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Cough event classification by pretrained deep neural network.

Liu JM, You M, Wang Z, Li GZ, Xu X, Qiu Z - BMC Med Inform Decis Mak (2015)

Bottom Line: Then the fine-tuning step is a back propogation tuning the neural network so that it can predict the observation probability associated with each HMM states, where the HMM states are originally achieved by force-alignment with a Gaussian Mixture Model Hidden Markov Model (GMM-HMM) on the training samples.From overall evaluation criteria, the DNN based methods are superior to traditional GMM-HMM based method on F1 and micro average with maximal 14% and 11% error reduction in PD and 7% and 10% in PI, meanwhile keep similar performances on macro average.They also surpass GMM-HMM model on specificity with maximal 14% error reduction on both PD and PI.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Cough is an essential symptom in respiratory diseases. In the measurement of cough severity, an accurate and objective cough monitor is expected by respiratory disease society. This paper aims to introduce a better performed algorithm, pretrained deep neural network (DNN), to the cough classification problem, which is a key step in the cough monitor.

Method: The deep neural network models are built from two steps, pretrain and fine-tuning, followed by a Hidden Markov Model (HMM) decoder to capture tamporal information of the audio signals. By unsupervised pretraining a deep belief network, a good initialization for a deep neural network is learned. Then the fine-tuning step is a back propogation tuning the neural network so that it can predict the observation probability associated with each HMM states, where the HMM states are originally achieved by force-alignment with a Gaussian Mixture Model Hidden Markov Model (GMM-HMM) on the training samples. Three cough HMMs and one noncough HMM are employed to model coughs and noncoughs respectively. The final decision is made based on viterbi decoding algorihtm that generates the most likely HMM sequence for each sample. A sample is labeled as cough if a cough HMM is found in the sequence.

Results: The experiments were conducted on a dataset that was collected from 22 patients with respiratory diseases. Patient dependent (PD) and patient independent (PI) experimental settings were used to evaluate the models. Five criteria, sensitivity, specificity, F1, macro average and micro average are shown to depict different aspects of the models. From overall evaluation criteria, the DNN based methods are superior to traditional GMM-HMM based method on F1 and micro average with maximal 14% and 11% error reduction in PD and 7% and 10% in PI, meanwhile keep similar performances on macro average. They also surpass GMM-HMM model on specificity with maximal 14% error reduction on both PD and PI.

Conclusions: In this paper, we tried pretrained deep neural network in cough classification problem. Our results showed that comparing with the conventional GMM-HMM framework, the HMM-DNN could get better overall performance on cough classification task.

No MeSH data available.


Related in: MedlinePlus

Performances on patient dependent test set. The results are shown as a function of the number of layers and number of hidden units in each layer. The performance of baseline was generated from a conventional GMM-HMM model.
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Figure 3: Performances on patient dependent test set. The results are shown as a function of the number of layers and number of hidden units in each layer. The performance of baseline was generated from a conventional GMM-HMM model.

Mentions: The results that earned from patient dependent test set is shwon in Figure 3, Table 3 and Table 4. In Figure 3, the above pictures showed the results from pretrained deep neural networks and the below pictures from neural networks that were randomly initialized. The bold numbers in the tables are the best results in all of the DNN models. Overall, without pretrain step, the neural network can only surpass the baseline on F1 and micro average. After the pretrain step added, the DNN based models reached the same level of macro average as baseline, and they are also superior than baseline on F1 and micro average. The best results for F1, macro average and micro average achieved by DNNs are 0.705, 0,863, and 0.905 respectively, the baseline counterparts are 0.563, 0.856, 0.792 respectively. In sensitivity, the baseline outperforms all of the DNN models. At the same time, all of the DNN models outperform the baseline by a large extend. We get 14% error reduction on F1 and 11% error reduction on micro average on this test set.


Cough event classification by pretrained deep neural network.

Liu JM, You M, Wang Z, Li GZ, Xu X, Qiu Z - BMC Med Inform Decis Mak (2015)

Performances on patient dependent test set. The results are shown as a function of the number of layers and number of hidden units in each layer. The performance of baseline was generated from a conventional GMM-HMM model.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4660085&req=5

Figure 3: Performances on patient dependent test set. The results are shown as a function of the number of layers and number of hidden units in each layer. The performance of baseline was generated from a conventional GMM-HMM model.
Mentions: The results that earned from patient dependent test set is shwon in Figure 3, Table 3 and Table 4. In Figure 3, the above pictures showed the results from pretrained deep neural networks and the below pictures from neural networks that were randomly initialized. The bold numbers in the tables are the best results in all of the DNN models. Overall, without pretrain step, the neural network can only surpass the baseline on F1 and micro average. After the pretrain step added, the DNN based models reached the same level of macro average as baseline, and they are also superior than baseline on F1 and micro average. The best results for F1, macro average and micro average achieved by DNNs are 0.705, 0,863, and 0.905 respectively, the baseline counterparts are 0.563, 0.856, 0.792 respectively. In sensitivity, the baseline outperforms all of the DNN models. At the same time, all of the DNN models outperform the baseline by a large extend. We get 14% error reduction on F1 and 11% error reduction on micro average on this test set.

Bottom Line: Then the fine-tuning step is a back propogation tuning the neural network so that it can predict the observation probability associated with each HMM states, where the HMM states are originally achieved by force-alignment with a Gaussian Mixture Model Hidden Markov Model (GMM-HMM) on the training samples.From overall evaluation criteria, the DNN based methods are superior to traditional GMM-HMM based method on F1 and micro average with maximal 14% and 11% error reduction in PD and 7% and 10% in PI, meanwhile keep similar performances on macro average.They also surpass GMM-HMM model on specificity with maximal 14% error reduction on both PD and PI.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Cough is an essential symptom in respiratory diseases. In the measurement of cough severity, an accurate and objective cough monitor is expected by respiratory disease society. This paper aims to introduce a better performed algorithm, pretrained deep neural network (DNN), to the cough classification problem, which is a key step in the cough monitor.

Method: The deep neural network models are built from two steps, pretrain and fine-tuning, followed by a Hidden Markov Model (HMM) decoder to capture tamporal information of the audio signals. By unsupervised pretraining a deep belief network, a good initialization for a deep neural network is learned. Then the fine-tuning step is a back propogation tuning the neural network so that it can predict the observation probability associated with each HMM states, where the HMM states are originally achieved by force-alignment with a Gaussian Mixture Model Hidden Markov Model (GMM-HMM) on the training samples. Three cough HMMs and one noncough HMM are employed to model coughs and noncoughs respectively. The final decision is made based on viterbi decoding algorihtm that generates the most likely HMM sequence for each sample. A sample is labeled as cough if a cough HMM is found in the sequence.

Results: The experiments were conducted on a dataset that was collected from 22 patients with respiratory diseases. Patient dependent (PD) and patient independent (PI) experimental settings were used to evaluate the models. Five criteria, sensitivity, specificity, F1, macro average and micro average are shown to depict different aspects of the models. From overall evaluation criteria, the DNN based methods are superior to traditional GMM-HMM based method on F1 and micro average with maximal 14% and 11% error reduction in PD and 7% and 10% in PI, meanwhile keep similar performances on macro average. They also surpass GMM-HMM model on specificity with maximal 14% error reduction on both PD and PI.

Conclusions: In this paper, we tried pretrained deep neural network in cough classification problem. Our results showed that comparing with the conventional GMM-HMM framework, the HMM-DNN could get better overall performance on cough classification task.

No MeSH data available.


Related in: MedlinePlus