further work on methodology

thesis/sections/methodology.tex

@@ -4,6 +4,17 @@
 
 \section{Methodology}\label{sec:methodology}
 
+While prior studies have demonstrated the promise of physiological signals for ovulation detection and phase classification,
+many are limited by small sample sizes, rigid inclusion criteria, or non-transparent methodologies.
+Temperature has emerged as a potentially predictive signal, but existing work often lacks scalability or generalizability.
+This study extends previous approaches by leveraging a large, heterogeneous real-world dataset of high-resolution core body temperature
+readings to develop and evaluate machine learning models for real-time ovulation prediction.
+In addition to model development, special emphasis is placed on evaluating performance across irregular cycles and assessing
+the predictive value of low-noise, high-resolution temperature data.
+
+The following section outlines the methodology used, including data preprocessing,
+feature extraction, input encoding, and model architectures.
+
 % why did I select tft over other methods -> include examples of time series and why I belief a complex model could help
 % you did I apply it
 % implementation details
@@ -368,6 +379,15 @@ fertility probability and ovulation-over indicator.
 Figure~\ref{fig:methodology_transformer_architecture} shows the overall architecture.
 The stacked inputs and outputs indicate batch processing.
 
+Model-specific architectural parameters are:
+\begin{itemize}
+    \item \textbf{Input Length} — Number of time steps included in each input sequence.
+    \item \textbf{Embedding dimension} — Dimensionality of the Transformer internal token representation.
+    \item \textbf{Number of Encoder-Layers} — Number of encoder layers to stack
+    \item \textbf{Number of Attention-Heads} — Number of attention heads to use in each layer
+\end{itemize}
+The specific values and tuning ranges for these parameters are discussed in Section~\ref{subsubsec:hyperparameter_tuning}.
+
 \subsubsection{Temporal Convolution Layer}
 \label{subsubsec:temporal_convolution_layer}
 
@@ -439,7 +459,7 @@ Hyperparameter tuning was divided into two stages:
 (1) tuning of input-related parameters such as resampling rate, and
 (2) tuning of model-specific architectural parameters such as hidden size or attention heads.
 
-\paragraph{Input Parameter Tuning}% \mbox{}\\
+\paragraph{Input Parameter Tuning:}
 This stage involved identifying optimal settings for data preprocessing and input representation.
 Key variables included the resampling rate (temporal resolution) and historical context length (input window size).
 These parameters strongly influence the structure of the input signal and can significantly affect model performance.
@@ -465,9 +485,9 @@ and whether incorporating data from previous cycles improves learning or introdu
     \centering
     \begin{tabular}{l>{\raggedright\arraybackslash}p{0.45\linewidth}>{\raggedright\arraybackslash}p{0.3\linewidth}}
         \toprule
-        \textbf{Parameter} & \textbf{Description} & \textbf{Values Tested} \\
+        \textbf{Parameter} & \textbf{Description}                           & \textbf{Values Tested} \\
         \midrule
-        Window length & Historical context in days (input window size) & 10, 20, 40, 80, 160 \\
+        Window length      & Historical context in days (input window size) & 10, 20, 40, 80, 160    \\
         \bottomrule
     \end{tabular}
     \caption{Input-related hyperparameters for convolutional LSTM and Transformer hybrids.}
@@ -480,10 +500,48 @@ while Table~\ref{tab:input_hyperparameters_conv} lists those for the convolution
 Note that convolutional models do not require an explicit resampling parameter, as they perform learned downsampling internally
 (see Section~\ref{subsubsec:temporal_convolution_layer}).
 
-\paragraph{Model Parameter Tuning}% \mbox{}\\
-Once suitable input configurations were established, model-specific hyperparameters were tuned.
-For LSTM-based models, this included hidden layer size and number of recurrent layers.
-For Transformer models, relevant parameters included the number of attention heads and encoder depth.
+\paragraph{Model Parameter Tuning}
+To identify the most suitable configuration for each model architecture, model-specific hyperparameters were tuned for optimal predictive performance.
+The goal was to balance model complexity and expressiveness in relation to the given input configuration.
+
+\begin{table}[htbp]
+    \centering
+    \setlength{\tabcolsep}{8pt} % adjust column spacing
+    \renewcommand{\arraystretch}{1.2} % more row spacing
+    \begin{tabular}{@{}p{0.28\textwidth}p{0.45\textwidth}p{0.20\textwidth}@{}}
+        \toprule
+        \textbf{Parameter} & \textbf{Description} & \textbf{Values Tested} \\
+        \midrule
+        Hidden Layer Size     & Size of the LSTM hidden layer          & 16, 32, 64, 128, 256, 512 \\
+        Number of LSTM Layers & Number of stacked LSTM layers          & 1, 2, 4                   \\
+        \bottomrule
+    \end{tabular}
+    \caption{Model hyperparameters for the LSTM and convolutional-LSTM hybrid architectures.}
+    \label{tab:lstm_model_hyperparameters}
+\end{table}
+
+
+\begin{table}[htbp]
+    \centering
+    \setlength{\tabcolsep}{8pt} % adjust column spacing
+    \renewcommand{\arraystretch}{1.2} % more row spacing
+    \begin{tabular}{@{}p{0.28\textwidth}p{0.45\textwidth}p{0.20\textwidth}@{}}
+        \toprule
+        \textbf{Parameter}        & \textbf{Description}                 & \textbf{Values Tested}    \\
+        \midrule
+        Embedding Dimension       & Size of the internal token embedding & 16, 32, 64, 128, 256, 512 \\
+        Number of Encoder Layers  & Number of stacked encoder layers     & 1, 2, 4, 8                \\
+        Number of Attention Heads & Number of attention heads per layer  & 1, 2, 4, 8                \\
+        \bottomrule
+    \end{tabular}
+    \caption{Model-related hyperparameters for the Transformer and Convolutional-Transformer-Hybrid architectures.}
+    \label{tab:transformer_model_hyperparameters}
+\end{table}
+
+Tables~\ref{tab:lstm_model_hyperparameters} and~\ref{tab:transformer_model_hyperparameters} summarize the tested hyperparameters and value ranges
+for the LSTM-based and Transformer-based models, respectively.
+Note that the same settings were used for the hybrid models, as their architecture beyond the convolutional front end is structurally identical.
+
 
 \vspace{0.5em}
 We acknowledge that interactions between input and model parameters may influence final model performance,
@@ -501,3 +559,16 @@ joint parameter space in a more efficient and principled manner.
 
 \subsubsection{Baseline Comparisons}\label{subsubsec:baseline_comparisons}
 
+\subsection{Ethical Considerations}\label{subsec:ethical_considerations}
+
+This study was conducted using pseudonymized data collected in accordance with the terms of service and privacy policy of the data provider, VivoSensMedical GmbH (Leipzig, Germany).
+All users whose data were included had consented to the use of their recordings for analytical purposes at the time of data collection.
+
+The study protocol was reviewed and approved by the provider's internal legal and scientific advisory team,
+which is responsible for ensuring ethical and regulatory compliance.
+
+All data used in this study were pseudonymized prior to access.
+No personal identifiers or sensitive metadata were included.
+Additional safeguards were implemented to ensure data confidentiality and integrity,
+including restricted access and use solely for the purposes of model development and evaluation.
+No compensation was provided to participants, as the data were originally collected as part of routine usage under the agreed terms.