temperature-based-fertility-prediction-thesis/thesis/sections/background.tex

%! Author = alex
%! Date = 3/7/25


\section{Background}\label{sec:background}

\subsection{Physiological Background}\label{subsec:physiological_background}

\subsubsection{Menstrual Cycle}\label{subsec:menstrual_cycle}
The menstrual cycle describes the physiological changes in the female body that prepare it for pregnancy.
It is divided into two phases: the \textbf{follicular phase} and the \textbf{luteal phase}.
\\
During the follicular phase, the ovarian follicles mature, and the endometrium (the inner lining of the uterus) thickens
in preparation for a potential implantation of a fertilized egg.
Around day 14 of a typical cycle, ovulation occurs, marking the transition to the luteal phase.
Ovulation refers to the rupture of the mature ovarian follicle and the release of an egg cell into the fallopian tube.
Figure~\ref{fig:background_basic_female_reproductive_system} illustrates the female reproductive system,
including the ovaries and the fallopian tubes.
\\
\begin{figure}
    \centering
    \includegraphics[width=0.4\textwidth]{background_female_reproductive_organs}
    \caption{The basic female reproductive system~\cite{wikimedia_commons_basic_2019}.}
    \label{fig:background_basic_female_reproductive_system}
\end{figure}
Ovulation is triggered by a surge in \textbf{luteinizing hormone (LH)}
and \textbf{follicle-stimulating hormone (FSH)}, following a peak in estradiol levels.
As ovulation occurs, estradiol levels drop, progesterone levels begin to rise and a slight increase in body temperature
(typically around 0.5°C) can be observed.
This marks the beginning of the luteal phase.
\\
During the luteal phase, the endometrium thickens further, creating an optimal environment for embryo implantation.
LH and FSH levels decrease, while progesterone remains elevated to support endometrial maintenance.
If fertilization does not occur, progesterone levels drop, leading to the shedding of the endometrial lining along
with the unfertilized egg.
This process, known as menstruation, marks the beginning of a new cycle.
\\
The menstrual cycle typically lasts around 28 days, with ovulation occurring near the midpoint.
However, variations, particularly in the follicular phase length, are common and can be influenced by factors such as stress, diet, exercise and age~\cite{silberstein_physiology_2000}.
Figure~\ref{fig:background_menstrual_cycle_physiology} provides a detailed overview of the hormonal and physiological changes
throughout the menstrual cycle.

\begin{figure}
    \centering
    \includegraphics[width=0.6\textwidth]{background_menstrual_cycle_physiology}
    \caption{Physiological changes during the menstrual cycle~\cite{pedroso_menstrual_2022}.}
    \label{fig:background_menstrual_cycle_physiology}
\end{figure}

Not every cycle results in ovulation—a phenomenon known as anovulation—which leads to a monophasic temperature pattern.
Anovulation can have various causes, including hormonal imbalances, stress, or underlying health conditions~\cite{rosenfield_adolescent_2013}.

Anovulation is reflected in temperature data as either an absence of a clear temperature rise or a rise
that is insufficient in magnitude or duration to be considered a reliable indicator of ovulation.
Distinguishing between ovulatory and anovulatory cycles is challenging, as the only definitive confirmation of
successful ovulation in a clinical sense is a positive pregnancy test.
Even ultrasound imaging can only confirm that an egg was released from its follicle—not whether it was fertilized or successfully implanted.

%TODO: find source for this

%TODO: show plot of different cycle types

\subsubsection{Fertility Prediction}\label{subsec:fertility_prediction}
Throughout the menstrual cycle, the chance of fertilization varies significantly.
An egg cell released from the ovary during ovulation, can be fertilized for up to 24 hours.
However, since male sperm cells can survive up to 6 days inside the female reproductive tract,
the fertile window is typically defined as the five days before ovulation until one day after ovulation~\cite{dunson_day-specific_1999}.
Research by~\citeauthor{dunson_day-specific_1999} has shown that the highest chance of fertilization is around one day before ovulation,
as illustrated in Figure~\ref{fig:background_pregnancy_chance}.

\begin{figure}
    \centering
    \includegraphics[width=0.7\textwidth]{background_pregnancy_chance_over_time}
    \caption{Chance of fertilization depending on the day of the menstrual cycle.
    The highest chance is around one day before ovulation~\cite{dunson_day-specific_1999}.}
    \label{fig:background_pregnancy_chance}
\end{figure}

It is important to note that fertility prediction is inherently dependent on ovulation prediction.
Since the probability of conception is tightly linked to ovulation timing, the accuracy of fertility prediction methods
is constrained by the precision of ovulation detection.
This relationship underscores the necessity of developing reliable ovulation prediction models,
as even small inaccuracies can significantly impact fertility assessments.

\subsubsection{Physiological Signs of Ovulation}\label{subsec:physiological_signs}
Several physiological signs correlate with ovulation and can be used for prediction.
As shown in Figure~\ref{fig:background_menstrual_cycle_physiology}, these include hormonal fluctuations (LH and FSH surges) and
changes in body temperature.
Additionally, variations in cervical mucus consistency, salivary ferning patterns,
and electrical resistance of the skin and vaginal mucosa have been observed~\cite{silberstein_physiology_2000}.

Among these, ultrasonography provides the most accurate confirmation of ovulation by detecting follicular changes,
but it is costly and requires specialized equipment.
Hormone measurements in urine and blood are widely used and available in at-home test kits,
but they require frequent testing.
Temperature-based methods, particularly body temperature tracking, offer a non-invasive alternative by
detecting the slight temperature rise that follows ovulation.
Advances in wearable technology have further enabled continuous and automated temperature monitoring,
improving accessibility and usability~\cite{alexander_fertilitatsmonitoring_2014, luo_detection_2020, yu_tracking_2022}.

\subsection{Technical Background}\label{subsec:technological_background}

\subsubsection{Time Series Analysis}\label{subsubsec:time_series_analysis}
Time series analysis is a fundamental tool for studying sequential data that evolves over time.
Unlike other data types, time series data has an inherent temporal order, where each data point is associated
with a timestamp, capturing its dependence on past values.
Time series analysis typically serves two main goals:
Understanding the underlying mechanisms that lead to the observed data and predicting future data points based on the
historical information and potentially external factors~\cite{cryer_time_2008}
\\
Time series analysis encompasses various methods, ranging from simple statistical models to complex deep learning architectures.
Classical methods


In the following, we will introduce the two most common approaches used for machine learning on time series data,
LSTMs and transformers.

\subsubsection{RNN and LSTM Networks}\label{subsubsec:lstm_networks}
Recurrent Neural Networks (RNNs) are extensions of classical neural networks that incorporate cyclic connections between neurons.
These recurrent connections allow the network to retain information from previous inputs by feeding the hidden state
from a prior time step into the current one, enabling a form of temporal memory.

In practice, this means that input data is processed sequentially, one step at a time.
At each step \(t\), the input \(x_t\) is combined with the previous hidden state \(h_{t-1}\) to produce a new
hidden state \(h_t\), which contributes to the output \(o_t\).
This process allows the network to learn temporal dependencies and model sequential data effectively~\cite{medsker_recurrent_1999}.

However, this approach has the downside that the model cannot explicitly control how it remembers or forgets information at each step,
limiting its ability to manage long-term dependencies.
During training via backpropagation, the weights of a neural network are updated based on the partial derivatives of the loss function.
The more propagations required (e.g., in deeper networks), the more multiplications are needed to compute gradients for earlier weights.

RNNs are typically trained using \textit{backpropagation through time} (BPTT), in which gradients are propagated through many time steps,
leading to numerical instability.
If the gradients shrink exponentially, the model suffers from the \emph{vanishing gradient} problem;
if they grow exponentially, it results in \emph{exploding gradients}~\cite{hochreiter_vanishing_1998}.
In both cases, learning is significantly impaired.

Exploding gradients can often be mitigated using techniques such as \emph{gradient clipping},
where the magnitude of the gradient is capped---typically within a range of \([-1, 1]\)---to stabilize training.

Figure~\ref{fig:rnn_unfolded} illustrates the unfolded structure of an RNN across three time steps.
This technique, known as \emph{unfolding}, clarifies how sequential inputs update the hidden state and generate
outputs at each time step.

\begin{figure}
    \centering
    \includegraphics[width=0.7\textwidth]{recurrent_neural_network_unfold}
    \caption{Schematic diagram of the unfolded structure of a recurrent neural network~\cite{fdeloche_english_2017}}
    \label{fig:rnn_unfolded}
\end{figure}

To address the vanishing gradient problem and enable better long-term memory,
\emph{Long Short-Term Memory} (LSTM) networks were introduced by \citeauthor{hochreiter_long_1997}~\cite{hochreiter_long_1997}.
LSTMs extend the RNN architecture by incorporating a memory cell and a series of gates that regulate the flow of
information: the \emph{forget gate}, the \emph{input gate}, and the \emph{output gate}.

\begin{itemize}
    \item The \emph{forget gate} determines which information from the previous cell state should be discarded.
    \item The \emph{input gate} controls what new information is added to the cell state.
    \item The \emph{output gate} selects relevant parts of the current cell state to produce the output and the next hidden state.
\end{itemize}

Each gate employs a sigmoid activation function to regulate the flow of information,
allowing LSTMs to preserve and update memory over long sequences.


\begin{figure}
    \centering
    \includegraphics[width=0.6\textwidth]{lstm_cell_diagram}
    \caption{Diagram of a LSTM cell showing the flow of information \cite{chevalier_english_2018}}
    \label{fig:lstm_architecture}
\end{figure}

Figure~\ref{fig:lstm_architecture} illustrates the architecture of an LSTM cell.
On the left, the previous cell state \(C_{t-1}\) represents the long-term memory,
while the hidden state \(h_{t-1}\) encodes the short-term memory from the preceding time step.
The current input \(x_t\) is processed together with these states to update the cell.
The resulting new cell state \(C_t\) and hidden state \(h_t\) are passed forward to the next time step or used to produce the model’s output.
Within the cell, the forget gate determines how much of the previous cell state \(C_{t-1}\) is retained.
The input gate updates the cell state with new information derived from the current input and previous hidden state.
Finally, the output gate controls how much of the updated cell state contributes to the hidden state \(h_t\),
which is passed on to the next time step or used for prediction.

LSTMs are widely used in biomedical applications due to their capacity to handle sequences of variable length and complexity.
In the context of ovulation prediction, where hormonal patterns exhibit periodicity but also irregularity,
LSTMs are well-suited to learn relevant time-dependent signals from sequential physiological measurements.

While powerful, LSTMs can be computationally intensive and sensitive to hyperparameter tuning.
Therefore, they are often compared with alternative architectures,
including simpler feedforward networks and more recent attention-based models,
to evaluate trade-offs in performance, interpretability, and computational cost.

The next section introduce the \emph{Transformer} architecture, a more recent alternative that forgoes
recurrence in favor of attention mechanisms.

\subsubsection{Transformer Models}\label{subsubsec:transformer_models}

Transformer models are a class of neural architectures that use \emph{self-attention}
to model dependencies in sequential data without relying on recurrence~\cite{vaswani_attention_2017}.
Unlike recurrent neural networks (RNNs), Transformers process input sequences in parallel,
allowing them to model relationships between any pair of input tokens or timesteps directly.
This mitigates the limitations of recurrent models, such as long-term memory constraints
and vanishing gradients.

Originally introduced for machine translation, Transformers have proven broadly applicable to
various sequence modeling tasks due to their flexibility, scalability, and strong performance
on complex temporal patterns.

At the core of the Transformer is the attention mechanism, which enables the model to compute
context-aware representations by weighing the importance of different input positions for each output.
This is achieved through \emph{scaled dot-product attention}, where queries, keys, and values are
linearly projected from the input and used to compute attention scores.

\begin{figure}
    \centering
    \includegraphics[width=0.4\textwidth]{background_transformer_architecture}
    \caption{The Transformer - architecture for an encoder-decoder model~\cite{vaswani_attention_2017}}
    \label{fig:background_transformer_architecture}
\end{figure}


Figure~\ref{fig:background_transformer_architecture} illustrates the original encoder-decoder model introduced by~\citeyear{vaswani_attention_2017}.

The Transformer architecture consists of two components: an \emph{Encoder} and a \emph{Decoder}.

\paragraph{Encoder:}

The encoder is responsible for encoding the input into a contextualized representation.
In the case of machine translation, this input would be a sentence in the source language.


The input tokens are first mapped to dense continuous vector representations (embeddings).
Since the attention mechanism permutation-invariant---that is, it does not inherently encode the order of tokens in the sequence---
\emph{positional encodings} are added to the token embeddings to provide information about the token positions in the sequence.

Without positional encoding, repeated tokens such as `The` would be indistinguishable
to the model regardless of their location, even if they play different syntactic or semantic roles.
Positional encodings, often based on sinusoidal functions, inject a unique position-dependent signal
into each token, enabling the model to distinguish between identical tokens in different positions.

Inside each encoder block, \emph{Multi-Head-Attention} is applied to the inputs.
Multi-Head-Attention extends the regular attention mechanism, by adding multiple attention heads that focus on different parts of the embeddings.
Each head does the scaled dot-product attention independently on its slice of the data.
The outputs of all heads are then concatenated and combined via a linear projection.

The outputs of the attention mechanism are then processed in a feed forward network to allow for a non-linear projection.
Residual connections for both the attention and the feed forward allow for better gradient flow and model stability.

\paragraph{Decoder:}

In a sequence-to-sequence Transformer, the decoder generates the output sequence autoregressively,
using the contextualized representation produced by the encoder.

At inference time, generation begins with a special \emph{start-of-sequence} token.
Like the encoder, the decoder embeds its inputs and augments them with positional encodings
to retain information about token order.

To ensure that the model does not access future tokens during training,
a \emph{look-ahead mask} is applied within the decoder’s self-attention mechanism.
This masking ensures that each position can only attend to earlier positions in the sequence,
preventing information leakage.
This component is referred to as \emph{masked multi-head self-attention}.

Following the masked self-attention, the decoder incorporates information from the encoder
via a \emph{cross-attention} layer.
Here, the decoder’s hidden states act as queries, while the encoder’s outputs serve as keys and values.
This allows the decoder to condition its predictions on the entire encoded input sequence.

The output of the cross-attention layer is passed through a position-wise feed-forward network
and further normalization and residual connections, analogous to the encoder blocks.

For tasks such as machine translation, the final decoder outputs are linearly projected
to the target vocabulary size, and a softmax function is applied to produce a probability distribution
over possible next tokens.

During inference, tokens are sampled sequentially from this distribution and fed back into the decoder for the next prediction step.
This process continues until a special \emph{end-of-sequence} token is generated, indicating that the model has completed the output sequence.

Depending on the use case and data complexity, multiple encoder and decoder layers can be stacked
to increase model capacity and improve predictive performance.

While originally developed for machine translation, the Transformer architecture has since been applied
successfully to a range of tasks, including time-series forecasting and biomedical data analysis(\cite{wu_deep_nodate,zeng_are_2022}).
Its ability to model long-range dependencies without recurrence makes it particularly suited for biomedical time-series,
where signals may be irregular, noisy, or span varying temporal scales.

\subsubsection{Convolutional Layers as Temporal Feature Extractors}
For high-resolution time-series data, the input dimensionality can become large,
especially in models like Transformers that process the entire sequence in parallel.
This can lead to increased memory consumption and slower training.
To mitigate this and retain as much information as possible, convolutional layers can be used
to reduce the sequence length while preserving important local patterns.

In this context, one-dimensional convolutions act as learnable filters that slide over the input sequence to extract temporal features.
Each filter is parameterized to respond to specific local structures in the data, such as peaks, slopes, or short-term motifs.
By adjusting the \emph{stride}—the step size of the convolution—the model can control the degree of downsampling,
effectively reducing the number of time steps passed to subsequent layers.

Additional dimensionality reduction can be achieved using pooling operations, such as \emph{max pooling}, which retains only the maximum value within a given window.
These techniques reduce the computational load while maintaining salient information for downstream processing.

Figure~\ref{fig:background_convolution_example} illustrates a simple one-dimensional convolution applied to a sequence using a filter of size 3.
The stride determines how far the filter moves at each step, affecting both the resolution and length of the resulting feature map.

\begin{figure}
    \centering
    \includegraphics[width=0.6\textwidth]{background_convolution_example}
    \caption{Example of a simple 1-D convolution on an input sequence.}
    \label{fig:background_convolution_example}
\end{figure}