ECG quality assessment based on hand-crafted statistics and deep-learned S-transform spectrogram features

Liu, Guoyang; Han, Xiao; Lan, Tian; Zhou, Weidong; Liu, Hui

doi:10.1016/j.cmpb.2021.106269

Cited by 46 publications

(21 citation statements)

References 25 publications

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“…Time series imaging is a popular technology which transforms time series data into images, such as recurrence plot, gramian angular field and spectrogram. Time series imaging is widely used in 2D CNNs for classification tasks, e.g., the 2D representation of time-frequency analysis -spectrogram, log spectrogram, mel spectrogram, and scalogram -is used in 2D CNN [22] [18] [23] [24] [25]. The 2D CNN has a promising performance in image classification, according to the recent literature work.…”

Section: Related Workmentioning

confidence: 99%

Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network

Lü

Wang

Hagenah

et al. 2023

IEEE Trans. Biomed. Eng.

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Tetanus is a life-threatening infectious disease, which is still common in low-and middle-income countries, including in Vietnam. This disease is characterized by muscle spasm and in severe cases is complicated by autonomic dysfunction. Ideally continuous vital sign monitoring using bedside monitors allows the prompt detection of the onset of autonomic nervous system dysfunction or avoiding rapid deterioration. Detection can be improved using heart rate variability analysis from ECG signals. Recently, characteristic ECG and heart rate variability features have been shown to be of value in classifying tetanus severity. However, conventional manual analysis of ECG is time-consuming. The traditional convolutional neural network (CNN) has limitations in extracting the global context information, due to its fixed-sized kernel filters. In this work, we propose a novel hybrid CNN-Transformer model to automatically classify tetanus severity using tetanus monitoring from low-cost wearable sensors. This model can capture the local features from the CNN and the global features from the Transformer. The time series imaging -spectrogram -is transformed from one-dimensional ECG signal and input to the proposed model. The CNN-Transformer model outperforms state-of-the-art methods in tetanus classification, achieves results with a F1 score of 0.82 ± 0.03, precision of 0.94 ± 0.03, recall of 0.73 ± 0.07, specificity of 0.97 ± 0.02, accuracy of 0.88 ± 0.01 and AUC of 0.85 ± 0.03. In addition, we found that Random Forest with enough manually selected features can be comparable with the proposed CNN-Transformer model.

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Section: Related Workmentioning

confidence: 99%

Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network

Lü

Wang

Hagenah

et al. 2023

IEEE Trans. Biomed. Eng.

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show abstract

“…However, due to the different databases, inputs, and network architectures, it is unclear how selecting TFDs, and CNNs can affect heart sound classification. Furthermore, many studies have proved that combining different signal processing methods can improve classification performance [45][46][47][48][49][50]. Though, their combining methods differ significantly, including channelwise stacking [48], spatial concatenation [45], hidden feature fusing [48][49][50] and input vector concatenation [51].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, many studies have proved that combining different signal processing methods can improve classification performance [45][46][47][48][49][50]. Though, their combining methods differ significantly, including channelwise stacking [48], spatial concatenation [45], hidden feature fusing [48][49][50] and input vector concatenation [51]. Among the combination methods, channel-wise stacking is the mainstream.…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Distributions of Heart Sound Signals: A Comparative Study using Convolutional Neural Networks

Bao¹,

Yan²,

Lam³

et al. 2022

Preprint

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Time-Frequency Distributions (TFDs) support the heart sound characterisation and classification in early cardiac screening. However, despite the frequent use of TFDs in signal analysis, no study comprehensively compared their performances on deep learning for automatic diagnosis. Furthermore, the combination of signal processing methods as inputs for Convolutional Neural Networks (CNNs) has been proved as a practical approach to increasing signal classification performance. Therefore, this study aimed to investigate the optimal use of TFD/ combined TFDs as input for CNNs. The presented results revealed that: 1) The transformation of the heart sound signal into the TF domain achieves higher classification performance than using of raw signals. Among the TFDs, the difference in the performance was slight for all the CNN models (within 1.3% in average accuracy). However, Continuous wavelet transform (CWT) and Chirplet transform (CT) outperformed the rest. 2) The appropriate increase of the CNN capacity and architecture optimisation can improve the performance, while the network architecture should not be overly complicated. Based on the ResNet or SEResNet family results, the increase in the number of parameters and the depth of the structure do not improve the performance apparently. 3) Combining TFDs as CNN inputs did not significantly improve the classification results. The findings of this study provided the knowledge for selecting TFDs as CNN input and designing CNN architecture for heart sound classification.

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Section: Introductionmentioning

confidence: 99%

“…The second category is deep learning-based methods [15][16][17][18], which usually utilize abstract features extracted by deep learning techniques or combine them with hand-crafted features to implement SQA. For instance, Liu et al proposed a new method that combines deep learning-based Stockwell Transform (S-Transform) spectrogram features and handcrafted statistical features to achieve SQA [15]. Huerta et al combined convolutional neural networks and wavelet transform to robustly identify high-quality ECG segments in the challenging setting of single-lead recordings of alternating sinus rhythms, atrial fibrillation episodes, and other rhythms [16].…”

Section: Introductionmentioning

confidence: 99%

Fusing depth local dual-view features and dual-input transformer framework for improving the recognition ability of motion artifact-contaminated electrocardiogram

Yuan

Zhao

et al. 2022

Complex Intell. Syst.

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Heart health monitoring based on wearable devices is often contaminated by various noises to varying degrees. Using signal quality indicators (SQIs) to achieve signal quality assessment (SQA) is among the most promising ways to solve this problem, but the performance of SQIs in expressing ECG quality features contaminated by motion artifact (MA) noise remains disappointing. Here, we present a novel SQA method that fuses the proposed depth local dual-view (DLDV) features and the dual-input transformer (DI-Transformer) framework to improve the recognition ability of MA-contaminated ECGs. The proposed DLDV features are to identify subtle differences between MA and ECG through depth local amplitude and phase angle features. When it fuses with the temporal relationship features extracted by DI-Transformer, its accuracy is significantly improved compared to the SQIs-based methods. In addition, we also verify the robustness and the accuracy of DLDV features on four traditional classifiers. Finally, we conduct our experiments on the two datasets. On the PhysioNet/Computing in Cardiology Challenge dataset, the DLDV features (Acc = 95.49%) outperform the combination of six SQIs features (Acc = 91.26%). When combined with our DI-Transformer, it delivered an accuracy of 99.62%, outperforming the state-of-the-art SQA methods. On the artificial testset constructed by MA noise, our DI-Transformer outperforms four traditional methods and also delivered an accuracy of 97.69%.

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ECG quality assessment based on hand-crafted statistics and deep-learned S-transform spectrogram features

Cited by 46 publications

References 25 publications

Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network

Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network

Time-Frequency Distributions of Heart Sound Signals: A Comparative Study using Convolutional Neural Networks

Fusing depth local dual-view features and dual-input transformer framework for improving the recognition ability of motion artifact-contaminated electrocardiogram

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