Efficient and robust ventricular tachycardia and fibrillation detection method for wearable cardiac health monitoring devices

Prabhakararao, Eedara; Manikandan, M. Sabarimalai

doi:10.1049/htl.2016.0010

Cited by 13 publications

(3 citation statements)

References 31 publications

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“…Some of these patterns can be detected and become predictor signals for early warning. Early detection of VT/VF is also very essential for wearable devices for timely delivery of electric shock therapy [126]. Not only essential for timely delivery but also essential for accurately distinguishing between shockable and non-shockable arrhythmia [127].…”

Section: Comparative Resultsmentioning

confidence: 99%

Cardiac Arrhythmia Classification Using Wavelet Analysis of Electrocardiograms

Malhotra

2023

Preprint

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Cardiac Arrhythmias are heart rhythm abnormalities that seriously impact the quality of life and can be lethal. Four major types of cardiac arrhythmia that originate from atria and ventricles are atrial flutter (AFL), atrial fibrillation (AF), ventricular tachycardia (VT), and ventricular fibrillation (VF). Annually about 50,000 embolic strokes mostly due to AF and 35,000 sudden cardiac deaths mostly due to VF are reported in Canada. Accurate detection and segregation of these arrhythmia swiftly is an essential requirement for appropriate treatment. Automating this process is especially critical and valuable for implantable devices and long-term monitoring scenarios. In this thesis, with the above motivation, we analyzed the electrocardiograms (ECGs) recorded during these 4 types of arrhythmia using wavelet transform. A total of 100 ECG segments con- taining 25 ECG segments each for AF, AFL, VT and VF, obtained from well-known open source databases were used in this study. Discriminative features were extracted from the wavelet coefficients and fed to a linear discriminant analysis based classifier. Based on the proposed scheme, best classification accuracies using library wavelets and adaptive continuous wavelets (ACW) are as follows: (i) for four group classification, Paul CW and A-pattern ACW attained 77% and 81% respectively, (ii) for the two group classification of AA, Paul CW and M-pattern ACW attained 76% and 86% respectively, (iii) for the two group classification of VA, Bump CW and M-pattern ACW attained 92% and 94% respectively.

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Section: Comparative Resultsmentioning

confidence: 99%

Cardiac Arrhythmia Classification Using Wavelet Analysis of Electrocardiograms

Malhotra

2023

Preprint

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“…, p ). The main objective is to find a model for predicting the values of Y i from new X values [43].…”

Section: Weighted Random Forestmentioning

confidence: 99%

Weighted Random Forests to Improve Arrhythmia Classification

et al. 2020

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Construction of an ensemble model is a process of combining many diverse base predictive learners. It arises questions of how to weight each model and how to tune the parameters of the weighting process. The most straightforward approach is simply to average the base models. However, numerous studies have shown that a weighted ensemble can provide superior prediction results to a simple average of models. The main goals of this article are to propose a new weighting algorithm applicable for each tree in the Random Forest model and the comprehensive examination of the optimal parameter tuning. Importantly, the approach is motivated by its flexibility, good performance, stability, and resistance to overfitting. The proposed scheme is examined and evaluated on the Physionet/Computing in Cardiology Challenge 2015 data set. It consists of signals (electrocardiograms and pulsatory waveforms) from intensive care patients which triggered an alarm for five cardiac arrhythmia types (Asystole, Bradycardia, Tachycardia, Ventricular Tachycardia, and Ventricular Fultter/Fibrillation). The classification problem regards whether the alarm should or should not have been generated. It was proved that the proposed weighting approach improved classification accuracy for the three most challenging out of the five investigated arrhythmias comparing to the standard Random Forest model.

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“…Hence, in analysing such signals, the algorithm cannot rely solely on heart rate. The methods already proposed for detection of ventricular tachycardia include flutter/fibrillation approaches like autocorrelation analysis [11], wavelet transformations [12,13], sample entropy [14], machine learning methods with features derived from signal morphology and analysis of power spectrum [15], time-frequency representation images [16], empirical mode decomposition [17], or using the zero crossing rate combined with base noise suppression with discrete cosine transform and beat-to-beat intervals [18].…”

Section: Related Workmentioning

confidence: 99%

Reducing False Arrhythmia Alarms Using Different Methods of Probability and Class Assignment in Random Forest Learning Methods

Gajowniczek

Grzegorczyk

Ząbkowski

2019

Sensors

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The literature indicates that 90% of clinical alarms in intensive care units might be false. This high percentage negatively impacts both patients and clinical staff. In patients, false alarms significantly increase stress levels, which is especially dangerous for cardiac patients. In clinical staff, alarm overload might lead to desensitization and could result in true alarms being ignored. In this work, we applied the random forest method to reduce false arrhythmia alarms and specifically explored different methods of probability and class assignment, as these affect the classification accuracy of the ensemble classifiers. Due to the complex nature of the problem, i.e., five types of arrhythmia and several methods to determine probability and the alarm class, a synthetic measure based on the ranks was proposed. The novelty of this contribution is the design of a synthetic measure that helps to leverage classification results in an ensemble model that indicates a decision path leading to the best result in terms of the area under the curve (AUC) measure or the global accuracy (score). The results of the research are promising. The best performance in terms of the AUC was 100% accuracy for extreme tachycardia, whereas the poorest results were for ventricular tachycardia at 87%. Similarly, in terms of the accuracy, the best results were observed for extreme tachycardia (91%), whereas ventricular tachycardia alarms were the most difficult to detect, with an accuracy of only 51%.

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Efficient and robust ventricular tachycardia and fibrillation detection method for wearable cardiac health monitoring devices

Cited by 13 publications

References 31 publications

Cardiac Arrhythmia Classification Using Wavelet Analysis of Electrocardiograms

Cardiac Arrhythmia Classification Using Wavelet Analysis of Electrocardiograms

Weighted Random Forests to Improve Arrhythmia Classification

Reducing False Arrhythmia Alarms Using Different Methods of Probability and Class Assignment in Random Forest Learning Methods

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