Data from: Estimation of inhalation flow profile using audio-based methods to assess inhaler medication adherence

Asthma and chronic obstructive pulmonary disease (COPD) patients are required to inhale forcefully and deeply to receive medication when using a dry powder inhaler (DPI). There is a clinical need to objectively monitor the inhalation flow profile of DPIs in order to remotely monitor patient inhalation technique. Audio-based methods have been previously employed to accurately estimate flow parameters such as the peak inspiratory flow rate of inhalations, however, these methods required multiple calibration inhalation audio recordings. In this study, an audio-based method is presented that accurately estimates inhalation flow profile using only one calibration inhalation audio recording. Twenty healthy participants were asked to perform 15 inhalations through a placebo Ellipta™ DPI at a range of inspiratory flow rates. Inhalation flow signals were recorded using a pneumotachograph spirometer while inhalation audio signals were recorded simultaneously using the Inhaler Compliance Assessment device attached to the inhaler. The acoustic (amplitude) envelope was estimated from each inhalation audio signal. Using only one recording, linear and power law regression models were employed to determine which model best described the relationship between the inhalation acoustic envelope and flow signal. Each model was then employed to estimate the flow signals of the remaining 14 inhalation audio recordings. This process repeated until each of the 15 recordings were employed to calibrate single models while testing on the remaining 14 recordings. It was observed that power law models generated the highest average flow estimation accuracy across all participants (90.89±0.9% for power law models and 76.63±2.38% for linear models). The method also generated sufficient accuracy in estimating inhalation parameters such as peak inspiratory flow rate and inspiratory capacity within the presence of noise. Estimating inhaler inhalation flow profiles using audio based methods may be clinically beneficial for inhaler technique training and the remote monitoring of patient adherence.哮喘与慢性阻塞性肺疾病（chronic obstructive pulmonary disease, COPD）患者在使用干粉吸入器（dry powder inhaler, DPI）时，需用力深吸气以获取药物。当前临床亟需实现干粉吸入器吸气气流曲线的客观监测，以此远程监控患者的吸气操作技术。此前已有基于音频的方法可精准估算吸气相关气流参数（如吸气峰值流速），但此类方法需多次录制校准用的吸气音频样本。本研究提出一种仅需一次校准吸气音频录制即可精准估算吸气气流曲线的音频分析方法。招募20名健康受试者，要求其借助安慰剂型Ellipta™干粉吸入器完成15次不同吸气流速的吸气动作。实验同步采用气动流速式肺量计（pneumotachograph spirometer）记录吸气气流信号，并通过连接于吸入器的吸入器依从性评估装置（Inhaler Compliance Assessment device）采集吸气音频信号。从每份吸气音频信号中提取其声学幅度包络。仅利用单次录制样本，分别构建线性回归与幂律回归模型，以筛选出最能匹配吸气声学包络与气流信号间关联的最优模型。随后使用该模型估算剩余14次吸气音频信号对应的气流曲线。重复上述流程，直至15次录制样本均被用于单次模型校准，并在其余14次样本上完成测试。实验结果显示，幂律模型在所有受试者中取得最高的平均气流估算准确率：幂律模型为90.89±0.9%，线性模型为76.63±2.38%。该方法在噪声环境下仍可实现吸气峰值流速、吸气容量等吸气参数的精准估算。基于音频的吸气气流曲线估算方法，有望在吸入器操作技术培训与患者用药依从性远程监控等临床场景中具备应用价值。