A new QRS detector stress test combining temporal jitter and F-score (JF) reveals significant performance differences amongst popular detectors

Porr, Bernd; Howell, Luis

doi:10.1101/722397

Cited by 18 publications

(19 citation statements)

References 43 publications

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“…The current results may be limited to the size of test dataset. In our future work, we plan to further validate the proposed method with a publicly available ECG database recently published by Porr and Howell [27]. This database contains noisy ECG signals where noise originates from the real life activities like walking or jogging.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Those methods are usually lightweight and could be implemented on embedded devices. However, these algorithms often only perform well with relatively clean ECG signals but are not robust enough to noises and artifacts [7,26,27]. On the other hand, the signal quality could vary over time, particularly in wearable devices, due to motions, electrode-skin conductance changes, or the use of dry electrodes [18,24].…”

Section: Introductionmentioning

confidence: 99%

“…However, the signals in this database are relatively noise free and thus they alone are not enough for training a robust R-peak detector. To the best of our knowledge, there are only two open databases that contain noisy ECG signals with annotated peaks [22,27]. However, these databases are not large enough for training accurate neural network models.…”

Section: Introductionmentioning

confidence: 99%

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Robust ECG R-peak detection using LSTM

Laitala

Jiang

Syrjälä

et al. 2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

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Detecting QRS complexes or R-peaks from the electrocardiogram (ECG) is the basis for heart rate determination and heart rate variability analysis. Over the years, multiple different methods have been proposed as solutions to this problem. Vast majority of the proposed methods are traditional rule based algorithms that are vulnerable to noise. We propose a new R-peak detection method that is based on the Long Short-Term Memory (LSTM) network. LSTM networks excel at temporal modelling tasks that include long-term dependencies, making it suitable for ECG analysis. Additionally, we propose data generator for creating noisy ECG data that is used to train the robust R-peak detector. Our initial testing shows that the proposed method outperforms traditional algorithms while the greatest competitive edge is achieved with the noisy ECG signals.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust ECG R-peak detection using LSTM

Laitala

Jiang

Syrjälä

et al. 2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

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“…As discussed earlier, the obtained performance of the proposed algorithm is the most efficient when the parameter µ is chosen as 0.02, α is 0.7 and the filter order is 5. The proposed algorithm's performance will be tested on the ECG stress test signals of the Glasgow University Database (GUDB) [10]. This database includes 125 ECG signal records collected from 25 subjects with five different tasks (sitting, math test, hand-bike, walking, and jogging on a treadmill).…”

Section: Simulation Resultsmentioning

confidence: 99%

Adaptive Noise Filter for Real-Time Stress ECG Signal Analysis

Hoang¹,

Pham²

2020

JST

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The stress Electrocardiogram (ECG) gives more efficient results for the diagnosis of cardiovascular diseases, which may not be apparent when the patients are at rest. However, the noise produced by the movement of the patient and the environment often contaminates the ECG signal. Motion artifact is the most prevalent and difficult type of interference to filter in stress test ECG. It corrupts the quality of the desired signal thus reducing the reliability of the stress test. In this work, we first describe a quantitative study of adaptive filtering for processing the stress ECG signals. The proposed method uses the motion information obtained from a 3-axis accelerometer as a noise reference signal for the adaptive filter and the optimal weight of the adaptive filter is adjusted by the Modified Error Data Normalized Step-Size (MEDNSS) algorithm. Finally, the performance of the proposed algorithm is tested on the stress ECG signal from the subject.

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“…Figure 2 shows the extracted R − S peak values from an ECG signal. By extracting these data under stress and without stress, the ECG can be accurately analyzed [ 22 ]. R peak and S peak were extracted from ECG signals after setting a threshold.…”

Section: Methodsmentioning

confidence: 99%

Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals

Kang

Shin

Jung

et al. 2021

Journal of Healthcare Engineering

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The mental stress faced by many people in modern society is a factor that causes various chronic diseases, such as depression, cancer, and cardiovascular disease, according to stress accumulation. Therefore, it is very important to regularly manage and monitor a person’s stress. In this study, we propose an ensemble algorithm that can accurately determine mental stress states using a modified convolutional neural network (CNN)- long short-term memory (LSTM) architecture. When a person is exposed to stress, a displacement occurs in the electrocardiogram (ECG) signal. It is possible to classify stress signals by analyzing ECG signals and extracting specific parameters. To maximize the performance of the proposed stress classification algorithm, fast Fourier transform (FFT) and spectrograms were applied to preprocess ECG signals and produce signals in both the time and frequency domains to aid the training process. As the performance evaluation benchmarks of the stress classification model, confusion matrices, receiver operating characteristic (ROC) curves, and precision-recall (PR) curves were used, and the accuracy achieved by the proposed model was 98.3%, which is an improvement of 14.7% compared to previous research results. Therefore, our model can help manage the mental health of people exposed to stress. In addition, if combined with various biosignals such as electromyogram (EMG) and photoplethysmography (PPG), it may have the potential for development in various healthcare systems, such as home training, sleep state analysis, and cardiovascular monitoring.

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A new QRS detector stress test combining temporal jitter and F-score (JF) reveals significant performance differences amongst popular detectors

Cited by 18 publications

References 43 publications

Robust ECG R-peak detection using LSTM

Robust ECG R-peak detection using LSTM

Adaptive Noise Filter for Real-Time Stress ECG Signal Analysis

Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals

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