An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm

Qin, Qin; Li, Jianqing; Yue, Yinggao; Liu, Chengyu

doi:10.1155/2017/5980541

Cited by 78 publications

(33 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Lossy methods, which traditionally were avoided in this setting, have been successfully revisited [ 18 ], and different compression methods based on the compression ratio and percentage root-mean-squared difference were proposed and advantageously benchmarked, including in terms of the accuracy of R-wave detection. Many other works are being developed in the very last years accounting for wavelet multiresolution analysis with promising results [ 51 ].…”

Section: Discussionmentioning

confidence: 99%

On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios

Melgarejo-Meseguer

Everss

Gimeno-Blanes

et al. 2018

Sensors

View full text Add to dashboard Cite

Despite the wide literature on R-wave detection algorithms for ECG Holter recordings, the long-term monitoring applications are bringing new requirements, and it is not clear that the existing methods can be straightforwardly used in those scenarios. Our aim in this work was twofold: First, we scrutinized the scope and limitations of existing methods for Holter monitoring when moving to long-term monitoring; Second, we proposed and benchmarked a beat detection method with adequate accuracy and usefulness in long-term scenarios. A longitudinal study was made with the most widely used waveform analysis algorithms, which allowed us to tune the free parameters of the required blocks, and a transversal study analyzed how these parameters change when moving to different databases. With all the above, the extension to long-term monitoring in a database of 7-day Holter monitoring was proposed and analyzed, by using an optimized simultaneous-multilead processing. We considered both own and public databases. In this new scenario, the noise-avoid mechanisms are more important due to the amount of noise that exists in these recordings, moreover, the computational efficiency is a key parameter in order to export the algorithm to the clinical practice. The method based on a Polling function outperformed the others in terms of accuracy and computational efficiency, yielding 99.48% sensitivity, 99.54% specificity, 99.69% positive predictive value, 99.46% accuracy, and 0.85% error for MIT-BIH arrhythmia database. We conclude that the method can be used in long-term Holter monitoring systems.

show abstract

Section: Discussionmentioning

confidence: 99%

On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios

Melgarejo-Meseguer

Everss

Gimeno-Blanes

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Following the denoising, the R peak of each ECG beat is located from the continuous beat stream. Various methods are proposed for R peak detection in the literature [32]- [38].…”

Section: B R Peak Detectionmentioning

confidence: 99%

DeepArrNet: An Efficient Deep CNN Architecture for Automatic Arrhythmia Detection and Classification From Denoised ECG Beats

2020

View full text Add to dashboard Cite

In this paper, an efficient deep convolutional neural network (CNN) architecture is proposed based on depthwise temporal convolution along with a robust end-to-end scheme to automatically detect and classify arrhythmia from denoised electrocardiogram (ECG) signal, which is termed as 'DeepArrNet'. Firstly, considering the variational pattern of wavelet denoised ECG data, a realistic augmentation scheme is designed that offers a reduction in class imbalance as well as increased data variations. A structural unit, namely PTP (Pontwise-Temporal-Pointwise Convolution) unit, is designed with its variants where depthwise temporal convolutions with varying kernel sizes are incorporated along with prior and post pointwise convolution. Afterward, a deep neural network architecture is constructed based on the proposed structural unit where series of such structural units are stacked together while increasing the kernel sizes for depthwise temporal convolutions in successive units along with the residual linkage between units through feature addition. Moreover, multiple depthwise temporal convolutions are introduced with varying kernel sizes in each structural unit to make the process more efficient while strided convolutions are utilized in the residual linkage between subsequent units to compensate the increased computational complexity. This architecture provides the opportunity to explore the temporal features in between convolutional layers more optimally from different perspectives utilizing diversified temporal kernels. Extensive experimentations are carried out on two publicly available datasets to validate the proposed scheme that results in outstanding performances in all traditional evaluation metrics outperforming other state-of-the-art approaches.

show abstract

“…Jenkal et al [61] have denoised the ECG signals by the DWT based thresholding for making the QRS complexes more perceptible, and then detected all the possible QRS complexes by the relatively small amplitude threshold, and finally removed the false QRS complexes by a moving threshold window whose size is determined by the maximum QRS complex duration and the maximum heart rate. Qin et al [62] have denoised the ECG signals by the DWT based thresholding for making the R peaks more detectable, and then adopted the signal mirroring technique to convert the large negative R peaks to the positive ones based on the processed signals, and finally truncated the local maxima by the amplitude threshold and the interval threshold to locate the R peaks.…”

Section: C: Temporal Thresholding and Morphological Thresholding Are mentioning

confidence: 99%

Wavelets for Electrocardiogram: Overview and Taxonomy

2019

IEEE Access

View full text Add to dashboard Cite

Physiological and pathological information within electrocardiogram (ECG) is crucial for the diagnosis of heart diseases. Computer-aided diagnosis for the ECG signals has drawn growing research attention up to date. Automatic ECG analysis mainly includes signal denoising, wave detection, and heartbeat classification. These three issues are relevant that the signal denoising can help attenuate the noises and accentuate the typical waves in ECG signals for wave detection, and wave detection can help locate the typical ECG waves and acquire the diagnostically valuable heartbeats based on these waves for the heartbeat classification. The wavelet-based methods play important roles in the three issues, but these methods are scattered and unorganized in the literature. In order to manifest the value of these methods, this paper contributes an overview and taxonomy on them. This paper does the comprehensive summary and systematic categorization on the methods for signal denoising, wave detection, and heartbeat classification according to the deep analysis of their methodological characteristics. By doing so, this paper not only uncovers the inner mechanism that why wavelet-based methods are suitable for ECG analysis but also reveals the designing principles that these methods potentially follow. Finally, this paper has provided an outlook for the developing prospect of ''wavelets for ECG'' in the future. INDEX TERMS Wavelets for electrocardiogram, signal denoising, wave detection, heartbeat classification, overview and taxonomy. I. OUTLINE AND INTRODUCTION A. OUTLINE AND CONTRIBUTION

show abstract

An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm

Cited by 78 publications

References 43 publications

On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios

On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios

DeepArrNet: An Efficient Deep CNN Architecture for Automatic Arrhythmia Detection and Classification From Denoised ECG Beats

Wavelets for Electrocardiogram: Overview and Taxonomy

Contact Info

Product

Resources

About