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Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians

机译：肺动脉高压的声学诊断：自动语音识别启发式分类算法优于医师

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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.
机译：我们假设自动语音识别启发式分类算法可以区分患有和不患有肺动脉高压（PH）的受试者的心音和优于医生的声音。同时记录心音，心电图和平均肺动脉压（mPAp）。心音记录被数字化，以训练和测试受语音识别启发的分类算法。我们使用mel频率倒谱系数从心音中提取特征。高斯混合模型将特征分为PH（mPAp≥25 mmHg）或正常（mPAp <25 mmHg）。对患者数据不了解的内科医生听了相同的心音录音并尝试了诊断。我们研究了164名受试者：86名mPAp≥25mmHg（mPAp 41±12 mmHg）和78名mPAp <25 mmHg（mPAp 17±5 mmHg）（p <0.005）。自动化语音识别启发算法的正确诊断率为74％，而医师的正确诊断率为56％（p（= 0.005）。该算法的假阳性率为34％，而临床医生为50％（p = 0.04）。对于医生来说，该算法的假阴性率为23％和68％（p = 0.0002）。我们开发了一种自动语音识别启发式分类算法，用于PH的声学诊断，其性能优于可以用来筛选PH并鼓励早期专家转诊的医生。

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