Temporal feature and heuristics-based Noise Detection over Classical Machine Learning for ECG Signal Quality Assessment

2019 ◽  
Author(s):  
Indra Hermawan ◽  
M. Anwar Ma'sum ◽  
P Riskyana Dewi Intan ◽  
Wisnu Jatmiko ◽  
Budi Wiweko ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Assessment ◽  
Signal Quality ◽  
Ecg Signal ◽  
Noise Detection ◽  
Temporal Feature

scholarly journals A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression

2018 ◽  
Vol 2018 ◽  
pp. 1-26 ◽  
Author(s):  
Andrea Němcová ◽  
Radovan Smíšek ◽  
Lucie Maršánová ◽  
Lukáš Smital ◽  
Martin Vítek
Keyword(s):  
Comparative Analysis ◽  
Quality Assessment ◽  
Quality Evaluation ◽  
Signal Transmission ◽  
Signal Quality ◽  
Ecg Signal ◽  
Extensive Review ◽  
Compression Process ◽  
Compression Efficiency ◽  
Compression Performance

The assessment of ECG signal quality after compression is an essential part of the compression process. Compression facilitates the signal archiving, speeds up signal transmission, and reduces the energy consumption. Conversely, lossy compression distorts the signals. Therefore, it is necessary to express the compression performance through both compression efficiency and signal quality. This paper provides an overview of objective algorithms for the assessment of both ECG signal quality after compression and compression efficiency. In this area, there is a lack of standardization, and there is no extensive review as such. 40 methods were tested in terms of their suitability for quality assessment. For this purpose, the whole CSE database was used. The tested signals were compressed using an algorithm based on SPIHT with varying efficiency. As a reference, compressed signals were manually assessed by two experts and classified into three quality groups. Owing to the experts’ classification, we determined corresponding ranges of selected quality evaluation methods’ values. The suitability of the methods for quality assessment was evaluated based on five criteria. For the assessment of ECG signal quality after compression, we recommend using a combination of these methods: PSim SDNN, QS, SNR1, MSE, PRDN1, MAX, STDERR, and WEDD SWT.

scholarly journals Recurrence Plot and Machine Learning for Signal Quality Assessment of Photoplethysmogram in Mobile Environment

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2188
Author(s):  
Donggeun Roh ◽  
Hangsik Shin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Quality Assessment ◽  
Feature Detection ◽  
Layer Structure ◽  
Recurrence Plot ◽  
Classification Model ◽  
Signal Quality ◽  
Predictive Values

The purpose of this study was to develop a machine learning model that could accurately evaluate the quality of a photoplethysmogram based on the shape of the photoplethysmogram and the phase relevance in a pulsatile waveform without requiring complicated pre-processing. Photoplethysmograms were recorded for 76 participants (5 min for each participant). All recorded photoplethysmograms were segmented for each beat to obtain a total of 49,561 pulsatile segments. These pulsatile segments were manually labeled as ‘good’ and ‘poor’ classes and converted to a two-dimensional phase space trajectory image using a recurrence plot. The classification model was implemented using a convolutional neural network with a two-layer structure. As a result, the proposed model correctly classified 48,827 segments out of 49,561 segments and misclassified 734 segments, showing a balanced accuracy of 0.975. Sensitivity, specificity, and positive predictive values of the developed model for the test dataset with a ‘poor’ class classification were 0.964, 0.987, and 0.848, respectively. The area under the curve was 0.994. The convolutional neural network model with recurrence plot as input proposed in this study can be used for signal quality assessment as a generalized model with high accuracy through data expansion. It has an advantage in that it does not require complicated pre-processing or a feature detection process.

scholarly journals Comparison of Machine Learning Algorithms for the Quality Assessment of Wearable ECG Signals Via Lenovo H3 Devices

2021 ◽  
Author(s):  
Fan Fu ◽  
Wentao Xiang ◽  
Yukun An ◽  
Bin Liu ◽  
Xianqing Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Quality Assessment ◽  
Learning Algorithms ◽  
Wearable Devices ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
Support Vector ◽  
Signal Quality ◽  
Ecg Signals

Abstract Purpose Electrocardiogram (ECG) signals collected from wearable devices are easily corrupted with surrounding noise and artefacts, where the signal-to-noise ratio (SNR) of wearable ECG signals is significantly lower than that from hospital ECG machines. To meet the requirements for monitoring heart disease via wearable devices, eliminating useless or poor-quality ECG signals (e.g., lead-falls and low SNRs) can be solved by signal quality assessment algorithms. Methods To compensate for the deficiency of the existing ECG quality assessment system, a wearable ECG signal dataset from heart disease patients collected by Lenovo H3 devices was constructed. Then, this paper compares the performance of three machine learning algorithms, i.e., the traditional support vector machine (SVM), least-squares SVM (LS-SVM) and long short-term memory (LSTM) algorithms. Different non-morphological signal quality indices (i.e., the approximate entropy (ApEn), sample entropy (SaEn), fuzzy measure entropy (FMEn), Hurst exponent (HE), kurtosis (K) and power spectral density (PSD) features) extracted from the original ECG signals are fed into the three algorithms as input. Results The true positive rate, true negative rate, sensitivity and accuracy are used to evaluate the performance of each method, and the LSTM algorithm achieves the best results on these metrics (97.14%, 86.8%, 97.46% and 95.47%, respectively). Conclusions Among the three algorithms, the LSTM-based quality assessment method is the most suitable for the signals collected by the Lenovo H3 devices. The results also show that the combination of statistical features can effectively evaluate the quality of ECG signals.

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