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Background <p>Auscultation is a noninvasive, real-time method of assessing respiratory diseases; however, its accuracy can vary depending on the clinician’s experience. Although deep learning approaches have shown potential for classifying respiratory sounds, most studies have taken place in controlled environments with minimal noise interference. This may limit their applicability in challenging clinical settings such as intensive care unit (ICU), where ambient noise and equipment interference are common. The aim of this study was to evaluate the performance of a deep learning model for classifying respiratory sounds in ICU environments and to assess the impact of noise-reduction preprocessing techniques.</p> Methods <p>We collected 701 respiratory sound recordings from ICU patients at Chungnam National University Hospital, including 325 normal sounds and 376 abnormal sounds (e.g., crackles, wheezes, and rhonchi). All recordings were obtained in a clinical setting and contained background noise from alarms, voices, and equipment. We performed noise reduction using three preprocessing techniques: band-pass filtering, Savitzky–Golay smoothing, and spectral gating noise reduction (SGNR). The sounds were converted into Mel spectrograms using Fourier transformation. We developed a deep learning model based on transfer learning using the Visual Geometry Group (VGG)-16 network for feature extraction and a convolutional neural network for classification. Model performance was evaluated using 10 repeated five-fold cross-validations.</p> Results <p>The baseline model trained on raw ICU recordings achieved an AUC of 0.75 (95% CI: 0.74–0.76) for distinguishing normal from abnormal respiratory sounds. Among the preprocessing techniques evaluated, band-pass filtering yielded the highest performance with an AUC of 0.80 (95% CI: 0.80–0.81). Savitzky–Golay smoothing and spectral gating noise reduction showed performance similar to raw (original) data.</p> Conclusion <p>Band-pass filtering was associated with a modest, though statistically significant, improvement in performance compared with unprocessed data. While deep learning–based respiratory sound classification is technically feasible in noisy ICU environments, the current level of performance is limited. Further research is required to clarify its potential role and clinical relevance before any consideration of routine clinical implementation, including multi-class classification, more advanced noise reduction strategies, and multi-center validation.</p>

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Respiratory sound analysis for ICU clinical decision support: deep learning-based classification of normal and abnormal sounds using real ICU data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月6日

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  • Soyun Kim,
  • Mi Ra Lee,
  • Taeyoung Ha,
  • YunKyong Hyon,
  • Sunju Lee,
  • Junhong Jo,
  • Chaeuk Chung,
  • Yoonjoo Kim,
  • Song I Lee

摘要

Background

Auscultation is a noninvasive, real-time method of assessing respiratory diseases; however, its accuracy can vary depending on the clinician’s experience. Although deep learning approaches have shown potential for classifying respiratory sounds, most studies have taken place in controlled environments with minimal noise interference. This may limit their applicability in challenging clinical settings such as intensive care unit (ICU), where ambient noise and equipment interference are common. The aim of this study was to evaluate the performance of a deep learning model for classifying respiratory sounds in ICU environments and to assess the impact of noise-reduction preprocessing techniques.

Methods

We collected 701 respiratory sound recordings from ICU patients at Chungnam National University Hospital, including 325 normal sounds and 376 abnormal sounds (e.g., crackles, wheezes, and rhonchi). All recordings were obtained in a clinical setting and contained background noise from alarms, voices, and equipment. We performed noise reduction using three preprocessing techniques: band-pass filtering, Savitzky–Golay smoothing, and spectral gating noise reduction (SGNR). The sounds were converted into Mel spectrograms using Fourier transformation. We developed a deep learning model based on transfer learning using the Visual Geometry Group (VGG)-16 network for feature extraction and a convolutional neural network for classification. Model performance was evaluated using 10 repeated five-fold cross-validations.

Results

The baseline model trained on raw ICU recordings achieved an AUC of 0.75 (95% CI: 0.74–0.76) for distinguishing normal from abnormal respiratory sounds. Among the preprocessing techniques evaluated, band-pass filtering yielded the highest performance with an AUC of 0.80 (95% CI: 0.80–0.81). Savitzky–Golay smoothing and spectral gating noise reduction showed performance similar to raw (original) data.

Conclusion

Band-pass filtering was associated with a modest, though statistically significant, improvement in performance compared with unprocessed data. While deep learning–based respiratory sound classification is technically feasible in noisy ICU environments, the current level of performance is limited. Further research is required to clarify its potential role and clinical relevance before any consideration of routine clinical implementation, including multi-class classification, more advanced noise reduction strategies, and multi-center validation.