Lung sound classification using cepstral-based statistical features

Sengupta, N.; Sahidullah, Md; Saha, Goutam

doi:10.1016/j.compbiomed.2016.05.013

Cited by 146 publications

(81 citation statements)

References 52 publications

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“…Filtering and de-noising are often used to ensure sound quality [2,6,57]. Breath sound features are mostly derived from frequency domain and Mel-Frequency Cepstral Coefficients (MFCCs), which model the signals based on the human sense of hearing, is widely used in different applications [4,31,57,69]. Support-vector machines (SVMs) are the most applied method in the work listed in Table 1.…”

Section: Breathing Detection With a Smartphone Microphonementioning

confidence: 99%

Breeze

Shih

Tomita

Lukic

et al. 2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Slow-paced biofeedback-guided breathing training has been shown to improve cardiac functioning and psychological wellbeing. Current training options, however, attract only a fraction of individuals and are limited in their scalability as they require dedicated biofeedback hardware. In this work, we present Breeze, a mobile application that uses a smartphone's microphone to continuously detect breathing phases, which then trigger a gamified biofeedback-guided breathing training. Circa 2.76 million breathing sounds from 43 subjects and control sounds were collected and labeled to train and test our breathing detection algorithm. We model breathing as inhalation-pause-exhalation-pause sequences and implement a phase-detection system with an attention-based LSTM model in conjunction with a CNN-based breath extraction module. A biofeedback-guided breathing training with Breeze takes place in real-time and achieves 75.5% accuracy in breathing phases detection. Breeze was also evaluated in a pilot study with 16 new subjects, which demonstrated that the majority of subjects prefer Breeze over a validated active control condition in its usefulness, enjoyment, control, and usage intentions. Breeze is also effective for strengthening users' cardiac functioning by increasing high-frequency heart rate variability. The results of our study suggest that Breeze could potentially be utilized in clinical and self-care activities. Sailboat In h a le P a u se E xh a le P a u se … Nose Mouth R e p e a te d a co u st ic b re a th in g se q u e n ce Fig. 1. Overview of Breeze, a mobile gamified biofeedback breathing training.

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Section: Breathing Detection With a Smartphone Microphonementioning

confidence: 99%

Breeze

Shih

Tomita

Lukic

et al. 2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

View full text Add to dashboard Cite

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“…A series of experiments were performed using this novel dataset and performance was compared with the BodyBeat [6] system. Several other shallow and deep learning based pulmonary activity detection works like wheeze detection [7][8][9][10][10][11][12][13][14][15] and cough detection [16][17][18] exist in literature. However, they often use limited training data which is not collected with a commodity smartphone.…”

Section: Introductionmentioning

confidence: 99%

DeepLung: Smartphone Convolutional Neural Network-Based Inference of Lung Anomalies for Pulmonary Patients

2019

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DeepLung is an end-to-end deep learning based audio sensing and classification framework for lung anomaly (e.g. cough, wheeze) detection for pulmonary patients from streaming audio and inertial sensor data from a chest-held smartphone. We design and develop 1-D and 2-D convolutional neural networks for DeepLung, and train them using the Interspeech 2010 Paralinguistic Challenge features. Two different audio windowing schemes: i) real-time respiration cycle based natural windowing, and ii) static length windowing are compared and experimented with. Classifiers are developed considering 2 different system architectures: i) mobile-cloud hybrid architecture, and ii) mobile in-situ architecture. Patient privacy is preserved in the phone by filtering speech with a shallow classifier. To evaluate DeepLung, a novel and rigorous lung activity dataset is made by collecting audio and inertial sensor data from more than 131 real pulmonary patients and healthy subjects and annotated accurately by professional crowdsourcing. Experimental results show that the best combination of DeepLung convolutional neural network is 15-27% more accurate when compared to a stateof-the-art smartphone based body sound detection system, with a best F1 score of 98%.

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“…The Hilbert transform is regularly used in signal processing, mainly, due the property to extend real functions into analytic functions. Signal processing can be considered as a subfield of mathematics, information and electrical engineering that concerns the analysis, synthesis and modification of signals, which are broadly defined as functions conveying 'information about the behavior or attributes of some phenomenon', such as sound, images and biological measurements [1]. The Hilbert transform has many uses, including solving problems in aerodynamics, condensed matter physics, optics, fluids, and engineering.…”

mentioning

confidence: 99%