Synthesis of Normal Heart Sounds Using Generative Adversarial Networks and Empirical Wavelet Transform

applied sciences Article Synthesis of Normal Heart Sounds Using Generative Adversarial Networks and Empirical Wavelet Transform Pedro Narváez * and Winston S. Percybrooks Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081001, Colombia; [email protected] * Correspondence: [email protected] Received: 31 July 2020; Accepted: 12 September 2020; Published: 8 October 2020 Abstract: Currently, there are many works in the literature focused on the analysis of heart sounds, specifically on the development of intelligent systems for the classification of normal and abnormal heart sounds. However, the available heart sound databases are not yet large enough to train generalized machine learning models. Therefore, there is interest in the development of algorithms capable of generating heart sounds that could augment current databases. In this article, we propose a model based on generative adversary networks (GANs) to generate normal synthetic heart sounds. Additionally, a denoising algorithm is implemented using the empirical wavelet transform (EWT), allowing a decrease in the number of epochs and the computational cost that the GAN model requires. A distortion metric (mel–cepstral distortion) was used to objectively assess the quality of synthetic heart sounds. The proposed method was favorably compared with a mathematical model that is based on the morphology of the phonocardiography (PCG) signal published as the state of the art. Additionally, different heart sound classification models proposed as state-of-the-art were also used to test the performance of such models when the GAN-generated synthetic signals were used as test dataset. In this experiment, good accuracy results were obtained with most of the implemented models, suggesting that the GAN-generated sounds correctly capture the characteristics of natural heart sounds. Keywords: generative adversarial network; heart sound classification; EWT; sound synthesis; machine learning; e-health 1. Introduction Cardiovascular diseases are one of the leading causes of death in the world. According to recent reports from the World Health Organization and the American Heart Association, more than 17 million people die each year from these diseases. Most of these deaths (about 80%) occur in low- and middle-income countries [1,2]. Tobacco use, unhealthy eating, and lack of physical activity are the main causes of heart disease [1]. Currently, there are sophisticated equipment and tests for diagnosing heart disease, such as: electrocardiogram, holter monitoring, echocardiogram, stress test, cardiac catheterization, computed tomography scan, and magnetic resonance imaging [3]. However, most of this equipment is very expensive, and must be used by specialized technicians and medical doctors, which limits its availability in rural and urban areas that do not have the necessary financial resources [4]. Therefore, even today, it is common in such scenarios for non-specialized medical personnel to rely on basic auscultation with an stethoscope as a primary screening tool for the detection of many cardiac abnormalities and heart diseases [5]. However, to be effective, this method requires a sufficiently trained ear to identify cardiac Appl. Sci. 2020, 10, 7003; doi:10.3390/app10197003 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, x 7003 FOR PROOFREADING 2 of 16 trained ear to identify cardiac conditions. Unfortunately, the literature suggests that in recent years conditions.such auscultation Unfortunately, training has the been literature in decline suggests [6–8]. that in recent years such auscultation training has beenThis in decline situation [6–8 has]. motivated the development of computer classification models to support the identificationThis situation of normal has motivated and abnormal the development heart sounds ofby computer non-specialized classification health professionals. models to support To date, the identificationmany investigations of normal related and to abnormal the analysis heart and sounds synthesis by non-specialized of heart sounds health (HSs) professionals. have been published, To date, manyobtaining investigations good results related especially to the in analysis the classification and synthesis of normal of heart and sounds abnormal (HSs) heart have sounds been published, [9–25]. obtainingHeart good sounds results are closely especially related in the to classificationboth, the vibration of normal of the and entire abnormal myocardial heart structure sounds [9 and–25]. the vibrationHeart of sounds the heart are valves closely during related closure to both, and the opening. vibration A of recording the entire of myocardial heart sounds structure is composed and the vibrationof series of of cardiac the heart cycles. valves A duringnormal closurecardiac andcycle, opening. as shown A recordingin Figure 1, of is heart composed sounds by is the composed S1 sound of series(generated of cardiac by the cycles. closing A of normal the atrioventricular cardiac cycle, va aslve), shown the inS2 Figure sound1 (generated, is composed by the by theclosing S1 sound of the (generatedsemilunar valve), by the the closing systole of the (range atrioventricular between S1 and valve), S2), the and S2 the sound diastole (generated (range included by the closing between of the S2 semilunarand S1 of the valve), next the cycle) systole [26]. (range Abnormalities between S1are and represented S2), and the by diastole murmurs (range that includedusually occur between in the S2 andsystolic S1 ofor the diastolic next cycle) interval [26 ].[27]. Abnormalities Health professi areonals represented use different by murmurs attributes that of usually heart murmurs occur in thefor systolictheir classification, or diastolic intervalthe most [27 common]. Health are: professionals timing, cadence, use diff duration,erent attributes pitch, and of heart shape murmurs of the murmur for their classification,[28,29]. Therefore the most the commonexaminer are: mu timing,st have cadence, an ear duration,sufficiently pitch, trained and shape to identify of the murmur each of [ 28these,29]. Thereforeattributes. the examiner must have an ear sufficiently trained to identify each of these attributes. Figure 1. Example signal for a normal heart sound. Despite the amount of work related to the classificationclassification of heart sounds, it is still didifficultfficult to statistically evaluate the robustness of these algorithms,algorithms, since the number of samples used in training is not susufficientfficient toto guaranteeguarantee a a generalized generalized model. model. Similarly, Similarly, there there have have been been no no significant significant advances advances in thein the classification classification of specific of specific types oftypes heart of murmurs, heart murmurs, due to the limiteddue to availabilitythe limited of availability corresponding of labelscorresponding in current labels public in databases current [public30,31]. Indatabases this sense, [30,31]. having In a this model sense, for the having generation a model of synthetic for the sounds,generation capable of synthetic of outputting sounds, varied capable synthetic of outputti heartng varied sounds synthetic indistinguishable heart sounds from indistinguishable natural ones by medicalfrom natural personnel, ones by could medical be used pers toonnel, augment could existing be used databases to augment for training existing robust databases machine for learningtraining models.robust machine However, learning heart soundmodels. signals However, are highly heart non-stationary,sound signals are and highly their level non-stationary, of complexity and makes their obtaininglevel of complexity good generative makes obtaining models very good challenging. generative models very challenging. In the the literature, literature, there there are are several several publications publications related related to the to generation the generation of synthetic of synthetic heart sounds heart sounds[16–25]. [16All– 25these]. All works these are works based are on based mathematical on mathematical models models to generate to generate the S1 the and S1 S2 and sections S2 sections of a ofcardiac a cardiac cycle. cycle. On the On other the other hand, hand, the systolic the systolic and anddiastolic diastolic intervals intervals of the of cardiac the cardiac cycle cycle are not are notadequately adequately modeled, modeled, and andas a result, as a result, do not do present not present the variability the variability recorded recorded in natural in natural normal normal heart heartsounds. sounds. Therefore, Therefore, these thesesynthetic synthetic models models are arenot notsuitable suitable to totrain train HS HS classification classification models. Additionally, a basicbasic time–frequencytime–frequency analysis of these synthetic signals shows that they are very didifferentfferent from natural signals. Table 1 1 presents presents aa comparisoncomparison ofof thethe didifferentfferent heart sound generative methods found in the Web ofof Science,Science, Scopus,Scopus, andand IEEEIEEE XploreXplore databases.databases. On the other hand, in recent years there have been great advances in the synthesis of audio, mainly speech, using machine learning techniques, specifically with deep learning. In Table 2, several proposed methods to improve audio synthesis are presented. According to the limitations presented in the currently proposed models for the generation of synthetic heart sounds, and taking into account

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