Prediction of Life-Threatening Heart Arrhythmias Using Obstructive Sleep Apnoea Characteristics

Alinejad, Ghazaleh Mohammad; Rasoulinezhad, S A; Shamsollahi, Mohammad Bagher

doi:10.1109/iraniancee.2019.8786614

Cited by 5 publications

(6 citation statements)

References 14 publications

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“…Life-threatening arrhythmias are a common symptom in patients with CI and MI, and the common life-threatening arrhythmias include EB, ET, VT, and VF (Deaconu et al, 2021), and the definitions of those four life-threatening arrhythmias are given in Table 1 according to the beating rhythm of the heart rate (Alinejad et al, 2019;Paliakaitė et al, 2019). In the initial period of suddenness of lifethreatening arrhythmias, patients sometimes experience sudden heart pain that is slight and rapid and disappears after a short rest such as sitting or lying down, which is called "transient" (Chorin et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree

et al. 2022

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Extreme bradycardia (EB), extreme tachycardia (ET), ventricular tachycardia (VT), and ventricular flutter (VF) are the four types of life-threatening arrhythmias, which are symptoms of cardiovascular diseases. Therefore, in this study, a method of life-threatening arrhythmia recognition is proposed based on pulse rate variability (PRV). First, noise and interference are wiped out from the arterial blood pressure (ABP), and the PRV signal is extracted. Then, 19 features are extracted from the PRV signal, and 15 features with highly important and significant variation were selected by random forest (RF). Finally, the back-propagation neural network (BPNN), extreme learning machine (ELM), and decision tree (DT) are used to build, train, and test classifiers to detect life-threatening arrhythmias. The experimental data are obtained from the MIMIC/Fantasia and the 2015 Physiology Net/CinC Challenge databases. The experimental results show that the DT classifier has the best average performance with accuracy and kappa coefficient (kappa) of 98.76 ± 0.08% and 97.59 ± 0.15%, which are higher than those of the BPNN (accuracy = 94.85 ± 1.33% and kappa = 89.95 ± 2.62%) and ELM (accuracy = 95.05 ± 0.14% and kappa = 90.28 ± 0.28%) classifiers. The proposed method shows better performance in identifying four life-threatening arrhythmias compared to existing methods and has potential to be used for home monitoring of patients with life-threatening arrhythmias.

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Section: Introductionmentioning

confidence: 99%

A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree

et al. 2022

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“…The performance parameters, i.e., sensitivity (Se) and positive prediction (+P), are summarized in Table V [35]. The obtained performance parameters prove that the possibility of failure is within tolerable limits for the application targeted [36], [37], [38], [39].…”

Section: Resultsmentioning

confidence: 80%

A Fully Analog Autonomous QRS Complex Detection and Low-Complexity Asystole, Extreme Bradycardia, and Tachycardia Classification System

Jha

Rajendran

Lenka

et al. 2022

IEEE Trans. Instrum. Meas.

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This article presents a power-efficient fully analog classifier architecture for the detection of critical cardiac abnormalities, i.e., asystole, extreme bradycardia, and tachycardia. To reduce power consumption and hardware complexity, an analog QRS complex detection circuit and arithmetic counter-based classification modules are introduced. The QRS detection circuit is autonomous and consumes an average current of 34 nA only, vis-à-vis state-of-the-art QRS detection designs that are digital signal processor (DSP) assisted and consume tens of microwatts of power. Furthermore, a heart-rate estimator provides the number of QRS complexes per minute. Each of the proposed modules is successfully validated through real electrocardiogram (ECG) test signals taken from the PhysioNet database. The proposed beat detector circuit exhibits a sensitivity of 97.85% and a positive prediction of 98.3%. Experimental results based on bedside monitor data show that the proposed classification module provides an overall sensitivity of 96.25% and a positive prediction of 96.97%. The complete classification architecture, implemented fully on analog platform, is simulated in the UMC 0.18-µm CMOS process and consumes 119-nW power with an active silicon area of 450 × 800 µm. Moreover, the low-power implementation makes it suitable for long-term battery-operated remote ECG monitoring systems.

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“…In this section, we evaluate the performance of the proposed method using two publicly available ECG recording datasets that include a relatively small number of instances with variable length and are highly imbalanced. The results of our method are compared against the top-ranking entries from PhysioNet 2015 and 2017 Challenges [12], [13] as well as several recently reported approaches to compare our results with various techniques from expert-based rules [62], [64], machine learning [65], representation learning [67]- [69], and methods based on combing the rule-and machine learningbased techniques [66]. Various baseline classifiers are also included in the comparison.…”

Section: Resultsmentioning

confidence: 99%

“…We used baseline category classifiers in MATLAB Classification Learner including the decision tree, linear discriminant, logistic regression, Naive Bayes, SVM, KNN, and Ensemble learning and reported results of their best variant for each category. Results on 2015 PhysioNet computing in Cardiology Dataset: Current works in false alarm reduction can be divided to rule-based methods based on human knowledge [62], [64], classical machine learning (ML) methods [65], and representation learning ones [67]- [69]. The methods based on human knowledge or combinations of ML and expert knowledge methods lead to considerably better results compared to the ones only using ML [91], since the MLbased approaches cannot show good performance when dealing with imbalanced datasets where there are only a few instances available for some arrhythmia.…”

Section: Resultsmentioning

confidence: 99%

“…The proposed method in [68] used a deep neuroevolution method that utilized genetic algorithms and [69] used neural networks for arrhythmia classification. The former method is based on using handcrafted features introduced by [70], which includes morphological and frequency features extracted from the ECG, ABP, and PPG signals.…”

Section: A Review Of Related Work For False Alarm Reduction In Icusmentioning

confidence: 99%

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Piece-wise Matching Layer in Representation Learning for ECG Classification

Ghazanfari¹,

Afghah²,

Zhang³

2020

Preprint

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This paper proposes piece-wise matching layer as a novel layer in representation learning methods for electrocardiogram (ECG) classification. Despite the remarkable performance of representation learning methods in the analysis of time series, there are still several challenges associated with these methods ranging from the complex structures of methods, the lack of generality of solutions, the need for expert knowledge, and large-scale training datasets. We introduce the piece-wise matching layer that works based on two levels to address some of the aforementioned challenges. At the first level, a set of morphological, statistical, and frequency features and comparative forms of them are computed based on each periodic part and its neighbors. At the second level, these features are modified by predefined transformation functions based on a receptive field scenario. Several scenarios of offline processing, incremental processing, fixed sliding receptive field, and event-based triggering receptive field can be implemented based on the choice of length and mechanism of indicating the receptive field. We propose dynamic time wrapping as a mechanism that indicates a receptive field based on event triggering tactics. The proposed layer outperforms the common kernels and pooling on high-level representations of ECG classification. This layer offers several advantages including 1) it enables the learning from few instances for training and handling long quasi-periodic time series, 2) the layer followed by a simultaneous multiple feature tracking mechanism that does not require any annotated data or expert knowledge in quasi-periodic time series, and 3) the proposed framework can provide and handle several scenarios like online processing and dynamic data-driven application systems. To evaluate the performance of this method in time series analysis, we applied the proposed layer in two publicly available datasets of PhysioNet competitions in 2015 and 2017 where the input data is ECG signal. These datasets present a good example of quasiperiodic time series signals with a small number of training samples, highly imbalanced classes, long time series recordings, and non-linear and complex patterns. We compared the performance of our method against a variety of known tuned methods from expert knowledge, machine learning, deep learning methods, and the combination of them. The proposed approach improves the state of the art in two known completions 2015 and 2017 around 4% and 7% correspondingly while it does not rely on in advance knowledge of the classes or the possible places of arrhythmia.

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Prediction of Life-Threatening Heart Arrhythmias Using Obstructive Sleep Apnoea Characteristics

Cited by 5 publications

References 14 publications

A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree

A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree

A Fully Analog Autonomous QRS Complex Detection and Low-Complexity Asystole, Extreme Bradycardia, and Tachycardia Classification System

Piece-wise Matching Layer in Representation Learning for ECG Classification

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