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<p>Recent studies have suggested that analysis of hemodialysis shunt sounds can predict hemodynamics, although optimal acoustic features remain unclear. This study compared engineered features (time- and frequency-domain statistics) with perceptual features such as Mel-scale transformations for predicting blood flow volume (FV) and resistive index (RI). We examined whether integrating clinical parameter variables could improve prediction accuracy. Shunt sounds from 107 recordings (48 patients with chronic kidney disease who underwent arteriovenous fistula creation surgery) were analyzed. Forty-seven acoustic features were extracted and classified into engineered or perceptual pools. After patient-level data splitting (80% training, 20% test), five features per pool were selected using bootstrap-based stability selection. Machine learning models were developed to classify brachial FV (threshold: 400&#xa0;mL/min) and RI (threshold: 0.6), and model performance was evaluated using bootstrap validation. Both acoustic feature sets demonstrated stable FV classification performance (AUC: 0.866 [engineered], 0.864 [perceptual]). Identical acoustic features were selected in acoustic-only and combined models, and the inclusion of clinical parameters did not improve test-set performance. RI classification performed worse than FV classification. Overall, perceptual features yielded stable FV predictions comparable to those from engineered features, supporting their utility for acoustic-based shunt monitoring. Further validation in larger cohorts is warranted.</p>

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Acoustic feature selection and clinical information integration for machine learning based dialysis shunt function prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • Shun Yoshida,
  • Daiichiro Akiyama,
  • Kie Ohkoshi,
  • Kohei Yamamura,
  • Keiichi Osano,
  • Miho Shikata,
  • Toshihisa Ishii,
  • Makiko Konishi,
  • Kazuya Takahashi,
  • Ayumu Nakashima

摘要

Recent studies have suggested that analysis of hemodialysis shunt sounds can predict hemodynamics, although optimal acoustic features remain unclear. This study compared engineered features (time- and frequency-domain statistics) with perceptual features such as Mel-scale transformations for predicting blood flow volume (FV) and resistive index (RI). We examined whether integrating clinical parameter variables could improve prediction accuracy. Shunt sounds from 107 recordings (48 patients with chronic kidney disease who underwent arteriovenous fistula creation surgery) were analyzed. Forty-seven acoustic features were extracted and classified into engineered or perceptual pools. After patient-level data splitting (80% training, 20% test), five features per pool were selected using bootstrap-based stability selection. Machine learning models were developed to classify brachial FV (threshold: 400 mL/min) and RI (threshold: 0.6), and model performance was evaluated using bootstrap validation. Both acoustic feature sets demonstrated stable FV classification performance (AUC: 0.866 [engineered], 0.864 [perceptual]). Identical acoustic features were selected in acoustic-only and combined models, and the inclusion of clinical parameters did not improve test-set performance. RI classification performed worse than FV classification. Overall, perceptual features yielded stable FV predictions comparable to those from engineered features, supporting their utility for acoustic-based shunt monitoring. Further validation in larger cohorts is warranted.