reference model. The seemingly normal signals are then passed through a personalized classifier, to recognize mild but informative signal morphology distortions. The idea is to develop a patient-specific reference for normal heart function. Another key contribution is developing a novel deviation analysis based on a controlled nonlinear transformation (with two computational and analytical optimization methods) to capture significant deviations of the signal towards any of predefined abnormality classes. Here, we embrace the proven but overlooked fact that certain features of ECG signals reflect underlying cardiac abnormalities before the occurrences of cardiac disease. The proposed method achieves a classification accuracy of 96.6% and provides a unique feature of predictive analysis by providing warnings before critical heart conditions. In particular, the chance of observing a severe problem (in terms of a red alarm) is raised by about 5% to 10% after observing a yellow alarm of the same type. Although we used this methodology to provide early precaution messages to elderly and high-risk heart-patients, the proposed method is general and applicable to similar bio-medical signal processing applications.

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Section: Ecg-based Heart Monitoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deviation Analysis Framework for ECG Signals Using Controlled Spatial Transformation

Chen

Valehi

Afghah

et al. 2019

2019 IEEE EMBS International Conference on Biomedical &Amp; Health Informatics (BHI)

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“…Recently, many researchers have revealed that the ECG characteristic features may reflect the underlying VTAs and have predictive value for the occurrence of VTAs event through the follow-up studies [20][21][22][23][24]. They offer the advantages of the availability of the long duration and high resolution ECG recordings prior to VTAs event that enable the possibility to investigate the mechanism that is responsible for the occurrence of VTAs.…”

Section: Prognostic Values Of Ecg Featuresmentioning

confidence: 99%

“…As a result, this section presents the review of the identified ECG characteristic features which act as important input features of the short-term VTAs prediction based on machine learning. The identified ECG parameters that have prognostic value in predicting the occurrence of VTAs are, namely: QT interval, QT interval corrected with heart rate, QT dispersion, QT dynamicity, T-wave alternans and heart rate variability [24]. Each prognostic feature is further elaborated in detail in following subsection.…”

Section: Prognostic Values Of Ecg Featuresmentioning

confidence: 99%

Ventricular Tachyarrhythmias Prediction Methods and its Prognostic Features: A Review

Chieng¹,

Hau²,

Omar³

et al. 2019

IJCDS

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This paper presents a literature review of ventricular tachyarrhythmias (VTAs) prediction methods and its prognostic features, as well as highlights the severity of the cardiovascular diseases in general population. This article provides the collective review of the short-term VTAs prediction based on the machine learning methods associated with the potential prognostics electrocardiogram (ECG) characteristics features that have been proposed in the recent literature. The basic morphology of the ECG waveform and its working principle is also briefly described for better understanding of the relationship between the ECG characteristics features and the occurrence of VTAs. In addition, the trend and future direction in the development of VTAs prediction system with machine learning are presented as well. It is desired that the progressive development of real-time, low computational cost and reliable short-term VTAs prediction algorithm in coming years could decrease the mortality rate of cardiovascular diseases within general populations. This article can be adopted as an initial idea and guidelines for beginners in this field to initiate their research.

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“…Correct VLP detection is actually considered a helpful and non-invasive diagnostic tool in patients with arrhythmogenic right ventricular cardiomyopathy for preventing malignant ventricular arrhythmias such as ventricular tachycardia and ventricular fibrillation. In fact, the prediction of ventricular tachycardia/ventricular fibrillation before their occurrence is essential to prevent delay of rescue procedures [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

2019

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The analysis of cardiac signals is still regarded as attractive by both the academic community and industry because it helps physicians in detecting abnormalities and improving the diagnosis and therapy of diseases. Electrocardiographic signal processing for detecting irregularities related to the occurrence of low-amplitude waveforms inside the cardiac signal has a considerable workload as cardiac signals are heavily contaminated by noise and other artifacts. This paper presents an effective approach for the detection of ventricular late potential occurrences which are considered as markers of sudden cardiac death risk. Three stages characterize the implemented method which performs a beat-to-beat processing of high-resolution electrocardiograms (HR-ECG). Fifteen lead HR-ECG signals are filtered and denoised for the improvement of signal-to-noise ratio. Five features were then extracted and used as inputs of a classifier based on a machine learning approach. For the performance evaluation of the proposed method, a HR-ECG database consisting of real ventricular late potential (VLP)-negative and semi-simulated VLP-positive patterns was used. Experimental results show that the implemented system reaches satisfactory performance in terms of sensitivity, specificity accuracy, and positive predictivity; in fact, the respective values equal to 98.33%, 98.36%, 98.35%, and 98.52% were achieved.

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ECG Parameters for Malignant Ventricular Arrhythmias: A Comprehensive Review

Cited by 47 publications

References 52 publications

A Deviation Analysis Framework for ECG Signals Using Controlled Spatial Transformation

A Deviation Analysis Framework for ECG Signals Using Controlled Spatial Transformation

Ventricular Tachyarrhythmias Prediction Methods and its Prognostic Features: A Review

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

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