Classifying Heart Sound Recordings using Deep Convolutional Neural Networks and Mel:Frequency Cepstral Coefficients

Rubin, Jonathan; Abreu, Rui; Ganguli, Anurag; Nelaturi, Saigopal; Matei, Ion; Sricharan, Kumar

doi:10.22489/cinc.2016.236-175

Cited by 82 publications

(80 citation statements)

References 5 publications

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“…In this paper we used Mel-frequency cepstral coefficients (MFCCs) [39] to represent PCG signal in compact representation. MFCCs are used almost in every study on automatic heart sound classification (for example, [24], [40]- [42]) due to their effectiveness in speech analysis. We compute MFCCs from 25ms of the window with a step size of 10ms.…”

Section: Feature Selectionmentioning

confidence: 99%

Phonocardiographic Sensing Using Deep Learning for Abnormal Heartbeat Detection

et al. 2018

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Cardiac auscultation involves expert interpretation of abnormalities in heart sounds using stethoscope. Deep learning based cardiac auscultation is of significant interest to the healthcare community as it can help reducing the burden of manual auscultation with automated detection of abnormal heartbeats. However, the problem of automatic cardiac auscultation is complicated due to the requirement of reliability and high accuracy, and due to the presence of background noise in the heartbeat sound. In this work, we propose a Recurrent Neural Networks (RNNs) based automated cardiac auscultation solution. Our choice of RNNs is motivated by the great success of deep learning in medical applications and by the observation that RNNs represent the deep learning configuration most suitable for dealing with sequential or temporal data even in the presence of noise. We explore the use of various RNN models, and demonstrate that these models deliver the abnormal heartbeat classification score with significant improvement. Our proposed approach using RNNs can be potentially be used for real-time abnormal heartbeat detection in the Internet of Medical Things for remote monitoring applications.

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Section: Feature Selectionmentioning

confidence: 99%

Phonocardiographic Sensing Using Deep Learning for Abnormal Heartbeat Detection

et al. 2018

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“…MFCC is widely used in fields such as speech recognition and analysis of biological sounds such as lung or heart sounds [9,[16][17][18]. MFCC is calculated by performing a discrete cosine transformation on the output from triangular filter banks evenly spaced along a logarithmic axis; this is referred to as a mel scale, and it approximates the human auditory frequency response.…”

Section: Automatic Bs Extraction On the Basis Of Acoustic Featuresmentioning

confidence: 99%

Automatic Bowel Motility Evaluation Technique for Noncontact Sound Recordings

et al. 2018

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Information on bowel motility can be obtained via magnetic resonance imaging (MRI)s and X-ray imaging. However, these approaches require expensive medical instruments and are unsuitable for frequent monitoring. Bowel sounds (BS) can be conveniently obtained using electronic stethoscopes and have recently been employed for the evaluation of bowel motility. More recently, our group proposed a novel method to evaluate bowel motility on the basis of BS acquired using a noncontact microphone. However, the method required manually detecting BS in the sound recordings, and manual segmentation is inconvenient and time consuming. To address this issue, herein, we propose a new method to automatically evaluate bowel motility for noncontact sound recordings. Using simulations for the sound recordings obtained from 20 human participants, we showed that the proposed method achieves an accuracy of approximately 90% in automatic bowel sound detection when acoustic feature power-normalized cepstral coefficients are used as inputs to artificial neural networks. Furthermore, we showed that bowel motility can be evaluated based on the three acoustic features in the time domain extracted by our method: BS per minute, signal-to-noise ratio, and sound-to-sound interval. The proposed method has the potential to contribute towards the development of noncontact evaluation methods for bowel motility.

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“…However, this system was proven only by a case study. Another approach is the use of the tools and techniques of deep learning for the automated analysis of heart sounds [27]. In this paper, an algorithm was presented that accepts PCG waveforms as input and uses a deep convolutional neural network architecture to discriminate between normal and abnormal heart sounds.…”

Section: Related Workmentioning

confidence: 99%

Classifying Heart Sounds Using Images of MFCC and Temporal Features

Nogueira

Ferreira

Jorge

2017

Progress in Artificial Intelligence

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Phonocardiogram signals contain very useful information about the condition of the heart. It is a method of registration of heart sounds, which can be visually represented on a chart. By analyzing these signals, early detections and diagnosis of heart diseases can be done. Intelligent and automated analysis of the phonocardiogram is therefore very important, to determine whether the patient's heart works properly or should be referred to an expert for further evaluation. In this work, we use electrocardiograms and phonocardiograms collected simultaneously, from the Physionet challenge database, and we aim to determine whether a phonocardiogram corresponds to a "normal" or "abnormal" physiological state. The main idea is to translate a 1D phonocardiogram signal into a 2D image that represents temporal and Mel-frequency cepstral coefficients features. To do that, we develop a novel approach that uses both features. First we segment the phonocardiogram signals with an algorithm based on a logistic regression hidden semi-Markov model, which uses the electrocardiogram signals as reference. After that, we extract a group of features from the time and frequency domain (Mel-frequency cepstral coefficients) of the phonocardiogram. Then, we combine these features into a two-dimensional time-frequency heat map representation. Lastly, we run a binary classifier to learn a model that discriminates between normal and abnormal phonocardiogram signals. In the experiments, we study the contribution of temporal and Mel-frequency cepstral coefficients features and evaluate three classification algorithms: Support Vector Machines, Convolutional Neural Network, and Random Forest. The best results are achieved when we map both temporal and Mel-frequency cepstral coefficients features into a 2D image and use the Support Vector Machines with a radial basis function kernel. Indeed, by including both temporal and Mel-frequency cepstral coefficients features, we obtain sligthly better results than the ones reported by the challenge participants, which use large amounts of data and high computational power.

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Classifying Heart Sound Recordings using Deep Convolutional Neural Networks and Mel:Frequency Cepstral Coefficients

Cited by 82 publications

References 5 publications

Phonocardiographic Sensing Using Deep Learning for Abnormal Heartbeat Detection

Phonocardiographic Sensing Using Deep Learning for Abnormal Heartbeat Detection

Automatic Bowel Motility Evaluation Technique for Noncontact Sound Recordings

Classifying Heart Sounds Using Images of MFCC and Temporal Features

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