What is the clinical evidence from this study?

Study design: Other. Population: Arrhythmia. Intervention: Multifeature combination and Stacking-DWKNN algorithm vs. Other models with the same features. Primary outcome: Heartbeat classification accuracy, sensitivity, and positive predictive value.

January 28, 2021Open Access

Heartbeat Classification Based on Multifeature Combination and Stacking-DWKNN Algorithm

Key Result

The multifeature combination and Stacking-DWKNN algorithm achieved an average accuracy of 99.01% for heartbeat classification on the MIT-BIH database.

Structured PICO

Does the multifeature combination and Stacking-DWKNN algorithm improve the accuracy of heartbeat classification compared to other models?

Population

Heartbeat data from the MIT-BIH arrhythmia database used to classify normal and abnormal heartbeats.

Intervention

Classification strategy using multifeature combination and Stacking-DWKNN algorithm

Comparator

Other models with the same features

Outcome

Identification of four types of normal and abnormal heartbeats (measured by accuracy, sensitivity, and positive predictive value)surrogate

A novel Stacking-DWKNN algorithm using multifeature combination achieves 99.01% average accuracy in classifying heartbeats, offering a highly sensitive tool for automated arrhythmia detection.

Abstract

Arrhythmia is one of the most common abnormal symptoms that can threaten human life. In order to distinguish arrhythmia more accurately, the classification strategy of the multifeature combination and Stacking-DWKNN algorithm is proposed in this paper. The method consists of four modules. In the preprocessing module, the signal is denoised and segmented. Then, multiple different features are extracted based on single heartbeat morphology, P length, QRS length, T length, PR interval, ST segment, QT interval, RR interval, R amplitude, and T amplitude. Subsequently, the features are combined and normalized, and the effect of different feature combinations on heartbeat classification is analyzed to select the optimal feature combination. Finally, the four types of normal and abnormal heartbeats were identified using the Stacking-DWKNN algorithm. This method is performed on the MIT-BIH arrhythmia database. The result shows a sensitivity of 89.42% and a positive predictive value of 94.90% of S-type beats and a sensitivity of 97.21% and a positive predictive value of 97.07% of V-type beats. The obtained average accuracy is 99.01%. Compared to other models with the same features, this method can improve accuracy and has a higher positive predictive value and sensitivity, which is important for clinical decision-making.

KI fragen

Bookmark

View Full Paper