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

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

The receiver operating characteristic (ROC) curve for our algorithm to detect the presence or absence of PH.The area-under-the-curve (AUC) was 0.74. The False Positive Rate (FPR)/True Positive Rate (TPR) point for the clinicians’ performance are also shown on the graph. The automated algorithm performs better than clinicians’ interpretation of the recorded heart sounds. X-axis shows the False Positive Rate, and the y-axis shows the True Positive Rate.
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f4: The receiver operating characteristic (ROC) curve for our algorithm to detect the presence or absence of PH.The area-under-the-curve (AUC) was 0.74. The False Positive Rate (FPR)/True Positive Rate (TPR) point for the clinicians’ performance are also shown on the graph. The automated algorithm performs better than clinicians’ interpretation of the recorded heart sounds. X-axis shows the False Positive Rate, and the y-axis shows the True Positive Rate.

Mentions: The algorithm, that uses 13 MFC coefficients, correctly classified 74% of patients as PH or non-PH. The algorithm resulted in a false negative rate of 23% and a false positive rate of 34%. A receiver operating characteristic (ROC) curve for the algorithm is shown in Fig. 4.


Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians
The receiver operating characteristic (ROC) curve for our algorithm to detect the presence or absence of PH.The area-under-the-curve (AUC) was 0.74. The False Positive Rate (FPR)/True Positive Rate (TPR) point for the clinicians’ performance are also shown on the graph. The automated algorithm performs better than clinicians’ interpretation of the recorded heart sounds. X-axis shows the False Positive Rate, and the y-axis shows the True Positive Rate.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5016849&req=5

f4: The receiver operating characteristic (ROC) curve for our algorithm to detect the presence or absence of PH.The area-under-the-curve (AUC) was 0.74. The False Positive Rate (FPR)/True Positive Rate (TPR) point for the clinicians’ performance are also shown on the graph. The automated algorithm performs better than clinicians’ interpretation of the recorded heart sounds. X-axis shows the False Positive Rate, and the y-axis shows the True Positive Rate.
Mentions: The algorithm, that uses 13 MFC coefficients, correctly classified 74% of patients as PH or non-PH. The algorithm resulted in a false negative rate of 23% and a false positive rate of 34%. A receiver operating characteristic (ROC) curve for the algorithm is shown in Fig. 4.

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus