Analysis and Classification of Full-Field Electroretinogram Signals
Аннотация
Electroretinography (ERG) is a non-invasive way of measuring the electrical activity of the retina with the help of light stimulation. ERG signals contain several different components which can be used to diagnose various retinal disorders. Some of these disorders include cone and rod related dystrophies, age-related macular degeneration, diabetic retinopathies, glaucoma and retinitis pigmentosa. In this thesis, various methods were utilized to analyze ERG signals, including time-domain, frequency-domain, and time-frequency domain techniques.
The process began with cleaning the signals database and preprocessing the extracted signals. Next, standard signal components including the a- and b-waves and implicit times were computed, and time-domain analysis was conducted on the signals. The time-domain analysis involved examining the amplitudes and latencies of the different wave components, which can provide insights into the functioning of different retinal cell types. Correlation analysis was also conducted to examine there’s relationship between the patients’ age and the signal components which the results show very little correlation between the age and the components, with some significant correlation between the individual components.
In the frequency domain, Fourier Transform was used to extract signal frequencies for analysis. The frequency content of the ERG signals can reveal information about the underlying physiological processes and potential abnormalities. However, due to the time-invariant nature of the frequency domain, drawing conclusions based solely on this analysis can be challenging.
For time-frequency domain analysis, short-time Fourier transform (STFT) was employed along with spectrogram analysis. The STFT allowed for the examination of how the frequency content of the signals evolved over time, providing a more comprehensive representation of the signal dynamics.
Additionally, features were extracted using different windows and window sizes for machine learning classifier training to classify the signals. The spectrograms computed were also used to train deep learning models with different architectures, and the results were compared based on the windows used.
The findings indicate that there is no discernible correlation between age and other signal components in the time domain. This suggests that age alone may not be a reliable predictor of ERG signal characteristics.
In the frequency domain, drawing conclusions based solely on the frequency content proved challenging due to the time-invariant nature of the analysis.
Moreover, it is evident that window sizes have a more significant impact on the resulting features compared to window functions. Larger window sizes yield improved frequency resolution, while smaller windows offer higher time resolution. This implies that the time and frequency resolution play a more substantial role in shaping the resulting features than the signal processing performed by the window function itself.
The analysis of ERG signals using various techniques, including time-domain, frequency-domain, and time-frequency domain methods, provides valuable insights into retinal function and potential disorders. The combination of these approaches, along with feature extraction and machine learning techniques, offers a comprehensive framework for understanding and interpreting ERG signals. However, careful consideration of factors such as window sizes and resolution trade-offs is crucial for obtaining meaningful and accurate results.
Keywords: electroretinography, retinal diagnoses, machine learning, Fourier transform, short-time Fourier transform, deep learning, time-domain analysis, frequency-domain analysis, time-frequency analysis, disease diagnoses, full-field electroretinograms, signal classification, feature extraction, feature learning, neural networks, signal analysis
The process began with cleaning the signals database and preprocessing the extracted signals. Next, standard signal components including the a- and b-waves and implicit times were computed, and time-domain analysis was conducted on the signals. The time-domain analysis involved examining the amplitudes and latencies of the different wave components, which can provide insights into the functioning of different retinal cell types. Correlation analysis was also conducted to examine there’s relationship between the patients’ age and the signal components which the results show very little correlation between the age and the components, with some significant correlation between the individual components.
In the frequency domain, Fourier Transform was used to extract signal frequencies for analysis. The frequency content of the ERG signals can reveal information about the underlying physiological processes and potential abnormalities. However, due to the time-invariant nature of the frequency domain, drawing conclusions based solely on this analysis can be challenging.
For time-frequency domain analysis, short-time Fourier transform (STFT) was employed along with spectrogram analysis. The STFT allowed for the examination of how the frequency content of the signals evolved over time, providing a more comprehensive representation of the signal dynamics.
Additionally, features were extracted using different windows and window sizes for machine learning classifier training to classify the signals. The spectrograms computed were also used to train deep learning models with different architectures, and the results were compared based on the windows used.
The findings indicate that there is no discernible correlation between age and other signal components in the time domain. This suggests that age alone may not be a reliable predictor of ERG signal characteristics.
In the frequency domain, drawing conclusions based solely on the frequency content proved challenging due to the time-invariant nature of the analysis.
Moreover, it is evident that window sizes have a more significant impact on the resulting features compared to window functions. Larger window sizes yield improved frequency resolution, while smaller windows offer higher time resolution. This implies that the time and frequency resolution play a more substantial role in shaping the resulting features than the signal processing performed by the window function itself.
The analysis of ERG signals using various techniques, including time-domain, frequency-domain, and time-frequency domain methods, provides valuable insights into retinal function and potential disorders. The combination of these approaches, along with feature extraction and machine learning techniques, offers a comprehensive framework for understanding and interpreting ERG signals. However, careful consideration of factors such as window sizes and resolution trade-offs is crucial for obtaining meaningful and accurate results.
Keywords: electroretinography, retinal diagnoses, machine learning, Fourier transform, short-time Fourier transform, deep learning, time-domain analysis, frequency-domain analysis, time-frequency analysis, disease diagnoses, full-field electroretinograms, signal classification, feature extraction, feature learning, neural networks, signal analysis