Deep learning based fetal distress detection from time frequency representation of cardiotocogram signal using Morse wavelet: research study

Daydulo, Yared Daniel; Thamineni, Bheema Lingaiah; Dasari, Hanumesh Kumar; Aboye, Genet Tadese

doi:10.1186/s12911-022-02068-1

Cited by 8 publications

(5 citation statements)

References 38 publications

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“…These authors used the CTU-UHB dataset and obtained 94.1%, 93.32%, and 98.34%, respectively. Duydulo et al [34] also used a CNN approach for CTG signal classification and obtained a 98.7% accuracy score. Parvathavarthine et al [35] and Frasch et al [36] also used CNN approach in their experiments with CTG signal classification and obtained 94.63% and 93.60 % accuracy scores respectively.…”

Section: Resultsmentioning

confidence: 99%

A Novel Approach for Cardiotocography Paper Digitization and Classification for Abnormality Detection

et al. 2023

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Cardiotocography (CTG) is a clinical procedure that is used to track and gauge the severity of fetal distress. Although CTG is the most often used equipment to monitor and assess the health of the fetus, the high rate of false positive results due to visual interpretation significantly contributes to needless surgical delivery or delayed intervention. In this study, a novel approach is introduced where both printing CTG paper is digitized and a machine learning approach is employed to detect the abnormality in the digitized CTG signal. Image processing-based preprocessing steps are employed to make the printing of CTG paper more convenient to extract the CTG signal. Various signal-processing techniques are used to calibrate the extracted CTG signal. Then, Empirical Mode Decomposition (EMD) is used to decompose the CTG signal into its frequency components and instantaneous frequency and spectral entropy features are extracted. After feature normalization and feature selection with ReliefF algorithm, support vector machines (SVM) is used for the classification of the normal and abnormal classes. A novel dataset is used in the experimental works and various performance evaluation metrics are used for the evaluation of the achievement of the proposed method. 10-fold cross-validation-based experiments show that the proposed method is quite efficient in abnormality detection in printing CTG papers where an average accuracy score of around 90.0% is produced.

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Section: Resultsmentioning

confidence: 99%

A Novel Approach for Cardiotocography Paper Digitization and Classification for Abnormality Detection

et al. 2023

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“…Overall, a multi-scale adversarial subdomain adaptation bearing fault diagnosis method is proposed in this paper, which consists of the time-frequency feature extraction module and the multi-scale adversarial subdomain adaptation network (MASDAN), as shown in To verify the effectiveness of CWT and select a suitable wavelet base, the selection experiment is performed on the QUT bearing dataset. The morse wavelet basis [2] and the bump wavelet basis [3] are chosen since the morse wavelet basis has flexible time-frequency local characteristics and strict analyticity and the bump wavelet basis has a smaller variance in frequency. So they both are suitable for the timefrequency analysis of non-stationary signals.…”

Section: Methodmentioning

confidence: 99%

“…In general, two major assumptions are necessary for data-driven diagnostic methods: (1) A large number of samples with labels are required while training diagnostic models. (2) The training and test samples should obey the same distribution. However, these assumptions cannot often be satisfied in actual situations.…”

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confidence: 99%

Adversarial subdomain adaptation method based on multi-scale features for bearing fault diagnosis

Zhou¹,

Zhao²,

Zhang³

et al. 2024

MFC

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Due to the variable working environment of bearings, the collected data often follow different probability distributions. It is hard to directly use the trained models to identify the bearing fault with different operating conditions. In addition, it is a high cost to label the samples for every work condition. To solve these problems, a multi-scale adversarial subdomain adaptation bearing fault diagnosis method is proposed, which is based on Continuous Wavelet Transform (CWT) and our constructed Multi-scale Adversarial SubDomain Adaptation Network (MASDAN). Firstly, to extract the features of non-stationary signals with different frequency bands, CWT is used to convert continuous vibration signals into two-dimensional time-frequency images. Secondly, to enhance the correlation of the features across frequency bands, a multi-scale ConvNeXt is proposed, which adds a multi-scale module based on ConvNeXt to obtain features with different scales. Finally, to reduce the distribution discrepancy between the source domain and the target domain and to avoid feature confusion in different domains, a domain adaptive alignment network introducing domain information is constructed. Two modules are included: the Domain Alignment Classification Network Module (DACNM) based on Multi-kernel Local Maximum Mean Discrepancy (MK-LMMD) and the Domain Adversarial Network Module (DANM) based on domain discrimination. Thus, the MASDAN consists of the multi-scale ConvNeXt module, DACNM, and DANM, which can realize the multi-scale feature extraction and adaptively align the fault features at different domains. The experimental results on the Qingdao University of Technology (QUT) bearing dataset and the Case Western Reserve University (CWRU) bearing dataset demonstrate the proposed method can effectively diagnose bearing faults under different operating conditions.

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“…Current deep learning methods for CTG interpretation, which use the physiological time series data as input, rely on proxy labels for fetal well-being recorded immediately after delivery: the umbilical artery blood pH and the 1-minute Apgar score [17][18][19][20][21][22][23]. Umbilical cord blood pH at the time of birth, often used in high-resource medical facilities, is presently the only objective quantification for the potential occurrence of fetal hypoxia during labor.…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of Deep Learning Models for Cardiotocography Interpretation

Chiou,

Young-Lin,

Kelly

et al. 2024

Preprint

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The inherent variability in the visual interpretation of cardiotocograms (CTGs) by obstetric clinical experts, both intra- and inter-observer, presents a substantial challenge in obstetric care. In response, we investigate automated CTG interpretation as a potential solution to enhance the early detection of fetal hypoxia during labor, which has the potential to reduce unnecessary operative interventions and improve overall maternal and neonatal care. This study employs deep learning techniques to reduce the subjectivity associated with visual CTG interpretation. Our results demonstrate that using objective umbilical cord blood pH outcome measurements, rather than clinician-defined Apgar scores, yields more consistent and robust model performance. Additionally, through a series of ablation studies, we explore the impact of temporal distribution shifts on the performance of these deep learning models. We examine tradeoffs between performance and fairness, specifically evaluating performance across demographic and clinical subgroups. Finally, we discuss the practical implications of our findings for the real-world deployment of such systems, emphasizing their potential utility in medical settings with limited resources.

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Deep learning based fetal distress detection from time frequency representation of cardiotocogram signal using Morse wavelet: research study

Cited by 8 publications

References 38 publications

A Novel Approach for Cardiotocography Paper Digitization and Classification for Abnormality Detection

A Novel Approach for Cardiotocography Paper Digitization and Classification for Abnormality Detection

Adversarial subdomain adaptation method based on multi-scale features for bearing fault diagnosis

Development and Evaluation of Deep Learning Models for Cardiotocography Interpretation

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