Automatic SVM classification of sudden cardiac death and pump failure death from autonomic and repolarization ECG
Julia Ramírez1, Violeta Monasterio2, Ana Mincholé3
1Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain; Biomedical Signal Interpretation and Computational Simulation (BSICoS) group, Aragón Institute of Engineering Research, IIS Aragón, University of Zaragoza, Zaragoza, Spain.
Insights
Electrocardiogram (ECG) markers like dispersion of repolarization restitution (Δα), T-wave alternans (IAA), and heart rate turbulence slope (TS) can help identify chronic heart failure patients at risk of sudden cardiac death (SCD) and pump failure death (PFD). Combining these ECG markers improves risk discrimination in CHF patients.
Area of Science:
- Cardiology
- Biomedical Engineering
- Medical Informatics
Background:
- Chronic heart failure (CHF) poses a significant risk of sudden cardiac death (SCD) and pump failure death (PFD).
- Accurate identification of high-risk CHF patients is crucial for effective preventative treatment strategies.
- Current risk stratification methods require enhancement for improved prognostic performance.
Purpose of the Study:
- To evaluate the prognostic capability of combining three ECG-derived markers: dispersion of repolarization restitution (Δα), T-wave alternans (IAA), and heart rate turbulence slope (TS).
- To assess the effectiveness of these markers in classifying CHF patients into SCD, PFD, and other outcomes.
- To determine the optimal combination of markers for discriminating between SCD and PFD in CHF patients.
Main Methods:
- Analysis of Holter ECG recordings from 597 CHF patients in sinus rhythm.
- Calculation of Δα, IAA, and TS indices for each patient.
- Implementation of a support vector machine (SVM) classifier to categorize patients into SCD, PFD, or other groups.
- Utilizing cross-validation for performance evaluation of the SVM classifier.
Main Results:
- Δα (≥0.035) and IAA (≥3.73 microV) were strongly associated with SCD risk, while TS (≤2.5 ms/RR) was linked to PFD risk.
- The combination of Δα and IAA improved sensitivity for SCD detection compared to Δα alone.
- The combination of Δα and TS yielded higher sensitivity for PFD detection than TS alone.
- SVM classification achieved a maximum specificity of 79% with 18% sensitivity for SCD and 81% specificity with 14% sensitivity for PFD.
Conclusions:
- ECG-derived risk markers, including Δα, TS, and IAA, demonstrate potential for efficient discrimination of SCD and PFD in CHF patients.
- The combined use of these ECG markers offers a promising non-invasive approach for risk stratification in CHF.
- Further research can refine these markers for clinical application in managing CHF patients.
Background:
Considering the rates of sudden cardiac death (SCD) and pump failure death (PFD) in chronic heart failure (CHF) patients and the cost-effectiveness of their preventing treatments, identification of CHF patients at risk is an important challenge. In this work, we studied the prognostic performance of the combination of an index potentially related to dispersion of repolarization restitution (Δα), an index quantifying T-wave alternans (IAA) and the slope of heart rate turbulence (TS) for classification of SCD and PFD.
Methods:
Holter ECG recordings of 597 CHF patients with sinus rhythm enrolled in the MUSIC study were analyzed and Δα, IAA and TS were obtained. A strategy was implemented using support vector machines (SVM) to classify patients in three groups: SCD victims, PFD victims and other patients (the latter including survivors and victims of non-cardiac causes). Cross-validation was used to evaluate the performance of the implemented classifier.
Results:
Δα and IAA, dichotomized at 0.035 (dimensionless) and 3.73 microV, respectively, were the ECG markers most strongly associated with SCD, while TS, dichotomized at 2.5 ms/RR, was the index most strongly related to PFD. When separating SCD victims from the rest of patients, the individual marker with best performance was Δα≥0.035, which, for a fixed specificity (Sp) of 90%, showed a sensitivity (Se) value of 10%, while the combination of Δα and IAA increased Se to 18%. For separation of PFD victims from the rest of patients, the best individual marker was TS ≤ 2.5 ms/RR, which, for Sp=90%, showed a Se of 26%, this value being lower than Se=34%, produced by the combination of Δα and TS. Furthermore, when performing SVM classification into the three reported groups, the optimal combination of risk markers led to a maximum Sp of 79% (Se=18%) for SCD and Sp of 81% (Se=14%) for PFD.
Conclusions:
The results shown in this work suggest that it is possible to efficiently discriminate SCD and PFD in a population of CHF patients using ECG-derived risk markers like Δα, TS and IAA.
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