A Markov-Switching Model Approach to Heart Sound Segmentation and Classification

Noman, Fuad; Salleh, Shaharuddin; Ting, Chee-Ming; Samdin, S. Balqis; Ombao, Hernando; Hussain, Hadri

doi:10.1109/jbhi.2019.2925036

Cited by 46 publications

(22 citation statements)

References 46 publications

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“…Continuous HMMs with Gaussian mixture densities were used for modeling the temporal structure in PCG. We extracted a set of features as in [18]. A sequence of 12 × 1 short-time Mel-frequency cepstral coefficients (MFCCs) were computed over consecutive windowed frames for each heartbeat to obtain a two-dimensional 12×T time-frequency representation with T the total number of feature vectors.…”

Section: Baseline Classifiersmentioning

confidence: 99%

Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks

Noman

Ting

Salleh

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats. We design a 1D-CNN that directly learns features from raw heart-sound signals, and a 2D-CNN that takes inputs of twodimensional time-frequency feature maps based on Mel-frequency cepstral coefficients (MFCC). We further develop a time-frequency CNN ensemble (TF-ECNN) combining the 1D-CNN and 2D-CNN based on score-level fusion of the class probabilities. On the large PhysioNet CinC challenge 2016 database, the proposed CNN models outperformed traditional classifiers based on support vector machine and hidden Markov models with various hand-crafted time-and frequency-domain features. Best classification scores with 89.22% accuracy and 89.94% sensitivity were achieved by the ECNN, and 91.55% specificity and 88.82% modified accuracy by the 2D-CNN alone on the test set.

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Section: Baseline Classifiersmentioning

confidence: 99%

Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks

Noman

Ting

Salleh

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…The scalograms are obtained using a continuous wavelet transform. In [14] adaptive sojourn hidden semi Markov model (HSMM) based heart sound segmentation is performed. In [15] adaptive sojourn hidden semi Markov model (HSMM) based heart sound segmentation is performed.…”

Section: Motivationmentioning

confidence: 99%

“…In [15] adaptive sojourn hidden semi Markov model (HSMM) based heart sound segmentation is performed. In [14] Markov switching autoregressive model used for model the raw heart sounds for heart sound segmentation. In [16] the features obtained from variational mode decomposition and Hilbert transformation are utilized with machine learning methods for identification of S1 and S2 heart sounds.…”

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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“…The HMM was applied for adult heart sound analysis by Springer, Lima and Schmidt et al [14]- [16]. The Markov-Switching Model was also used by Noman et al [17] for adult heart sound segmentation and classification. These results inspired us to implement the HMM in this work for the dominant test-frequency prediction.…”

Section: Introductionmentioning

confidence: 99%

A Phonography-Based Method Improved by Hidden Markov Model for Fetal Breathing Movement Detection

Goda

Telek²

2021

IEEE Access

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This paper proposes a novel phonography-based method for Fetal Breathing Movement (FBM) detection by its excitation sounds. It requires significantly less effort than the current procedures, and it allows long-term measurement, even at home. More than 50 pregnancies in the third trimester were examined, for a minimum of 20 minutes, taking synchronous long-term measurements using a commercial phonocardiographic fetal monitor and a 3D ultrasound machine. To analyze the gained chaotic signal, the frequency band was split into single test-frequencies in the 15-35 Hz frequency band, and their signal-free (silent) zones were regarded as the starting point (SP) of the next motions. The analysis made other disturbing signals, such as fetal hiccups, trunk rotation and limb movements, or maternal heart beats, distinguishable. The dominant test-frequencies of the analysis were predicted by a Hidden Markov Model (HMM). The SPs of the motion units (episodes) were determined by some features of the FBM, applying weighting factors. The recorded material lasted for 16 hours altogether (with nearly 3.5 hours of FBM). Based on the results of HMM method, nearly 7500 FBM episodes were identified in the phonogram signal with an average length of 0.96±0.13 seconds. The procedure for phonography-based breathing movement detection can be combined with a fetal heart activity measurement, and thus allows very intensive, long-term monitoring of the fetus.

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A Markov-Switching Model Approach to Heart Sound Segmentation and Classification

Cited by 46 publications

References 46 publications

Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks

Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks

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

A Phonography-Based Method Improved by Hidden Markov Model for Fetal Breathing Movement Detection

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