# Deep Learning-Enhanced Spectrogram Analysis for
## 3 Methodology
### Dataset and Preprocessing  
Biomedical signals acquired through palpation and percussion were transformed into spectrograms using the Short-Time Fourier Transform (STFT), a widely employed technique for the time-frequency representation of non-stationary signals [17; 18]. The dataset consisted of 18,240 spectrograms obtained from patients, encompassing eight anatomical regions [19]. These regions represent standardized anatomical zones frequently examined through percussion and palpation, including the upper and lower abdominal quadrants, left and right flanks, and the epigastric area. Zone mapping followed the clinical protocol defined by the iApp platform [31], ensuring reproducibility and relevance for diagnostic analysis. The signals were collected using a physiological sound transducer (TSD108, BIOPAC Systems) connected to the BIOPAC MP150 data acquisition system as part of the iApp platform [31], ensuring reproducibility and relevance for diagnostic analysis. This segmentation is consistent with standard clinical examination techniques used to assess gastrointestinal and abdominal health, where physicians routinely perform palpation and percussion across these zones to detect abnormalities, such as tenderness, swelling, or acoustic changes. Only the sound signals from palpation and percussion were used for training and classification, and no force or acceleration signals were included. Each signal was recorded at a sampling rate of 312 Hz with a duration of approximately 11.5 s. The resulting time-domain sound signals were processed into spectrograms using STFT to enable time-frequency analysis. Each spectrogram was normalized and converted into grayscale to reduce the computational complexity and improve model generalization [20]. To standardize the dataset, each spectrogram was resized to 128 x 128 pixels, normalized to a range of [0, 1], and augmented to prevent overfitting [25]. Several domain-specific augmentation techniques have been applied to improve model generalization and prevent overfitting. These included horizontal flipping, Gaussian noise injection, intensity scaling, and time-frequency masking. Each method was carefully chosen to maintain the spectral integrity of biomedical signals, avoiding transformations that could introduce unrealistic or misleading frequency patterns [32]. Each transformation was tested to ensure that it did not distort diagnostic characteristics within the spectrograms. Table 2 summarizes the dataset distribution. The STFT was applied to convert the raw time-series signals into spectrograms, ensuring a rich time-frequency representation. \[S(t,f)=\int_{-\infty}^{\infty}x(\tau)w(\tau-t)e^{-j2\pi f\tau}d\tau \tag{1}\]  
where \(x(\tau)\) is the input signal, \(w(\tau-t)\) is the window function, and \(e^{-j2\pi f\tau}\) is the Fourier transform [21]. Recent comparative studies, such as [PERSON] et al. [33], have shown that al \begin{table}
\begin{tabular}{c c c} \hline
**Region** & **Training Samples** & **Testing Samples** \\ \hline R1 & 1824 & 456 \\ R2 & 1824 & 456 \\ R3 & 1824 & 456 \\ R4 & 1824 & 456 \\ R5 & 1824 & 456 \\ R6 & 1824 & 456 \\ R7 & 1824 & 456 \\ R8 & 1824 & 456 \\ \hline Total & 14,592 & 3648 \\ \hline \end{tabular}
\end{table} 
Table 2: Dataset Distribution Across Anatomical Regions. ternative time-frequency distribution methods, such as Continuous Wavelet Transform (CWT) and Chirplet Transform (CT) can also enhance spectrogram-based CNN performance. However, STFT remains widely used owing to its balance between computational efficiency and interpretability in biomedical signal-classification tasks. To standardize the dataset, each spectrogram was resized to 128 \(\times\) 128 pixels, normalized to a range of [0, 1], and augmented to prevent overfitting [25]. A 128 \(\times\) 128 resolution was selected after conducting preliminary trials with alternative sizes, including 64 \(\times\) 64 and 224 \(\times\) 224.