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.
We hypothesized that vibrations created by the pulmonary circulation would create sound like the vocal cords during speech and that subjects with pulmonary artery hypertension (PAH) might have a unique sound signature. We recorded heart sounds at the cardiac apex and the second left intercostal space (2LICS), using a digital stethoscope, from 27 subjects (12 males) with a median age of 7 years (range: 3 months-19 years) undergoing simultaneous cardiac catheterization. Thirteen subjects had mean pulmonary artery pressure (mPAp) < 25 mmHg (range: 8-24 mmHg). Fourteen subjects had mPAp ≥ 25 mmHg (range: 25-97 mmHg). We extracted the relative power of the frequency band, the entropy, and the energy of the sinusoid formants from the heart sounds. We applied linear discriminant analysis with leave-one-out cross validation to differentiate children with and without PAH. The significance of the results was determined with a t test and a rank-sum test. The entropy of the first sinusoid formant contained within an optimized window length of 2 seconds of the heart sounds recorded at the 2LICS was significantly lower in subjects with mPAp ≥ 25 mmHg relative to subjects with mPAp < 25 mmHg, with a sensitivity of 93% and specificity of 92%. The reduced entropy of the first sinusoid formant of the heart sounds in children with PAH suggests the existence of an organized pattern. The analysis of this pattern revealed a unique sound signature, which could be applied to a noninvasive method to diagnose PAH.Keywords: pulmonary hypertension, congenital heart disease, auscultation, machine learning, language recognition. Untreated pulmonary artery hypertension (PAH) is a progressive, fatal disease. 1 It complicates many conditions and may affect up to 100 million people worldwide. 2,3 PAH is difficult to diagnose because symptoms appear late in the disease course and the findings on clinical examination are missed easily.The finding on auscultation of a loud pulmonary component of the second heart sound (S2) in PAH has led to the exploration of phonocardiographic associations between S2 and pulmonary artery pressure (PAp) in the time domain. 4-10 However, precise demarcation, timing, and segmentation of the components of S2 remain challenging. [7][8][9][11][12][13] We have explored instead quantitative information in the frequency domain of heart sounds that distinguish between subjects with and without PAH. 14 The relative power of the frequencies between 21 and 22 Hz of the heart sounds recorded at the second left intercostal space (2LICS) was significantly reduced in subjects with PAH. 14 However, there was a 22% error in detecting PAH. Therefore, by investigating further the recordings in these same subjects, we sought to explore other features of the heart sounds in this specific frequency domain that might contain a unique feature that would identify subjects with PAH.Normal speech patterns have a unique signature related to vocal cord vibration, which can be used, for example, to recognize a speaker as male or female...
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