Respiratory infections in children increase the risk of fatal lung disease,making effective identification and analysis of breath sounds essential.However,most studies have focused on adults ignoring pediatric patient...Respiratory infections in children increase the risk of fatal lung disease,making effective identification and analysis of breath sounds essential.However,most studies have focused on adults ignoring pediatric patients whose lungs are more vulnerable due to an imperfect immune system,and the scarcity of medical data has limited the development of deep learning methods toward reliability and high classification accuracy.In this work,we collected three types of breath sounds from children with normal(120 recordings),bronchitis(120 recordings),and pneumonia(120 recordings)at the posterior chest position using an off-the-shelf 3M electronic stethoscope.Three features were extracted from the wavelet denoised signal:spectrogram,mel-frequency cepstral coefficients(MFCCs),and Delta MFCCs.The recog-nition model is based on transfer learning techniques and combines fine-tuned MobileNetV2 and modified ResNet50 to classify breath sounds,along with software for displaying analysis results.Extensive experiments on a real dataset demonstrate the effectiveness and superior performance of the proposed model,with average accuracy,precision,recall,specificity and F1 scores of 97.96%,97.83%,97.89%,98.89%and 0.98,respectively,achieving superior performance with a small dataset.The proposed detection system,with a high-performance model and software,can help parents perform lung screening at home and also has the potential for a vast screening of children for lung disease.展开更多
目的 :使用自适应滤波技术消除呼吸音采集过程中受到的环境噪声干扰,进一步提高基于呼吸音的呼吸监测的准确性。方法:首先对颈部呼吸音及环境噪声两路信号同时进行采集,之后针对最小均方误差(least mean square,LMS)、归一化最小均方误...目的 :使用自适应滤波技术消除呼吸音采集过程中受到的环境噪声干扰,进一步提高基于呼吸音的呼吸监测的准确性。方法:首先对颈部呼吸音及环境噪声两路信号同时进行采集,之后针对最小均方误差(least mean square,LMS)、归一化最小均方误差(normalized least mean square,NLMS)和递归最小二乘(recursive least squares,RLS)3种自适应滤波算法进行理论分析并对实际采集的呼吸音进行分段处理。结果:采用LMS算法对数据进行分段处理的效果并不理想,而对于NLMS和RLS算法,选择分段长度为N=40 000时对无噪声信号的"污染"最小且滤波效果较理想。结论:自适应滤波算法用于抑制呼吸音中噪声的可行性得到了验证,并最终给出了滤波效果相对最佳的算法及其相应参数,可为基于呼吸音的呼吸监测的实际应用提供参考。展开更多
基金funded by the Scientific Research Starting Foundation of Hainan University(KYQD1882)the Flexible Introduction Scientific Research Starting Foundation of Hainan University(2020.11-2025.10).
文摘Respiratory infections in children increase the risk of fatal lung disease,making effective identification and analysis of breath sounds essential.However,most studies have focused on adults ignoring pediatric patients whose lungs are more vulnerable due to an imperfect immune system,and the scarcity of medical data has limited the development of deep learning methods toward reliability and high classification accuracy.In this work,we collected three types of breath sounds from children with normal(120 recordings),bronchitis(120 recordings),and pneumonia(120 recordings)at the posterior chest position using an off-the-shelf 3M electronic stethoscope.Three features were extracted from the wavelet denoised signal:spectrogram,mel-frequency cepstral coefficients(MFCCs),and Delta MFCCs.The recog-nition model is based on transfer learning techniques and combines fine-tuned MobileNetV2 and modified ResNet50 to classify breath sounds,along with software for displaying analysis results.Extensive experiments on a real dataset demonstrate the effectiveness and superior performance of the proposed model,with average accuracy,precision,recall,specificity and F1 scores of 97.96%,97.83%,97.89%,98.89%and 0.98,respectively,achieving superior performance with a small dataset.The proposed detection system,with a high-performance model and software,can help parents perform lung screening at home and also has the potential for a vast screening of children for lung disease.
文摘目的 :使用自适应滤波技术消除呼吸音采集过程中受到的环境噪声干扰,进一步提高基于呼吸音的呼吸监测的准确性。方法:首先对颈部呼吸音及环境噪声两路信号同时进行采集,之后针对最小均方误差(least mean square,LMS)、归一化最小均方误差(normalized least mean square,NLMS)和递归最小二乘(recursive least squares,RLS)3种自适应滤波算法进行理论分析并对实际采集的呼吸音进行分段处理。结果:采用LMS算法对数据进行分段处理的效果并不理想,而对于NLMS和RLS算法,选择分段长度为N=40 000时对无噪声信号的"污染"最小且滤波效果较理想。结论:自适应滤波算法用于抑制呼吸音中噪声的可行性得到了验证,并最终给出了滤波效果相对最佳的算法及其相应参数,可为基于呼吸音的呼吸监测的实际应用提供参考。
