Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis

Abstract

The aggravation of left ventricular diastolic dysfunction (LVDD) could lead to ventricular remodeling, wall stiffness, reduced compliance, and progression to heart failure with a preserved ejection fraction. A non-invasive method based on convolutional neural networks (CNN) and heart sounds (HS) is presented for the early diagnosis of LVDD in this paper. A deep convolutional generative adversarial networks (DCGAN) model-based data augmentation (DA) method was proposed to expand a HS database of LVDD for model training. Firstly, the preprocessing of HS signals was performed using the improved wavelet denoising method. Secondly, the logistic regression based hidden semi-Markov model was utilized to segment HS signals, which were subsequently converted into spectrograms for DA using the short-time Fourier transform (STFT). Finally, the proposed method was compared with VGG-16, VGG-19, ResNet-18, ResNet-50, DenseNet-121, and AlexNet in terms of performance for LVDD diagnosis. The result shows that the proposed method has a reasonable performance with an accuracy of 0.987, a sensitivity of 0.986, and a specificity of 0.988, which proves the effectiveness of HS analysis for the early diagnosis of LVDD and demonstrates that the DCGAN-based DA method could effectively augment HS data.

Deep Learning for Heart Sound Analysis: A Literature Review

“…In the reviewed literature, most studies use CNN models with 2 to 34 convolutional layers [9, 10, 11, 13, 14, 15, 18, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 47, 48, 50, 51, 52, 55, 58, 59, 60, 61, 68, 69, 71, 72, 73, 84], which are usually equipped with rectified linear units, batch normalization, dropout and pooling components, and some of the layers are linked by residual connections. Wang et al [76] test 10 different CNN models including GoogleNet, SqueezeNet, DarkNet19, ModileNetv2, Inception-ResNetv2, DenseNet201, Inceptionv3, ResNet101, NasNet-Large, and Xception to compare the performances.…”

“…At last, 71 original articles were included. These studies can be broadly categorized into several groups: methods (15 papers, including heart sound segmentation [6, 7, 8, 9, 10, 11, 12, 13], noise cancellation [14, 15, 16], algorithm development [17, 18, 19], and database development [20]), cardiac murmurs detection (36 papers [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56]), valvular heart disease (6 papers [57, 58, 59, 60, 61, 62]), congenital heart disease (4 papers [63, 64, 65, 66]), heart failure (4 papers [67, 68, 69, 70]), coronary artery disease (2 papers [71, 72]), rheumatic heart disease (2 papers [73, 74]), and extracardiac applications (2 papers [75, 76]).…”

Association between phonocardiography and echocardiography in heart failure patients with preserved ejection fraction

“… 11 Convolutional neural network trained with a short-time Fourier transform spectrum is reported to show 99% of sensitivity and specificity in distinguishing LV diastolic dysfunction ( n = 30) from healthy controls ( n = 41). 10 The LV diastolic dysfunction group was a mixture of HFrEF and HFpEF patients (LVEF: 45% ± 16%) with a high E / e ′ ratio (18.6 ± 6.7).…”

Artificial intelligence and heart failure: A state‐of‐the‐art review

“…Studies show excellent performance metrics with these (Table 3). [21][22][23][24][25][26][27][28][29][30][31][32][33][34] By detecting subtle changes in cardiac structure and function over time, ML algorithms can assist in identifying early indicators of HF. This early detection can facilitate timely interventions and preventative measures, thereby potentially reducing the burden of HF and enhancing patient outcomes.…”

“…29 In another study, a learning model based on CNN and augmented data of heart sounds demonstrated even higher performance metrics (accuracy of 0.987, sensitivity of 0.986, and specificity of 0.988) in establishing an early diagnosis of left ventricular diastolic dysfunction. 26 Heart sound-based AI models present a potentially straightforward and non-invasive method of HF screening, although additional clinical implementation and validation are required.…”