A XAI-based Framework for Frequency Subband Characterization of Cough Spectrograms in Chronic Respiratory Disease

• URL: http://arxiv.org/abs/2508.16237v1
• Date: Fri, 22 Aug 2025 09:16:11 GMT
• Title: A XAI-based Framework for Frequency Subband Characterization of Cough Spectrograms in Chronic Respiratory Disease
• Abstract summary: This paper presents an explainable artificial intelligence (XAI)-based framework for the spectral analysis of cough sounds associated with chronic respiratory diseases.<n>A Convolutional Neural Network (CNN) is trained on time-frequency representations of cough signals.
• Score: 1.5796619021736646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents an explainable artificial intelligence (XAI)-based framework for the spectral analysis of cough sounds associated with chronic respiratory diseases, with a particular focus on Chronic Obstructive Pulmonary Disease (COPD). A Convolutional Neural Network (CNN) is trained on time-frequency representations of cough signals, and occlusion maps are used to identify diagnostically relevant regions within the spectrograms. These highlighted areas are subsequently decomposed into five frequency subbands, enabling targeted spectral feature extraction and analysis. The results reveal that spectral patterns differ across subbands and disease groups, uncovering complementary and compensatory trends across the frequency spectrum. Noteworthy, the approach distinguishes COPD from other respiratory conditions, and chronic from non-chronic patient groups, based on interpretable spectral markers. These findings provide insight into the underlying pathophysiological characteristics of cough acoustics and demonstrate the value of frequency-resolved, XAI-enhanced analysis for biomedical signal interpretation and translational respiratory disease diagnostics.

Related papers

• FreqPhys: Repurposing Implicit Physiological Frequency Prior for Robust Remote Photoplethysmography [54.32412212867472]
  Photoplethysmography enables contactless physiological monitoring by capturing subtle skin-color variations from videos.<n>FreqPhys is a frequency-guided rulation framework that explicitly leverages physiological frequency priors for robust signal recovery.<n>It yields significant improvements over state-of-the-art approaches, particularly under challenging motion conditions.
  arXiv  Detail & Related papers  (2026-04-01T06:25:42Z)
• Explainable Multi-Modal Deep Learning for Automatic Detection of Lung Diseases from Respiratory Audio Signals [0.49581497240446293]
  This study presents an explainable multimodal deep learning framework for automatic lung-disease detection using respiratory audio signals.<n>The framework incorporates Grad-CAM, Integrated Gradients, and SHAP, generating interpretable spectral, temporal, and feature-level explanations.<n>The findings demonstrate the framework's potential for telemedicine, point-of-care diagnostics, and real-world respiratory screening.
  arXiv  Detail & Related papers  (2025-11-29T17:15:58Z)
• XAI-Driven Spectral Analysis of Cough Sounds for Respiratory Disease Characterization [1.4344188754375113]
  We propose an eXplainable Artificial Intelligence (XAI)-driven methodology to enhance the understanding of cough sound analysis for respiratory disease management.<n>We employ occlusion maps to highlight relevant spectral regions in cough spectrograms processed by a Convolutional Neural Network (CNN)
  arXiv  Detail & Related papers  (2025-08-20T11:59:13Z)
• Respiratory Disease Classification and Biometric Analysis Using Biosignals from Digital Stethoscopes [3.2458203725405976]
  This work presents a novel approach leveraging digital stethoscope technology for automatic respiratory disease classification and biometric analysis. By leveraging one of the largest publicly available medical database of respiratory sounds, we train machine learning models to classify various respiratory health conditions. Our approach achieves high accuracy in both binary classification (89% balanced accuracy for healthy vs. diseased) and multi-class classification (72% balanced accuracy for specific diseases like pneumonia and COPD)
  arXiv  Detail & Related papers  (2023-09-12T23:54:00Z)
• Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning [48.29204631769816]
  We re-extract disease labels from CXR reports to make them more realistic by considering disease severity and uncertainty in classification. Our experimental results show that models considering disease severity and uncertainty outperform previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-06T19:19:41Z)
• Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation [67.19443246236048]
  Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases. Some small-sized airway branches (e.g., bronchus and terminaloles) significantly aggravate the difficulty of automatic segmentation. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function.
  arXiv  Detail & Related papers  (2022-09-05T16:38:13Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• Frequency comb and machine learning-based breath analysis for COVID-19 classification [0.6113111451963646]
  We present a robust analytical method that simultaneously measures tens of thousands of spectral features in each breath sample. Using 170 individual samples at the University of Colorado, we report a cross-validated area under the Receiver-Operating-Characteristics curve of 0.849(4). This method detected a significant difference between male and female breath as well as other variables such as smoking and abdominal pain.
  arXiv  Detail & Related papers  (2022-02-04T05:58:52Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning [133.87426554801252]
  We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
  arXiv  Detail & Related papers  (2021-05-25T12:06:55Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Inception-Based Network and Multi-Spectrogram Ensemble Applied For Predicting Respiratory Anomalies and Lung Diseases [16.318395700171624]
  This paper presents an inception-based deep neural network for detecting lung diseases using respiratory sound input. Recordings of respiratory sound collected from patients are transformed into spectrograms where both spectral and temporal information are well presented. These spectrograms are fed into the proposed network, referred to as back-end classification, for detecting whether patients suffer from lung-relevant diseases.
  arXiv  Detail & Related papers  (2020-12-26T08:25:02Z)
• Contralaterally Enhanced Networks for Thoracic Disease Detection [120.60868136876599]
  There exist many similar structures in the left and right parts of the chest, such as ribs, lung fields and bronchial tubes. This kind of similarities can be used to identify diseases in chest X-rays, according to the experience of broad-certificated radiologists. We propose a deep end-to-end module to exploit the contralateral context information for enhancing feature representations of disease proposals.
  arXiv  Detail & Related papers  (2020-10-09T10:15:26Z)
• CNN-MoE based framework for classification of respiratory anomalies and lung disease detection [33.45087488971683]
  This paper presents and explores a robust deep learning framework for auscultation analysis. It aims to classify anomalies in respiratory cycles and detect disease, from respiratory sound recordings.
  arXiv  Detail & Related papers  (2020-04-04T21:45:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.