Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.
Pediatric Pulmonary Hypertension Service, Pediatric Cardiac Critical Care Unit, Stollery Children's Hospital, Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Canada.
Sci Rep. 2016 Sep 9;6:33182. doi: 10.1038/srep33182.
We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral.
我们假设，一种受自动语音识别启发的分类算法，可以区分肺动脉高压（PH）患者和非 PH 患者的心音，并优于医生。同时记录心音、心电图和平均肺动脉压（mPAp）。将心音记录数字化，以训练和测试受语音识别启发的分类算法。我们使用梅尔频率倒谱系数从心音中提取特征。高斯混合模型将特征分类为 PH（mPAp≥25mmHg）或正常（mPAp<25mmHg）。对患者数据不知情的医生听取相同的心音记录并尝试诊断。我们研究了 164 名受试者：86 名 mPAp≥25mmHg（mPAp 41±12mmHg），78 名 mPAp<25mmHg（mPAp 17±5mmHg）（p<0.005）。与医生的 56%相比，自动语音识别启发算法的正确诊断率为 74%（p=0.005）。算法的假阳性率为 34%，而医生为 50%（p=0.04）。算法的假阴性率为 23%，而医生为 68%（p=0.0002）。我们开发了一种受自动语音识别启发的分类算法，用于 PH 的声学诊断，优于医生，可用于 PH 的筛查，并鼓励更早地转介给专科医生。
