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  \title{Deep Residual Learning on Scalograms for Multiclass Fetal State Classification using Expert-Annotated Cardiotocographic Signals}
   \author{Trie Maya Kadarina, Basari and Dadang Gunawan \\
      Dept. of Electrical Engineering, Universitas Indonesia, 16424, Indonesia\\
      guna@eng.ui.ac.id \\
      \AndAuthor
      Abraham Auzan \\
      Elektra Medical Research and Development, PT Elektra Abimantrana Internasional, Indonesia\\
      abraham@telectg.co.id}
  \titleodd{Title of the\ldots}
  \authoreven{N. SurnameofAuthor1 et al.}
  \keywords{cardiotocography, expert validation, fetal state classification, residual network, scalograms}
  \received{June 24, 2013}

  \abstract{Accurate classification of cardiotocographic (CTG) signals plays a critical role in the early detection of fetal health conditions, enabling timely and appropriate medical interventions. A more precise understanding of cardiotocography patterns, particularly in suspicious cases, can help minimize unnecessary interventions and reduce healthcare costs. This study presents a deep learning-based multiclass classification framework for fetal state analysis using expert-annotated CTG data. The dataset consists of fetal heart rate and uterine contraction signals, which are concatenated and transformed into time-frequency representations using Continuous Wavelet Transform (CWT). The resulting scalograms are used as input for a Modified ResNet-based deep learning model to classify fetal states into three categories: normal, suspicious, and pathological. The model achieves an overall accuracy of 78\%, with a precision of 86\%, specificity of 86\%, sensitivity of 77.7\%, and an F1-score of 77.6\%. Stable validation loss indicates good generalization capability. These results demonstrate the feasibility of combining time-frequency signal representations and deep residual networks for robust fetal state classification. The proposed approach offers potential contributions to intelligent clinical decision support systems in prenatal care. 
}
  \abstractSi{Ta študija uporablja kardiotokografske signale, ki združujejo fetalni srčni utrip in kontrakcije maternice v skalogram s pomočjo kontinuirane valovne transformacije. Nastali skalogram služi kot vhod v model globokega učenja za klasifikacijo statusa ploda na podlagi strokovne anotacije.}
  \maketitle
  
\section{Introduction}
Ensuring fetal well-being is critical to maintaining the health and safety of babies during pregnancy and childbirth. The World Health Organization (WHO) reports that more than 800 women die every day from complications of pregnancy and childbirth, especially in areas with limited access to quality health services \cite{ref1}. Furthermore, according to the WHO report, effective monitoring of maternal and fetal health can lower newborn mortality rates by up to 20\% in regions where adequate healthcare services are available \cite{ref2}.

Health care professionals can take precautions to reduce the risk if fetal complications arise from antenatal care evaluations [3]. Cardiotocography (CTG) is a widely used tool for assessing fetal conditions during pregnancy and labor by recording fetal heart rate (FHR) and uterine contraction (UC) signals. A key advantage of CTG is its ability to provide continuous and real-time monitoring, allowing early detection of fetal distress and uterine activity changes \cite{ref4}. By identifying fetal hypoxia, CTG monitoring reduces the risk of newborn asphyxia and lowers perinatal mortality rates \cite{ref5,ref6}.

CTG interpretation follows three main guidelines from NICE (National Institute of Health and Care Excellence), FIGO (International Federation of Gynecology and Obstetrics), and ACOG (American College of Obstetricians and Gynecologists) \cite{ref7}. However, CTG assessment often varies due to subjectivity and differences in expertise among healthcare professionals. Fatigue, stress, complex cases, and time constraints can further hinder accurate and timely decision-making \cite{ref8}.
To address these challenges, artificial intelligence (AI) and machine learning (ML) techniques have been integrated into CTG analysis. ML enhances diagnostic accuracy by detecting disease patterns and risk factors, enabling early fetal distress detection and proactive interventions \cite{ref9,ref10}. AI-driven models allow healthcare providers to analyze large volumes of fetal monitoring data, leading to faster and more informed decisions, while also reducing unnecessary interventions such as cesarean sections \cite{ref11,ref12}.

The classification of the fetal state in CTG is essential for the early detection of fetal conditions, particularly when rapid intervention is required during pregnancy or labor \cite{ref13}. This classification includes normal, suspicious, and pathological states, with suspicious cases requiring closer monitoring due to potential abnormalities such as irregular heart rate variability or decelerations. Detecting suspicious cases early is critical, as misclassification can lead to unnecessary interventions or delayed medical responses, affecting maternal and fetal safety \cite{ref14}. A deeper understanding of borderline CTG patterns can help optimize clinical decisions, reduce unnecessary procedures, and lower healthcare costs.

This study aims to address existing gaps in CTG analysis and presents two key contributions in advancing fetal state classification using deep learning and signal processing:

(1) Multiclass fetal state classification with expert annotations. Unlike previous studies that classify fetuses into only normal and pathological categories, this study introduces a suspicious category, creating a three-class classification (normal, suspicious, pathological). Expert annotations are used instead of relying solely on biochemical indicators like cord blood pH values, ensuring a more clinically relevant and interpretable classification.

(2) Combining FHR and UC signals as input for a TF-modified ResNet-50 model in scalogram-based CTG classification. This study combines FHR and UC signals, transforming them into scalograms using Continuous Wavelet Transform (CWT) to capture temporal and frequency-domain features. A TF-modified ResNet-50 model, enhanced with additional dense layers and dropout mechanisms, is employed to optimize feature extraction, reduce overfitting, and improve generalization for three-class fetal state classification.

\section{Related Works}
Several studies have applied machine learning algorithms to predict fetal abnormalities by extracting features from CTG signals \cite{ref15,ref16}. These studies have primarily focused on classifying fetal conditions into normal and abnormal (pathological) categories, as well as predicting specific complications such as fetal hypoxia and preterm birth \cite{ref17,ref18,ref19,ref20,ref21}. Previous studies have used different labelling approaches based on data availability. Expert annotations involves obstetricians clinically assessing fetal conditions, while biochemical indicators such as cord blood pH and Base Excess (BE) evaluate acid-base balance and hypoxia risk. The Apgar score is also used to validate fetal distress predictions based on newborn health.

Previous studies have classified fetal status into three classes: normal, suspicious, and pathological as shown in Table 1. However, most of these studies are based on traditional machine learning models trained in the UCI Machine Learning Repository dataset \cite{ref22}. These studies applied a variety of algorithms, including Artificial Neural Networks (ANN), Rough Neural Networks, Naïve Bayes, Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), and Light Gradient Boosting Machine (LGBM). Although these models achieve high accuracy (89\%-99\%) \cite{ref23,ref24,ref25,ref26,ref27,ref28}, the dataset has significant limitations, as it contains only 21 static features and lacks the temporal information necessary to capture the dynamic nature of CTG signals. This limitation hinders the ability of the model to fully assess fetal conditions, which depend on time-dependent variations in FHR and uterine contractions for a more accurate and reliable classification.

\begin{table*}[h]
    \centering
    \caption{Summary of Related Studies using UCI CTG Dataset}
    \begin{tabularx}{\textwidth}{|c|X|X|X|X|X|}
    \hline
    Work & Input &  Label Type & Classes & \textbf{Method} & \textbf{Performance} \\ \hline

    \cite{ref23} & 21 statistical features & Expert Annotations & Normal, Suspicious, Pathological & Rough Neural Network & Accuracy = 90.5\% \\ \hline

    \cite{ref24} & 21 statistical features & Expert Annotations & Normal, Suspicious, Pathological & LR, KNN, DT, RF, SVM, and GB & Highest accuracy = 98\% (RF) \\ \hline

    \cite{ref25} & 21 statistical features & Expert Annotations & Normal, Suspicious, Pathological & LR, KNN, RF, and GBM & Highest accuracy = 99\% (RF) \\ \hline

    \cite{ref26} & 21 statistical features & Expert Annotations & Normal, Suspicious, Pathological & RF, Naïve Bayes, SVM & Highest accuracy = 99.93\% (RF) \\ \hline

    \cite{ref27} & 21 statistical features & Expert Annotations & Normal, Suspicious, Pathological & RF, LR, DT, SVM, Voting Classifier, KNN & Highest accuracy = 97.51\% (RF) \\ \hline

    \cite{ref28} & 21 statistical features & Expert Annotations & Normal, Suspicious, Pathological & ANN, LSTM, XGB, SVM, KNN, LGBM, RF & Highest accuracy = 89--99\% (XGB, SVM, KNN, LGBM, RF) \\ \hline

    \end{tabularx}
\end{table*}

Deep learning offers a more robust approach by eliminating the need for manual feature selection and enabling automatic extraction of important features directly from raw signals \cite{ref29}. Several studies have explored deep learning for CTG classification \cite{ref17,ref19,ref29,ref30,ref31}. Despite these developments, most studies only use FHR signals, ignoring UC signals, which play a crucial role in assessing fetal response to labour stress. The combination of FHR and UC provides a more comprehensive view of fetal well-being, particularly in detecting late or variable decelerations, which are early indicators of fetal distress and hypoxia \cite{ref16,ref32}.  Table 2 shows the summary of related work using CTU-UHB dataset. 

\begin{table*}[h!]
\centering
\caption{Summary of Related Studies using CTU-UHB Dataset}
\begin{tabularx}{\textwidth}{|C|C|C|X|X|X|}
\hline
Work & Input & Label Type & Classes & Method & Performance \\ \hline

\cite{ref17} & FHR Signals & Delivery mode \& pH & Normal (vaginal delivery), Pathological & 1DCNN-MLP & Sensitivity=80\%, Specificity=79.1\% \\ \hline

\cite{ref19} & FHR Signals & pH & Normal, Hypoxia & Ensemble Learning, CNN, DenseNet & CNN: F1=0.9 (Normal), 0.23 (Hypoxia); Bagging Tree + NB: F1=0.76 \& 0.45 \\ \hline

\cite{ref29} & FHR Signals & pH & Normal, Distressed & TF Morse Wavelet, ResNet & Accuracy=98.7\%, Sens=97\%, Spec=100\% \\ \hline

\cite{ref30} & FHR Signals  & pH & Normal, Hypoxia & DenseNet & Precision=50\%, Sensitivity=43\%, F1=46\% \\ \hline

\cite{ref31} & FHR Signals & pH & Normal, Abnormal & SE-ResNet50, XGBooster & Accuracy=96.34\%, Precision=96.7\%, Sens=97.3\%, Spec=96\% \\ \hline

\cite{ref33} & FHR and UC & pH \& BE & Normal, Abnormal & TF, ECSVM & Sens=85.2\%, Spec=66.1\% \\ \hline

\cite{ref34} & FHR and UC & pH & Normal, Abnormal & CNN, RNN & Accuracy=95.15\%, Sens=96.2\%, Spec=94.09\% \\ \hline

\cite{ref35} & FHR and UC & Apgar Score \& pH & Normal, Abnormal & CTG-Net & F1 Score=67\% \\ \hline

\cite{ref36} & FHR and UC & pH & Normal, Mild Hypoxia, Severe Hypoxia & 2D-CNN & Accuracy=70\% \\ \hline

\end{tabularx}
\end{table*}


A number of research employ both FHR and UC signals with varying outcome predictions utilizing various signal processing approaches. For instance,  Zeng, et al. classified fetal state in normal and abnormal classes based on pH values and Base Excess (BE) values used to assess the oxygenation status and acid-base balance of the fetus for indications of hypoxia. They used the Time-Frequency (TF) and Ensemble Cost-Sensitive Support Vector Machine (ECSVM) features which resulted in a sensitivity of 85.2\%, a specificity of 66.1\%, and a quality index of 75.0\% \cite{ref33}. Liang et al. utilized a CNN-RNN model to detect fetal hypoxia based on pH values, achieving an accuracy of 95.15\% \cite{ref34}. Similarly, Ogasawara et al. developed CTG-Net, a CNN-based model that classifies fetal conditions using Apgar scores and umbilical artery pH, resulting in an F1 score of 67\% \cite{ref35}. Meanwhile, Saini et al. applied a 2D-CNN model to classify fetal conditions into normal, mild hypoxia, and severe hypoxia, obtaining an accuracy of 70\% \cite{ref36}. 

These studies demonstrate the potential of deep learning to improve fetal state classification, but challenges remain to improve accuracy and ensure clinical reliability. Existing research mainly focuses on binary classification, distinguishing between normal and abnormal cases while relying on biochemical markers such as pH levels or Apgar scores for labeling. However, suspicious cases, which lie between normal and pathological conditions, are often ignored, potentially resulting in either unnecessary interventions or delayed responses to actual risks. Additionally, most studies process CTG signals in a raw time-series format, which may not fully capture the frequency-domain characteristics of fetal heart rate fluctuations. The use of scalograms (TF representations) offers richer feature extraction, enhancing deep learning performance. To address these gaps, a multiclass classification framework is developed by incorporating both FHR and UC signals and leveraging time-frequency representations for enhanced feature extraction.


\section{Methods}
 Expert labeling was chosen in this study over physiological metrics like cord blood pH value as it considers multiple clinical factors for a more comprehensive fetal assessment. By using both FHR and UC for a comprehensive assessment and employing multiclass classification, this study enhances the detection of the suspicious category for early risk identification. This study employs a systematic methodology as shown in Figure 1 to classify fetal conditions using CTG signals and deep learning. The research methodology consists of three main steps: data selection and annotation analysis, data preprocessing and feature extraction, and model training and evaluation. In the first step, CTG data is collected from the publicly available CTU-UHB CTG dataset  with expert-validated labels \cite{ref37}. To assess annotation consistency, the Pearson correlation matrix is applied before finalizing the dataset for model training.
 
\begin{figure*}[h] % '*' for two columns
  \begin{center}
    \epsfig{file=fig1.eps, width=160mm} % 
  \end{center}
  \caption{Proposed Methodology for CTG Classification.}
\end{figure*}

The second step, data preprocessing and feature extraction, involves cleaning and augmenting the CTG signals to enhance data quality. The CTG signals, which consist of FHR and UC signals, are then concatenated and converted into Time-Frequency (TF) representations using Continuous Wavelet Transform (CWT) for feature extraction, generating scalogram images that serve as input for the deep learning model.

In the final step, model training and evaluation, the scalogram images, along with expert annotations, are split into training (80\%) and validation (10\%) sets, with a separate portion reserved for testing. A modified ResNet-50 model is developed with additional dense and dropout layers to improve classification performance, along with focal loss and class weighting to address class imbalance. The model is trained  and optimized through hyperparameter tuning. Evaluation is conducted followed by a comparison with other models. The final analysis identifies key classification challenges and provides insights for further model improvement, aiming to enhance early detection of fetal health conditions for better clinical decision-making. 
 
 The following describes the algorithm used for CTG signal classification.
 
 \begin{algorithm}
    \caption{CTG\_Classification}
    \begin{algorithmic}[1]
        \State \textbf{Input:} Selected Raw CTG signals (FHR, UC) with expert-validated labels
        \State \textbf{Output:} Classified fetal state (Normal, Suspicious, Pathological)
        
        \Statex
        \State \textbf{Step 1: Preprocessing Data}
        \State Remove noise from FHR and UC signals
        \State Interpolate missing values
        \State Detect and remove outliers
        \State Perform data augmentation using time slicing
        
        \Statex
        \State \textbf{Step 2: Concatenation}
        \State Merge FHR and UC signals into a single input
        
        \Statex
        \State \textbf{Step 3: Continuous Wavelet Transform (CWT)}
        \State Convert concatenated signal into a scalogram representation
        
        \Statex
        \State \textbf{Step 4: Model Training (Modified ResNet-50)}
        \State Initialize ResNet-50 model with additional Dense and Dropout layers
        \State Compile model with Adam optimizer and categorical cross-entropy loss
        \State Train using 80\% dataset, validate using 10\%
        
        \Statex
        \State \textbf{Step 5: Model Evaluation}
        \State Test model on 10\% dataset
        \State Compute Accuracy, Precision, Sensitivity, F1-Score
        \State Compare results with baseline models (CNN, MobileNet, etc.)
        
        \Statex
        \State \textbf{Step 6: Classification Output}
        \State Predict fetal state: Normal, Suspicious, or Pathological
        \State Return classification results
        
        \Statex
        \State \textbf{End Algorithm}
    \end{algorithmic}
\end{algorithm}

\subsection{Dataset}
We utilized the public CTG dataset from the Czech Technical University (CTU), containing 552 CTG recordings [37], available at: https://physionet.org/content/ctu-uhb-ctgdb/1.0.0/. This CTG data was annotated by nine obstetricians following FIGO guidelines at each evaluation step as shown in Figure 2. The CTG dataset annotation is available at https://people.ciirc.cvut.cz/~spilkjir/data.html\cite{ref38}. In the CTU-CHB Intrapartum Cardiotocography dataset, the collected signals mainly focused on the first stage labour and second stage labour phases of the labour process. The first stage labour includes the opening of the cervix from the beginning of contraction to the full opening of the cervix, reflecting variations in the fetal heart rate as the intensity of contractions increases. The dataset also includes the second phase of labor, when the cervix has fully opened and the birth process begins, showing an FHR response to stronger contractions as the mother straining.

\begin{figure}[h]
  \begin{center}
    \epsfig{file=fig2.eps,width=50mm}
  \end{center}
  \caption{CTG evaluation step in CTG CTU-UHB dataset.}
\end{figure}

In this study, data annotations were determined based on the majority voting technique of nine experts on each CTG recording. A technique in data labeling in which the final decision is determined based on the label that is most chosen among several experts \cite{ref39}. This technique is often used when there are multiple evaluations from different sources or individuals, for example, in a situation where a number of experts label the data, and we need to determine a single consensus label to use. 
Annotations are evaluated at each step, followed by a consistency analysis to identify the step with the highest labeling agreement. This is done by calculating the Pearson correlation for each pair of experts among the nine existing experts, resulting in a 9×9 correlation matrix where each element represents the agreement between two experts. Since the matrix is symmetrical, it captures all possible expert pairings. The evaluation step with the highest correlation is selected, ensuring more consistent and reliable labeling for the final dataset.

\subsection{Preprocessing Steps}
Preprocessing techniques in this study include data cleaning, augmentation, and signal concatenation. The data cleaning process consists of several stages. First, all zero values in the dataset are replaced with NaN (Not a Number) to mark potentially invalid or missing values, as a value of zero may indicate unrecorded or incorrect data. If NaN values occur for more than 15 consecutive seconds, they are removed to prevent long segments of missing data from affecting the analysis.
Outlier detection is then performed to identify extreme values that deviate significantly from the majority of the data, as such anomalies can impact the analysis. FHR signals outside the range of 50–200 bpm are considered outliers \cite{ref29}, while UC signals are classified as outliers if they exceed the 0–100 mmHg range \cite{ref40}. To handle missing values, linear interpolation is applied, assuming a linear change between two known data points. Additionally, spike detection is used to identify and correct sudden, unrealistic changes in the data. For FHR signals, a spike is defined as a change exceeding 25 bpm [29], while for UC signals, the threshold is set at 40 mmHg \cite{ref40}. These sudden variations, likely caused by measurement errors or interference rather than actual physiological changes, are corrected using cubic spline interpolation to ensure smooth transitions between valid data points.

Data cleaning is applied to FHR and UC signals across all categories. A 30-minute segment of selected evaluation step from the CTU-UHB dataset is used, with data augmentation applied to the imbalanced suspicious and pathological classes using a 5- and 10-minute slicing technique. The augmented data is then split into training, validation, and testing sets in an 80:10:10 ratio.

The next step is to concatenate the two FHR and UC signals. FHR and UC signals are synchronized based on time. If there is a difference in sampling times, interpolation is performed to align the two signals so that they have a uniform timestamp. The FHR and UC signals are concatenate into one multivariate dataset where each time point will have two values: one for FHR and one for UC.

\subsection{Continuous Wavelet Transform}
The next stage is the signal transformation into the time-frequency domain using continuous wavelet transformation (CWT). Continuous Wavelet Transform (CWT) is a method for analyzing signals by breaking them into different parts based on scale or frequency \cite{ref41}. CWT is very useful for signals whose frequency changes over time. CWT works by shifting and changing the size of the mother wavelet along the signal and seeing how similar the signal is to the wavelet at each point. The result is numbers (coefficients) that show how similar the signal is to a wavelet at a given scale and position. In this way, changes in the frequency of the signal overtime can be seen, providing a more complete picture compared to traditional Fourier analysis, which assumes the signal does not change. The mathematical equation for the wavelet function (1) is as follows \cite{ref42}:

\begin{equation}
    \psi_{a,b}(t) = \frac{1}{\sqrt{a}} \psi \left( \frac{t - b}{a} \right)
\end{equation}

With \( a \) representing the scaling parameter for dilation and \( b \) representing the moving parameter for translation across the signal location. CWT follows two properties which are represent in equation (2) and (3) \cite{ref43}:

\begin{equation}
    \int_{-\infty}^{\infty} \psi(t) \, dt = 0
\end{equation}

\begin{equation}
    \int_{-\infty}^{\infty} \left[ \psi(t) \right]^2 dt = 0
\end{equation}

For CWT, the mathematical equation \(C(a,b)\) is obtained by integrating the input function with the wavelet which is stated in equation (4) \cite{ref42}: 

\begin{equation}
    C(a,b) = \int_{-\infty}^{\infty} f(t) \frac{1}{\sqrt{|a|}} \psi \left( \frac{t - b}{a} \right) dt
\end{equation}

In this study, a Morse wavelet in the frequency domain is used with the following formulation, as shown in equation  (5) \cite{ref43}:

\begin{equation}
    \psi_{\beta,\gamma}(\omega) = U(\omega) a_{\beta,\gamma} \omega^{\beta} e^{\omega^{\gamma}}
\end{equation}

\( \beta \) and \( \gamma \) are parameters that control the shape of the wavelet. \( \beta \) controls the shape and width of the wavelet while \( \gamma \) controls the asymmetry or shift of the wavelet shape. \( \omega \) is a variable frequency in the frequency domain, which indicates the frequency analyzed at each point in time. The central frequency ( \( \omega_o \)) of the wavelet that regulates the number of oscillations. If we put Morse wavelets into the CWT formula, then the formula becomes as shown in equation (6) \cite{ref43}:

\begin{equation}
    \resizebox{0.9\hsize}{!}{$
    C(a,b) = \int_{-\infty}^{\infty} f(t) \frac{1}{\sqrt{|a|}} 
    U \left( \frac{t - b}{a} \right) a_{\beta,\gamma} 
    \left( \frac{t - b}{a} \right)^{\beta} 
    e^{- \left( \frac{t - b}{a} \right)^{\gamma}} 
    e^{i \omega_o \frac{t - b}{a}} dt
    $}
\end{equation}

Equation (7) shows how CWT uses Morse wavelets for scale and frequency analysis of signals. Appropriate wavelet parameters are selected to obtain optimal scalogram representation. In this study, \( \beta \)=20 and \( \gamma \)=3 was selected. Sampling frequency 4Hz with a signal length of 7200 and voice per octave of 12.The combination of \( \beta \)=20 and \( \gamma \)=3 was selected to ensure an optimal balance, allowing the wavelet to maintain high-frequency resolution while preserving sufficient time resolution. With a sampling frequency of 4 Hz, this combination preserves both low and high-frequency details while retaining essential information.

CWT can be used to decompose one-dimensional (1D) signals into two-dimensional (2D) \cite{ref44}. The 1D signal is decomposed into wavelet coefficients, capturing similarities at different scales and positions. This produces a 2D scalogram, where time and frequency are represented, and color indicates coefficient amplitude. Scalograms enhance feature and frequency visualization, revealing details not apparent in 1D representations \cite{ref44}.

\subsection{Residual Network}
Residual Network (ResNet), introduced in 2015 by Kaiming He et al. \cite{ref45}, is a convolutional neural network (CNN) architecture that is significant in deep learning, particularly for image recognition and computer vision. ResNet overcomes vanishing gradients and degradation issues by using residual learning, enabling deeper networks to train more effectively \cite{ref46}.

As input is a scalogram image with an input shape of 224×224×3. Adam optimizer is used with a learning rate of 0.001. The model uses sparse categorical cross-entropy to compute loss in multi-class classification, with three classes: normal, suspicious, and pathological. To overcome class imbalance, focal loss and class weight are used. Focal loss is a variant of the cross entropy loss function designed to focus on examples that are difficult to classify [47]. In class imbalance, models tend to focus on majority classes, neglecting harder minority cases. Focal loss mitigates this by down-weighting easy examples and emphasizing difficult ones, improving learning for minority classes. Equation (7) provides the focal loss formula \cite{ref47}:
\begin{equation}
    FL(p_t) = -\alpha_t (1 - p_t)^{\gamma} \log(p_t)
\end{equation}

Where \( p_t \) is the correct prediction probability for a particular class, \( \alpha_t \) is a weighing factor for minority classes (e.g., giving more weight to minority classes), and \( \gamma \) is a parameter that controls how much attention is focused on a difficult instance (the larger the \( \gamma \), the greater the focus on the difficult example). Focal loss helps the model learn examples from more difficult minority classes by giving more weight to the errors from those examples. In this study, we used \( \alpha\)=0.25 and \( \gamma \)=2 based on the consideration that these values can perform well in various cases \cite{ref48}. 

Class weight is a technique that involves giving more weight to a minority class in a standard loss function such as cross entropy loss \cite{ref49}.This approach adjusts weights for sparse classes, ensuring the model treats minority class predictions as equally important. Class weights are computed using equation (8) \cite{ref49}. 

\begin{equation}
    \omega_i = \frac{N}{n_i}
\end{equation}

Where \( \omega_i \) is the weight for the \( i \) class, \( N \) is the total number of samples, and \( n_i \) is the number of samples in class \( i \).  Less frequent classes receive higher weights to balance the loss function, ensuring the model considers minority classes. To further improve performance, ResNet-50 is modified with dropout layers to prevent overfitting and dense layers for better classification. Figure 3 illustrates the architecture of the modified ResNet-50. 

\begin{figure}[h!]
 \begin{center}
    \epsfig{file=fig3.eps,width=65mm} % figure1.eps is a standalone file in EPS format
  \end{center}
  \caption{Modified ResNet-50 architecture.}
\end{figure}

Model performance evaluation involves a set of metrics that can measure certain aspects of the model's performance. Some common evaluation metrics used which are accuracy, precision, sensitivity, specificity, and F1-score \cite{ref50}. Accuracy shown in equation (9), measures the proportion of the number of correct predictions to the total number of samples \cite{ref50}. 

\begin{equation}
    \text{Accuracy} = \frac{\text{TP} + \text{TN}}{\text{TP} + \text{TN} + \text{FP} + \text{FN}}
\end{equation}

TP is true positives (number of correct positive predictions), and FP is false positives. TN is true negative (number of correct negative predictions), FP is false positive (number of false positive predictions), and FN is false negative (number of false negative predictions). Precision shown in equation (10), measures the proportion of positive predictions that are truly positive.

\begin{equation}
    \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}}
\end{equation}

Specificity shown in equation (11), measures the proportion of negative classes that are correctly classified as negative \cite{ref50}. 

\begin{equation}
    \text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}}
\end{equation}

Sensitivity measures the model's ability to identify all positive samples. The formula is in (12) \cite{ref50}:

\begin{equation}
    \text{Sensitivity} = \frac{\text{TP}}{\text{TP} + \text{FN}}
\end{equation}

F1-score is the harmonic means of precision and sensitivity, providing a balance between the two. The formula is shown in (13) \cite{ref50}: 

\begin{equation}
    \text{F1-score} = 2 \times \frac{\text{Precision} \times \text{Sensitivity}}{\text{Precision} + \text{Sensitivity}}
\end{equation}

\section{Results}
This section presents the findings from each stage of the research methodology that has been conducted.
\subsection{Data Selection and Annotation Analysis Results}

In the CTG-UHB dataset, annotations are assigned as follows: 1 (normal), 2 (suspicious), 3 (pathological), and -1 (uninterpretable). Table 3 presents the data distribution from the majority voting of nine experts across three evaluation steps. The sample count varies at each step, reflecting differences in expert agreement and data interpretation. The large number of uninterpretable cases in evaluation step 3 indicates that more cases were considered ambiguous or difficult to categorize. Since this study only requires normal, suspicious, and pathological labels, the uninterpretable category (-1) is ignored. A correlation matrix is calculated for each step, as shown in Figure 4 for step 1, excluding the uninterpretable label. 

\begin{table}[h]
  \centering
  \caption{Data distribution based on majority votes from nine experts}
  \begin{tabular}{|c|c|c|c|c|}
    \hline
    \multirow{2}{*}{Label} & \multirow{2}{*}{Criteria} & \multicolumn{3}{c|}{CTG Evaluation} \\ \cline{3-5}
     & & Step 1 & Step 2 & Step 3 \\ \hline
    1  & Normal          & 296 & 229 & 127 \\ \hline
    2  & Suspicious      & 220 & 251 & 153 \\ \hline
    3  & Pathological    &  30 &  68 &  57 \\ \hline
   -1  & Uninterpretable &   6 &   4 & 215 \\ \hline
  \end{tabular}
\end{table}

\begin{figure*}[h!]
  \begin{center}
    \epsfig{file=fig4.eps,width=100mm} % figure1.eps is a standalone file in EPS format
  \end{center}
  \caption{Expert correlation matrix step 1 excluding uninterpretable label (-1).}
\end{figure*}

To evaluate the consistency of expert annotations, correlation values are analyzed across different evaluation steps. The average correlation values for each step are: step 1=0.62, step 2=0.59, and step 3=0.54. For this reason, CTG data from step 1 will be used in this study because it has a high correlation between experts that shows strong agreement, which means that the labels on this data are more consistent and reliable. Stable training data and minimal noise are essential for machine learning models because it makes it easier for models to find accurate patterns. The labels assigned to the data in evaluation step 1 tend to be uniform. This reduces the risk of ambiguity in the training data, so the model can learn from clearer patterns and not be affected by inconsistent labels.

\subsection{Data Preprocessing and Feature Extraction Results}

The data cleaning was carried out on both FHR and UC signals in all categories, including normal, suspicious, and pathological. Figure 5 shows one of the results of the data cleaning stage in the suspicious category. It shows how the signals are efficiently refined by the data cleaning process, which eliminates artifacts and irregularities that can obstruct precise analysis.

\begin{figure*}[h!] % '*' for two columns
  \begin{center}
    \epsfig{file=fig5.eps, width=140mm} % 
  \end{center}
  \caption{Example of cleaned FHR and UC signals from the suspicious class.}
\end{figure*}

Following the data cleaning process, data augmentation was applied to address the imbalance among the normal, suspicious, and pathological classes. By utilizing the slicing augmentation technique with shifts of 5 and 10 minutes, additional samples were generated for the minority class. As a result, after preprocessing, the dataset from evaluation step 1 was balanced to include 280 normal, 203 suspicious, and 86 pathological samples. The augmented dataset was then split into training (80\%), validation (10\%), and testing (10\%) sets for model evaluation. Therefore, according to equation (8), the class weight for the normal class was 0.677, the suspicious class was 0.935, and the pathological class was 2.207.

Once the dataset was cleaned and balanced, the FHR and UC signals were concatenated to form a multivariate dataset, where each time point contains two values: one for FHR and one for UC. This transformation enhances the temporal relationship between the signals, allowing the model to better capture patterns across both variables. The concatenation result is shown in Figure 6. The combined FHR and UC signals are then transformed into scalogram images using Continuous Wavelet Transform (CWT), providing a TF representation for enhanced feature extraction. This enables deep learning models to analyze temporal variations effectively. Figure 7 presents an example of the resulting scalogram.

\begin{figure}[h!]
 \begin{center}
  \epsfig{file=fig6.eps,width=80mm} % figure1.eps is a standalone file in EPS format
  \end{center}
  \caption{Example of results of concatenation FHR and UC signals.}
\end{figure}

\begin{figure}[htb]
 \begin{center}
    \epsfig{file=fig7.eps,width=65mm} % figure1.eps is a standalone file in EPS format
  \end{center}
  \caption{Scalogram representation of the concatenated FHR and UC signals.}
\end{figure}

\subsection{Model Training and Evaluation Results}

We conducted several experiments using Google Colab Pro with an A100 GPU and 62.8 GB of RAM. 
The modified ResNet-50 outperformed the original model, achieving 78\% accuracy, 86\% precision, 86\% specificity, 77.7\% sensitivity, and an F1-score of 77.6\%, compared to the original ResNet-50, which achieved 71\% accuracy, 71\% precision, 83.5\% specificity, 74\% sensitivity, and an F1-score of 72\%. Detailed metrics for each class are shown in Figure 8. 

\begin{figure*}[h!] % '*' for two columns
  \begin{center}
    \epsfig{file=fig8.eps, width=160mm} % figure2.eps is a standalone file in EPS format
  \end{center}
  \caption{Classification performance metrics per class.}
\end{figure*}

Figure 9, 10 and 11 display the simulation results of ResNet-50 and the modified model. The modified ResNet-50 demonstrates better generalization, achieving a more balanced training (60-65\%) and validation accuracy with a stable validation loss, reducing overfitting. In contrast, the original ResNet-50 exhibits overfitting, with training accuracy nearing 100\%, while validation accuracy remains low (50-60\%) and fluctuates. Both models perform well in classifying normal and pathological cases but struggle with the suspicious class. 

\begin{figure*}[h!]
 \begin{center}
    \epsfig{file=fig9.eps,width=120mm} % figure1.eps is a standalone file in EPS format
  \end{center}
  \caption{Confusion matrix (a) ResNet-50 (b) Modified ResNet-50.}
\end{figure*}

\begin{figure*}[h!] % '*' for two columns
  \begin{center}
    \epsfig{file=fig10.eps, width=160mm} % figure2.eps is a standalone file in EPS format
  \end{center}
  \caption{Training and validation accuracy and loss of ResNet-50 model.}
\end{figure*}

\begin{figure*}[h!] % '*' for two columns
  \begin{center}
    \epsfig{file=fig11.eps, width=160mm} % figure2.eps is a standalone file in EPS format
  \end{center}
  \caption{Training and validation accuracy and loss of Modified ResNet-50 model.}
\end{figure*}

In addition, experiments were also carried out on CTG data and labeling at different evaluation steps using the modified ResNet-50 model with the results shown in Figure 12. The model achieves the highest accuracy (78\%) at evaluation step 1, with strong precision (86\%) and specificity (86\%), while Step 3 shows the lowest performance across all metrics, with an accuracy of 50\% and an F1-score of 49.4\%. When combining multiple steps, the performance is slightly improved, with the 1,2 combination yielding 62.7\% accuracy and the 1,2,3 combination resulting in 53\% accuracy. These results suggest that evaluation step 1 provides the most consistent and accurate classification.

\begin{figure*}[h!] % '*' for two columns
  \begin{center}
   \epsfig{file=fig12.eps, width=160mm} % 
  \end{center}
  \caption{Performance of the modified ResNet-50 Model on CTG Data at each evaluation step.}
\end{figure*}

The performance of the modified ResNet-50 model is also compared to other deep learning models using CTG evaluation step 1 data with the same number of epochs (100 epochs). The results are shown in Table 4. It can be seen that the modified ResNet-50 provides the best performance in classification, achieving the highest accuracy (78\%), high precision (86\%), and strong specificity (92\%). This model is followed by the original ResNet-50, which also gives good results but still loses to its modified version. EfficientNet features a fairly good balance of performance with competitive precision, sensitivity, and F1-Score metrics, making it an efficient alternative for this task. In contrast, CNN and SqueezeNet showed low performance, indicating incompatibility with this classification task.

\begin{table*}[ht]
\centering
\caption{Comparison of Other Deep Learning Model Performance}
\begin{tabularx}{\textwidth}{|x|x|x|x|x|x|}
\hline
Model & Accuracy (\%) & Precision (\%) & Specificity (\%) & Sensitivity (\%) & F1-Score (\%)\\
\hline
CNN            & 58   & 58.3  & 73.2  & 58.2  & 58.2  \\
\hline
DenseNet       & 64   & 63.8  & 76    & 64    & 61.2  \\
\hline
MobileNet      & 60.8 & 66.3  & 77.4  & 60.9  & 63    \\
\hline
EfficientNet   & 67.7 & 68.9  & 78.4  & 67.7  & 67.2  \\
\hline
SqueezeNet     & 50.6 & 55    & 71.9  & 50.6  & 52    \\
\hline
ResNet-50      & 71   & 71    & 83.5  & 74    & 72    \\
\hline
\textbf{Modified ResNet-50} & \textbf{78} & \textbf{86} & \textbf{86} & \textbf{77.7} & \textbf{77.6} \\
\hline
\end{tabularx}
\end{table*}


To strengthen the validation of the proposed approach, Table 5 presents a comparative analysis with previous studies that have used the same dataset (CTU-UHB) and extracted both CTG signals (FHR and UC). The studies by Zeng et al.\cite{ref33}, Liang et al.\cite{ref34}, and Ogasawara et al. \cite{ref35}. primarily classify CTG outcomes into normal and abnormal categories, relying on annotations such as pH, Base Excess, and Apgar scores. While Liang et al. achieve the highest accuracy (95.15\%) and F1-score (95.20\%) using a 1DCNN-RNN (BiGRU) approach, other models such as DNN (CTG-net) and 2D CNN show moderate performance. The proposed TF-modified ResNet-50 model classifies CTG signals into three categories (normal, suspicious, pathological) based on expert annotations following FIGO standards. Despite handling a more complex classification task, the model achieves 78\% accuracy and a 77.6\% F1-score, demonstrating its effectiveness compared to previous methods.

\begin{table*}[h] 
    \centering
    \caption{Comparison of model performance with related studies}
        \begin{tabularx}{\textwidth}{|c|X|X|X|X|}
            \hline
            Work & Outcome & Annotations & Method & Performance\\ 
            \hline
            Zeng et al.\cite{ref33} & normal \& abnormal & pH \& Base Excess (BE) & TF and ECSVM & Quality index= 75.0\% \\ 
            \hline
            Liang et al.\cite{ref34} & normal \& abnormal & pH & 1DCNN-RNN (BiGRU) & Accuracy= 95.15\%, F1-score= 95.20\% \\ 
            \hline
            Ogasawara et al.\cite{ref35} & normal \& abnormal & Apgar Score \& pH & DNN (CTG-net) & F1-score= 67\% \\ 
            \hline
            Saini et al.\cite{ref36} & normal hypoxia, mild hypoxia and severe hypoxia & pH & 2D CNN & Accuracy= 70\% \\ 
            \hline
            Current Work & normal, suspicious, pathological & 9 experts (FIGO standard) & TF-Modified ResNet-50 & Accuracy= 78\%, F1-score= 77.6\% \\ 
            \hline
        \end{tabularx}
\end{table*}

\section{Discussion}
The results of this study highlight the effectiveness of the TF-modified ResNet-50 model in classifying CTG signals, particularly when trained using data from evaluation step 1. The majority voting technique applied by nine experts at different evaluation steps resulted in varying degrees of agreement, as reflected in the correlation analysis. Evaluation step 1 demonstrated the highest correlation (0.62) among experts, indicating a more consistent and reliable labeling process. In contrast, evaluation step 3 had the lowest correlation (0.54) and a significantly higher number of uninterpretable cases, suggesting increased uncertainty and stricter evaluation criteria. This finding supports the decision to use evaluation step 1 data for training, as it provides a more stable dataset with minimal noise, allowing the model to learn clearer patterns.

The data preprocessing steps, including data cleaning, augmentation, and signal concatenation, played a crucial role in improving model performance. Data augmentation with 5 and 10 minute slicing shifts effectively addressed the imbalance among normal, suspicious, and pathological classes, ensuring that the model received sufficient training samples across all categories. The transformation of FHR and UC signals into scalogram images using CWT further enhanced feature extraction, allowing the deep learning model to analyze temporal variations more effectively.

The model training and evaluation results show that the modified ResNet-50 model outperformed the original ResNet-50 and other deep learning models. With 78\% accuracy, 86\% precision, 86\% specificity, 77.7\% sensitivity, and a 77.6\% F1-score, the modified model demonstrated improved classification performance. The additional dropout and dense layers improved generalization, reducing overfitting while maintaining a good balance between training and validation accuracy. The confusion matrix analysis (Figure 9) revealed that both ResNet-50 models performed well for normal and pathological classes but struggled with the suspicious class, indicating the inherent difficulty in distinguishing borderline cases. This limitation suggests that further optimization, such as enhanced feature extraction, data augmentation, or hybrid modeling approaches, is needed to improve the classification of the suspicious category.

The comparison with previous studies further reinforces the effectiveness of the proposed approach. Unlike previous works that primarily classify CTG signals into normal and abnormal categories, this study introduces a more granular classification into normal, suspicious, and pathological classes based on expert annotations following FIGO standards. Although the 1DCNN-RNN (BiGRU) model by Liang et al. achieved the highest accuracy (95.15\%) and F1-score (95.20\%) \cite{ref34}, it was designed for a binary classification task, making direct comparisons less relevant. Beyond binary classification, other studies, such as Saini et al. \cite{ref36}, have explored multi-class approaches to fetal state classification. Both the proposed work and Saini et al. classify fetal conditions into three categories. With a TF-modified ResNet-50 architecture, it extracts features more effectively than the 2D CNN used by Saini et al. As a result, the proposed model achieves higher accuracy (78\%) and F1-score (77.6\%), outperforming Saini et al.’s 70\% accuracy, demonstrating better robustness and clinical applicability.

Although the proposed model demonstrates improved performance in multiclass classification, further analysis is needed to understand the factors influencing its effectiveness and areas for improvement. These findings underscore the crucial role of expert consensus in ensuring high-quality training data for deep learning models. Higher agreement among experts, as observed in Step 1, leads to more reliable labels, which in turn improves the model's ability to generalize well on unseen data. The results also indicate that deep learning models may require further optimization to improve their ability to classify the suspicious class, possibly through more targeted data augmentation techniques or hybrid modeling approaches that integrate clinical domain knowledge.

\section{Conclusion} % conclusion and discussion; just discussion etc.
In this study, fetal states were classified as normal, suspicious, or pathological based on annotations from nine experts in accordance with FIGO standards. Both CTG signals (FHR and UC) were concatenated into a single scalogram image. The modified ResNet-50 model with added dense and dropout layers performs well in classifying normal and abnormal signals but struggles with suspicious cases. The F1-score for each class is as follows: 83\% for normal, 62.5\% for suspicious, and 87.5\% for pathological. Overall, this model achieves  an accuracy value of 78\%, precision of 86\%, specificity of 86\%, sensitivity of 77.7\% and F1-score of 77.6\%. The model also demonstrated stable validation loss, indicating good generalization to new data.

Following the promising results, this study highlights areas for further enhancement. While data augmentation helped balance class distribution, optimizing the classification of suspicious cases remains an important focus. Future work can refine augmentation techniques, integrate additional data for this class, and implement more consistent labeling methods. Additionally, advancements in model architecture, external validation, and augmentation strategies could further improve the model’s reliability and clinical applicability.

\subsection*{Acknowledgement}
We sincerely thank Dr. Ari Waluyo, Sp.OG, for his valuable insights and feedback, which greatly contributed to the development of this research. We also appreciate the support from the Faculty of Engineering, Universitas Indonesia, through the Seed Funding research grant program (NKB-2614/UN2.F4.D/PPM.00.00/2023).


% bibliography style is not predefined; authors can apply their own,
% but it should be sent to matjaz.gams@ijs.si with the accepted paper


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