Key result
The proposed embedded algorithm for QRS detection achieved average F1 scores of 99.67%, 99.73%, and 99.83% on the MIT-BIH Arrhythmia, Pacemaker Rhythm, and Normal Sinus Rhythm databases, respectively.
Why the study?
Most existing QRS detection algorithms rely on complex computations requiring off-line processing on a personal computer, creating a need for efficient embedded algorithms for wearable devices and telemedicine.
A novel embedded algorithm for QRS detection demonstrates high accuracy (F1 >99.6%) and fast execution times, making it highly suitable for wearable devices and telemedicine applications.
Answers are grounded in this paper and cite their sources.
May enable real-time QRS detection on wearables; leaves open prospective clinical validation.
Electrocardiogram (ECG) is one of the most useful medical examinations for the monitoring of cardiovascular diseases. The position and the duration of the QRS complex on the ECG signal are very important in the diagnosis of these diseases. Even though several R-peak (and hence QRS complex) detection algorithms are available, most are based on complex computations that require off-line processing on a personal computer (PC). However, with the advances in wearable devices and telemedicine, an algorithm that can run efficiently on a microcontroller (or embedded system) is needed. This article presents the development of an embedded algorithm for the detection of the QRS complex of an ECG signal. The algorithm is based on the shape and appearance of the signal. It extracts certain characteristics like the shape of the QRS complex, its slope, trend, and the duration between two successive QRS complexes, and then use them to increase detection accuracy. First, the R-peak is detected through the application of three levels of tests using adaptive thresholds. Second, from each R position, the positions of Q and S are detected using three other tests. To evaluate the performance of the algorithm, the MIT-BIH database was used and the sensitivity, positive prediction, and F1 score were used as evaluation metrics. The algorithm obtained average F1 scores of 99.67%, 99.73%, and 99.83% for the MIT-BIH Arrhythmia, Pacemaker Rhythm, and the Normal Sinus Rhythm Databases, respectively. Both normal and abnormal ECG signals were used in this performance test. The algorithm was then implemented on a microcontroller system, and its accuracy and run time were evaluated. The obtained F1 score results were the same as on the personal computer (PC) and an average run time of <tex-math notation="LaTeX">16.23~μ s </tex-math> per sample was obtained. The performance of the algorithm was also compared to other commonly used algorithms. The proposed algorithm has great potential in wearable systems for long-term monitoring.
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Tueche et al. (2021) studied Cardiovascular diseases (ECG monitoring). Embedded algorithm for QRS detection based on signal shape vs. Other commonly used algorithms was evaluated on F1 score for QRS detection. The proposed embedded algorithm for QRS detection achieved average F1 scores of 99.67%, 99.73%, and 99.83% on the MIT-BIH Arrhythmia, Pacemaker Rhythm, and Normal Sinus Rhythm databases, respectively.
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