Learnable masks for time series explainability
Over the past decade, Deep Learning (DL) models have been integrated into data-driven sectors such as e-commerce and healthcare to assist humans in making informed decisions. In neuroscience, these models have been utilized to analyze complex time series data, such as EEG and MEG, providing valuable...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/446097 |
| Acceso en línea: | https://hdl.handle.net/2117/446097 |
| Access Level: | acceso abierto |
| Palabra clave: | Deep learning (Machine learning) Neurosciences Numerical analysis Wavelets (Mathematics) Deep learning Sèries temporals Explicabilitat Wavelets Time series Explainability Aprenentatge profund (Aprenentatge automàtic) Neurociències Anàlisi numèrica Ondetes (Matemàtica) Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | Over the past decade, Deep Learning (DL) models have been integrated into data-driven sectors such as e-commerce and healthcare to assist humans in making informed decisions. In neuroscience, these models have been utilized to analyze complex time series data, such as EEG and MEG, providing valuable insights for diagnosis and treatment. However, the black-box nature of this technology makes it challenging to provide the data foundation of those decisions and limits its trustworthiness. This research aims to propose a new explainability method that generates an attribution mask based on the multilevel discrete wavelet transform (DWT). Traditional approaches usually focus on the time or frequency domain, but this method simultaneously preserves and analyzes both. Even though the continuous wavelet transform (CWT) seems a better approach for signal processing and analysis, the DWT is more suitable for this task. It provides a set of orthogonal mother wavelets that allows a non-redundant and easily invertible transformation to frequency bands and time scales. The DWT has an advantage over the CWT, as it provides a more compact representation of the signal and allows for a more efficient implementation. Acting as a quadrature mirror filter bank, the DWT decomposes signals into approximation and detail coefficients across multiple levels, preserving critical information at various resolutions. To validate and evaluate the method, simulated datasets and the SleepEDF dataset were used. Both have been used for classification; the first one provides a defined ground truth, and the second establishes a well-defined classification problem, such as sleep staging. Additionally, various wavelet families, including Daubechies, Symlets, and Coiflets, have been tested and analyzed both qualitatively and quantitatively. The results show similar behavior between Symlets and Coiflets, due to their nearly symmetric nature. However, based on the Quantus complexity metric, Coiflets are less complex, which is preferred in the context of explainability. Moreover, the number of vanishing moments allows us to fine-tune the trade-off between frequency and time resolution, as well as computational cost. This research suggests that four vanishing moments offer a suitable balance between the various properties. Then, results show that while the learned attribution masks accurately highlight features in simulated scenarios, their performance on the real-world sleep staging task is limited. This is attributed to the DWT's coarse, dyadic frequency bands, which do not align well with the specific, non-dyadic frequency bands of sleep physiology. Therefore, this work introduces a promising framework, but highlights that its practical effectiveness depends on the suitability of the chosen transform for the specific problem domain. |
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