Prediction of Sudden Cardiac Death in Implantable Cardioverter Defibrillators: A Review and Comparative Study of Heart Rate Variability Features

Parsi, Ashkan; O’Loughlin, Declan; Glavin, Martin; Jones, Edward

doi:10.1109/rbme.2019.2912313

Cited by 16 publications

(7 citation statements)

References 82 publications

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“…Short-term HRV analysis can track dynamic changes of cardiac autonomic function within minutes and is a convenient method for the estimation of autonomic status. Many studies have evaluated the value of HRV metrics for SCD prediction with short-term HRV analysis [ 21 , 29 , 30 ]. For example, Hämmerle et al [ 31 ] demonstrated that HRVTI measured in a single 5-min ECG recording in a cohort of patients with atrial fibrillation is an independent predictor of cardiovascular mortality and might be a valuable tool for further risk stratification to guide patient management.…”

Section: Discussionmentioning

confidence: 99%

“…The following 11 commonly used HRV metrics were calculated according to previously published literatures [ 21 ]: (1) Time-domain metrics, include the standard deviation of normally conducted RRIs (SDNN), the root mean square of successive differences in normally conducted RRIs (RMSSD), triangular interpolation of RRI histogram (TINN) and HRV triangular index (HRVTI); (2) frequency‐domain metrics, include total power with frequency < 0.4 Hz (TP), power in low frequency range (0.04–0.15 Hz) (LF), and relative power of the low frequency range in normalized units (nLF); (3) nonlinear dynamic metrics, include Poincaré plot standard deviation perpendicular the line of identity (SD1), ratio of SD1 to Poincaré plot standard deviation along the line of identity (SD2) (SD1/SD2), sample entropy (SampEn) and deceleration capacity of heart rate (DC).…”

Section: Methodsmentioning

confidence: 99%

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Performance of heart rate adjusted heart rate variability for risk stratification of sudden cardiac death

Yan

Song

Wei

et al. 2023

BMC Cardiovasc Disord

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Purpose As a non-invasive tool for the assessment of cardiovascular autonomic function, the predictive value of heart rate variability (HRV) for sudden cardiac death (SCD) risk stratification remains unclear. In this study, we investigated the performance of the individualized heart rate (HR) adjusted HRV (HRVI) for SCD risk stratification in subjects with diverse risks. Methods A total of 11 commonly used HRV metrics were analyzed in 192 subjects, including 88 healthy controls (low risk group), 82 hypertrophic cardiomyopathy (HCM) patients (medium risk group), and 22 SCD victims (high risk group). The relationship between HRV metrics and HR was examined with long-term and short-term analysis. The performance HRVI was evaluated by area under the receiver operating characteristic curve (AUC) and covariance of variation (CV). Results Most of the HRV metrics were exponentially decayed with the increase of HR, while the exponential power coefficients were significantly different among groups. The HRVI metrics discriminated low, medium and high risk subjects with a median AUC of 0.72[0.11], which was considerably higher than that of the traditional long-term (0.63[0.04]) and short-term (0.58[0.05]) HRV without adjustment. The average CV of the HRVI metrics was also significantly lower than traditional short-term HRV metrics (0.09 ± 0.02 vs. 0.24 ± 0.13, p < 0.01). Conclusions Subjects with diverse risks of SCD had similar exponential decay relationship between HRV metrics and HR, but with different decaying rates. HRVI provides reliable and robust estimation for risk stratification of SCD.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Performance of heart rate adjusted heart rate variability for risk stratification of sudden cardiac death

Yan

Song

Wei

et al. 2023

BMC Cardiovasc Disord

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“…For this data pre-processing step, the loss-pass filter was employed to filter the noise of the signals for the HHT. Past studies approved a filter at 16 Hz that well-exhibited characteristics of the NSR and arrhythmias [34,35]. Referring to the literature's approach for the Butterworth filter, the cutoff frequency with a low-pass order between 4 and 8 was one of the suggestive parameters to remove ECG's noise [36].…”

Section: Featuring By Marginal Hilbert Spectrummentioning

confidence: 99%

Hybrid-Pattern Recognition Modeling with Arrhythmia Signal Processing for Ubiquitous Health Management

Hsiao

Kan

Kuo

et al. 2022

Sensors

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We established a web-based ubiquitous health management (UHM) system, “ECG4UHM”, for processing ECG signals with AI-enabled models to recognize hybrid arrhythmia patterns, including atrial premature atrial complex (APC), atrial fibrillation (AFib), ventricular premature complex (VPC), and ventricular tachycardia (VT), versus normal sinus rhythm (NSR). The analytical model coupled machine learning methods, such as multiple layer perceptron (MLP), random forest (RF), support vector machine (SVM), and naive Bayes (NB), to process the hybrid patterns of four arrhythmia symptoms for AI computation. The data pre-processing used Hilbert–Huang transform (HHT) with empirical mode decomposition to calculate ECGs’ intrinsic mode functions (IMFs). The area centroids of the IMFs’ marginal Hilbert spectrum were suggested as the HHT-based features. We engaged the MATLABTM compiler and runtime server in the ECG4UHM to build the recognition modules for driving AI computation to identify the arrhythmia symptoms. The modeling extracted the crucial data sets from the MIT-BIH arrhythmia open database. The validated models, including the premature pattern (i.e., APC–VPC) and the fibril-rapid pattern (i.e., AFib–VT) against NSR, could reach the best area under the curve (AUC) of the receiver operating characteristic (ROC) of approximately 0.99. The models for all hybrid patterns, without VPC versus AFib and VT, achieved an average accuracy of approximately 90%. With the prediction test, the respective AUCs of the NSR and APC versus the AFib, VPC, and VT were 0.94 and 0.93 for the RF and SVM on average. The average accuracy and the AUC of the MLP, RF, and SVM models for APC–VT reached the value of 0.98. The self-developed system with AI computation modeling can be the backend of the intelligent social-health system that can recognize hybrid arrhythmia patterns in the UHM and home-isolated cares.

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“…According to statistics [ 3 ], most SCDs occur outside the hospital. Up to 80% of SCDs are caused by ventricular arrhythmias [ 4 , 5 ], of which ventricular fibrillation (VF) is the primary cause [ 6 ]. VF is the rapid, disordered, and asynchronous contraction of ventricular muscles, which is manifested as irregular fluctuation in time and space on an electrocardiogram (ECG).…”

Section: Introductionmentioning

confidence: 99%

“…Patients at high risk for SCD are indicated for implantable cardioverter defibrillators, which use intracardiac electrograms to predict VF and provide appropriate treatment [ 5 , 11 ]. However, many cases of VF occur in people without previous diagnosis records, and ECG is currently the most commonly used and effective tool for detecting cardiac status, which is widely used in various telemedicine and mobile health systems, including but not limited to ambulatory Holter recording and automatic external defibrillators (AEDs) [ 7 , 10 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set

Wan

Huang

Wang

2021

Sensors

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Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.

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Prediction of Sudden Cardiac Death in Implantable Cardioverter Defibrillators: A Review and Comparative Study of Heart Rate Variability Features

Cited by 16 publications

References 82 publications

Performance of heart rate adjusted heart rate variability for risk stratification of sudden cardiac death

Performance of heart rate adjusted heart rate variability for risk stratification of sudden cardiac death

Hybrid-Pattern Recognition Modeling with Arrhythmia Signal Processing for Ubiquitous Health Management

Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set

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