  This paper presents a fast and cost-effective method for diagnosing cardiac
abnormalities with high accuracy and reliability using low-cost systems in
clinics. The primary limitation of automatic diagnosing of cardiac diseases is
the rarity of correct and acceptable labeled samples, which can be expensive to
prepare. To address this issue, two methods are proposed in this work. The
first method is a unique Multi-Branch Deep Convolutional Neural Network (MBDCN)
architecture inspired by human auditory processing, specifically designed to
optimize feature extraction by employing various sizes of convolutional filters
and audio signal power spectrum as input. In the second method, called as Long
short-term memory-Convolutional Neural (LSCN) model, Additionally, the network
architecture includes Long Short-Term Memory (LSTM) network blocks to improve
feature extraction in the time domain. The innovative approach of combining
multiple parallel branches consisting of the one-dimensional convolutional
layers along with LSTM blocks helps in achieving superior results in audio
signal processing tasks. The experimental results demonstrate superiority of
the proposed methods over the state-of-the-art techniques. The overall
classification accuracy of heart sounds with the LSCN network is more than 96%.
The efficiency of this network is significant compared to common feature
extraction methods such as Mel Frequency Cepstral Coefficients (MFCC) and
wavelet transform. Therefore, the proposed method shows promising results in
the automatic analysis of heart sounds and has potential applications in the
diagnosis and early detection of cardiovascular diseases.
