Cardiac arrhythmia detection using linear and non-linear features of HRV signal

Sivanantham, A.; Devi, Swapna

doi:10.1109/icaccct.2014.7019200

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…This will form the in-phase component and the quadrature component of the baseband signal (also called the IF signal). The I/Q two-way IF signal is expressed in complex form as Equation (4).…”

Section: Signal Modelmentioning

confidence: 99%

“…HRV holds diverse clinical implications, particularly in conditions like coronary heart disease [1], hypertension [2], and diabetic neuropathy, which manifest as neurological impairments [3] in the cardiac system. Moreover, HRV serves as a valuable tool for diagnosing different types of arrhythmias [4][5][6], guiding pharmacological interventions and effectiveness judgment [7]. As a non-invasive physiological indicator, HRV has attracted significant attention, showcasing its potential for application in clinical settings.…”

Section: Introductionmentioning

confidence: 99%

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Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning

Yuan,

Fan,

Deng

et al. 2024

Sensors

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The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value.

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Section: Signal Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning

Yuan,

Fan,

Deng

et al. 2024

Sensors

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“…According to the methods used for extraction, the obtained patterns can be classified in categories such as: wavelet, statistical, and heartbeat or morphological features. Researchers use them either individually [34]- [38] or coupled with other features [1], [32], [39], [40].…”

Section: Features Extractionmentioning

confidence: 99%

“…Sahoo et al [77] classified temporal, morphological and heartbeat features with an accuracy of 96.67% using an SVM model. Heart rate variability (HRV) features are also frequently used for arrhythmia detection using SVM classifiers ( [27], [39], [95]- [98] ), being advantageous as they can also be calculated from other bio-signals such as PPG [99]. Other papers that employed SVM for the final classification are illustrated and summarized in table 4 [27], [39], [100].…”

Section: Support Vector Machinementioning

confidence: 99%

Techniques to aid prediction of pacing dependence at 30 days in patients requiring pacemaker implantation after cardiac surgery

Cretu

Tindale

Abbod

et al. 2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Cardiac arrhythmia is a condition caused by an impaired electrical conduction of the heart, resulting in irregular rhythms that can increase the risk of stroke or even lead to sudden cardiac deaths. Luckily, bio-signals such as electrocardiogram (ECG) and arterial blood pressure (ABP) can be utilized to assess the health of the patient. This work presents a comprehensive review on the recent machine learning (ML) and deep learning methods applied for arrhythmia classification using both, ECG and ABP signals, including preliminary steps such as pre-processing, feature extraction and feature optimization.

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“…In works of many authors the Discriminant analysis is used as the classifying method: in tasks of automatic sleep staging (Ebrahimi et al, 2013), mental load estimation (Cinaz et al, 2013), arrhythmia detection (Sivanantham and Shenbaga Devi, 2014), real-life stress detection (Melillo et al, 2011) and for automatic assessment of heart failure severity (Melillo et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Application of the Discriminant Analysis for Diagnostics of the Arterial Hypertension - Analysis of Short-Term Heart Rate Variability Signals

Kublanov

Dolganov

Borisov

2016

Proceedings of the 4th International Congress on Neurotechnology, Electronics and Informatics

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The investigation of the diagnostic possibilities for the arterial hypertension is presented. The 41 features of the statistical, geometric, spectral and nonlinear methods during functional loads were considered for two groups: healthy volunteers and patients suffering from the arterial hypertension of the II-III degree. Application of the linear and quadratic discriminant analysis showed particular features that have high classification efficiency.

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Cardiac arrhythmia detection using linear and non-linear features of HRV signal

Cited by 9 publications

References 7 publications

Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning

Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning

Techniques to aid prediction of pacing dependence at 30 days in patients requiring pacemaker implantation after cardiac surgery

Application of the Discriminant Analysis for Diagnostics of the Arterial Hypertension - Analysis of Short-Term Heart Rate Variability Signals

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