Fundamental Heart Sound Classification using the Continuous Wavelet Transform and Convolutional Neural Networks

Meintjes, Andries; Lowe, Andrew; Legget, Malcolm E.

doi:10.1109/embc.2018.8512284

Cited by 44 publications

(24 citation statements)

References 10 publications

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“…In fact, the gold standard algorithm presented by Liang et al [21] proved an average sensitivity of 94% [21], whereas their later improvement [22] correctly recognized the 97% of the heart sounds. More recent studies [12,13,14,15,16,17,18,19,20], based on a wide range of different techniques, achieve sensitivity values up to 99%, with two exceptions [8,17] reaching 99.3% and 99.4%.…”

Section: Resultsmentioning

confidence: 99%

“…The latter include wavelets, empirical mode decomposition, and time frequency representations [5,6,11]. All these possibilities proved to be suitable for obtaining average sensitivity values ranging from 73% to 99.4% [5,8,12,13,14,15,16,17,18,19,20]. To date, machine learning approaches are combined with traditional techniques to increase their automation and further improve their performance [5].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Method for Measuring the Timing of Heart Sound Components through Digital Phonocardiography

Giordano

Knaflitz

2019

Sensors

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The auscultation of heart sounds has been for decades a fundamental diagnostic tool in clinical practice. Higher effectiveness can be achieved by recording the corresponding biomedical signal, namely the phonocardiographic signal, and processing it by means of traditional signal processing techniques. An unavoidable processing step is the heart sound segmentation, which is still a challenging task from a technical viewpoint—a limitation of state-of-the-art approaches is the unavailability of trustworthy techniques for the detection of heart sound components. The aim of this work is to design a reliable algorithm for the identification and the classification of heart sounds’ main components. The proposed methodology was tested on a sample population of 24 healthy subjects over 10-min-long simultaneous electrocardiographic and phonocardiographic recordings and it was found capable of correctly detecting and classifying an average of 99.2% of the heart sounds along with their components. Moreover, the delay of each component with respect to the corresponding R-wave peak and the delay among the components of the same heart sound were computed: the resulting experimental values are coherent with what is expected from the literature and what was obtained by other studies.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Method for Measuring the Timing of Heart Sound Components through Digital Phonocardiography

Giordano

Knaflitz

2019

Sensors

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“…The obtained features are fed to DNN based stacked autoencoder classifier. In [13] the S1 and S2 scalograms are classified using DNN. The scalograms are obtained using a continuous wavelet transform.…”

Section: Motivationmentioning

confidence: 99%

Automatic Recognition of Fundamental Heart Sound Segments From PCG Corrupted With Lung Sounds and Speech

Babu

Ramkumar

2020

IEEE Access

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Automated recognition of fundamental heart sound segments (FHSS) from Phonocardiogram (PCG) is the preliminary step before clinical parameters extraction to detect the presence of abnormality if any. PCG acquisition systems are usually based on microphones. These microphones apart from cardiac sounds will also pick up non-cardiac sounds like lung sounds and speech. The recognition of FHSS is challenging in the presence of non-cardiac events. Deep learning techniques like convolutional neural network (CNN) and recurrent neural network (RNN) are suitable for automated FHSS. However, it will be shown that their performance is degraded in the presence of interference like lung sounds, and speech. Hence in this work, a combination of conventional signal processing technique with deep neural network (DNN) is proposed to enhance the accuracy of automated FHSS. The conventional signal processing technique is based on EWT which can adaptively design the filter banks based on the type of interference. For DNN, U-Net is considered. The method involves the segmentation of PCG using EWT and recognition of FHSS using U-Net based DNN. Envelope features are extracted from the EWT based reconstructed signal and used for training the U-Net based DNN to recognize FHSS. To further improve the recognition accuracy of FHSS, delineation parameters obtained from EWT are incorporated for temporal modeling with the outcomes of U-Net based DNN. The performance of the proposed method is analyzed using both real-time signals and signals taken from standard databases like the Physionet database, and Littmann's lung sound library. Realtime PCG is acquired using an in-house developed PCG acquisition system. The proposed U-Net based DNN with the EWT method achieves FHSS recognition accuracy of 91.17% for PCG with lung sound interference and 90.78% for PCG with speech interference. The proposed method significantly improves the accuracy of FHSS recognition compared to long short term memory (LSTM), and gated recurrent unit (GRU). INDEX TERMS Recognition, segmentation, fundamental heart sound, lung sound, speech, empirical wavelet transform (EWT), deep neural network (DNN).

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“…Combination of continuous wavelet transform (CWT) and CNN is often used in classification of time-series data [8,30,31,32]. This is taking advantage of the high frequency and time resolution provided by CWT, allowing detail analysis at various times and frequency.…”

Section: Related Workmentioning

confidence: 99%

Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework

Yahya

Musa

Ong

et al. 2019

Sensors

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In this work, an algorithm for the classification of six motor functions from an electroencephalogram (EEG) signal that combines a common spatial pattern (CSP) filter and a continuous wavelet transform (CWT), is investigated. The EEG data comprise six grasp-and-lift events, which are used to investigate the potential of using EEG as input signals with brain computer interface devices for controlling prosthetic devices for upper limb movement. Selected EEG channels are the ones located over the motor cortex, C3, Cz and C4, as well as at the parietal region, P3, Pz and P4. In general, the proposed algorithm includes three main stages, band pass filtering, CSP filtering, and wavelet transform and training on GoogLeNet for feature extraction, feature learning and classification. The band pass filtering is performed to select the EEG signal in the band of 7 Hz to 30 Hz while eliminating artifacts related to eye blink, heartbeat and muscle movement. The CSP filtering is applied on two-class EEG signals that will result in maximizing the power difference between the two-class dataset. Since CSP is mathematically developed for two-class events, the extension to the multiclass paradigm is achieved by using the approach of one class versus all other classes. Subsequently, continuous wavelet transform is used to convert the band pass and CSP filtered signals from selected electrodes to scalograms which are then converted to images in grayscale format. The three scalograms from the motor cortex regions and the parietal region are then combined to form two sets of RGB images. Next, these RGB images become the input to GoogLeNet for classification of the motor EEG signals. The performance of the proposed classification algorithm is evaluated in terms of precision, sensitivity, specificity, accuracy with average values of 94.8%, 93.5%, 94.7%, 94.1%, respectively, and average area under the receiver operating characteristic (ROC) curve equal to 0.985. These results indicate a good performance of the proposed algorithm in classifying grasp-and-lift events from EEG signals.

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Fundamental Heart Sound Classification using the Continuous Wavelet Transform and Convolutional Neural Networks

Cited by 44 publications

References 10 publications

A Novel Method for Measuring the Timing of Heart Sound Components through Digital Phonocardiography

A Novel Method for Measuring the Timing of Heart Sound Components through Digital Phonocardiography

Automatic Recognition of Fundamental Heart Sound Segments From PCG Corrupted With Lung Sounds and Speech

Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework

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