Deep Learning for Sleep Disorders: A Review

Hepsiba, D; Anand, L.D. Vijay; Princy, R. Jane Preetha

doi:10.1109/icbsii51839.2021.9445159

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…Several potential speech recognition and augmentation methods were described by Hepsiba et al [8]. RNN and DNN were trained to conduct spectral masking, and other techniques such as ResLSTM, MOGA, and DCCRN were carried out to figure out the magnitude spectrograms of the impaired speech.…”

Section: Research Objectivesmentioning

confidence: 99%

Speech Enhancement with Background Noise Suppression in Various Data Corpus Using Bi-LSTM Algorithm

2024

IJEER

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Noise reduction is one of the crucial procedures in today’s teleconferencing scenarios. The signal-to-noise ratio (SNR) is a paramount factor considered for reducing the Bit error rate (BER). Minimizing the BER will result in the increase of SNR which improves the reliability and performance of the communication system. The microphone is the primary audio input device that captures the input signal, as the input signal is carried away it gets interfered with white noise and phase noise. Thus, the output signal is the combination of the input signal and reverberation noise. Our idea is to minimize the interfering noise thus improving the SNR. To achieve this, we develop a real-time speech-enhancing method that utilizes an enhanced recurrent neural network with Bidirectional Long Short Term Memory (Bi-LSTM). One LSTM in this sequence processing framework accepts the input in the forward direction, whereas the other LSTM takes it in the opposite direction, making up the Bi-LSTM. Considering Bi-LSTM, it takes fewer tensor operations which makes it quicker and more efficient. The Bi-LSTM is trained in real-time using various noise signals. The trained system is utilized to provide an unaltered signal by reducing the noise signal, thus making the proposed system comparable to other noise-suppressing systems. The STOI and PESQ metrics demonstrate a rise of approximately 0.5% to 14.8% and 1.77% to 29.8%, respectively, in contrast to the existing algorithms across various sound types and different input signal-to-noise ratio (SNR) levels.

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Section: Research Objectivesmentioning

confidence: 99%

Speech Enhancement with Background Noise Suppression in Various Data Corpus Using Bi-LSTM Algorithm

2024

IJEER

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“…The objective of the present study was to develop a method to accurately detect SA by recording trachea-sternal motion and sound, along with SaO 2 and subjecting these signals to deep neural networks (DNNs) with high computing potential. 16 Recent advances in artificial intelligence approaches, especially development of DNNs, have led to their extensive use in health-care applications and specifically increasing the accuracy of diagnostic sleep apnea testing. 17 We employed these techniques to estimate AHI in a larger population than in previous studies using similar technology.…”

Section: Introductionmentioning

confidence: 99%

Sleep Apnea Detection by Tracheal Motion and Sound, and Oximetry via Application of Deep Neural Networks

Ghahjaverestan¹,

Aguiar²,

Hummel³

et al. 2023

NSS

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Purpose Sleep apnea (SA) is highly prevalent, but under diagnosed due to inaccessibility of sleep testing. To address this issue, portable devices for home sleep testing have been developed to provide convenience with reasonable accuracy in diagnosing SA. The objective of this study was to test the validity a novel portable sleep apnea testing device, BresoDX1, in SA diagnosis, via recording of trachea-sternal motion, tracheal sound and oximetry. Patients and Methods Adults with a suspected sleep disorder were recruited to undergo in-laboratory polysomnography (PSG) and a simultaneous BresoDX1 recording. Data from BresoDX1 were collected and features related to breathing sounds, body motions and oximetry were extracted. A deep neural network (DNN) model was trained with 61-second epochs of the extracted features to detect apneas and hypopneas from which an apnea-hypopnea index (AHI) was calculated. The AHI estimated by BresoDX1 (AHI breso ) was compared to the AHI determined from PSG (AHI PSG ) and the sensitivity and specificity of SA diagnosis were assessed at an AHI PSG ≥ 15. Results Two-hundred thirty-three participants (mean ± SD) 50 ± 16 years of age, with BMI of 29.8 ± 6.6 and AHI of 19.5 ± 22.7, were included. There was a strong relationship between AHI breso and AHI PSG (r = 0.91, p < 0.001). SA detection for an AHI PSG ≥ 15 was highly sensitive (90.0%) and specific (85.9%). Conclusion We conclude that the DNN model we developed via recording and analyses of trachea-sternal motion and sound along with oximetry provides an accurate estimate of the AHI PSG with high sensitivity and specificity. Therefore, BresoDX1 is a simple, convenient and accurate portable SA monitoring device that could be employed for home SA testing in the future.

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“…While [20], [4], and [6] discuss AE applications restricted to speech and music based on a variety of DNNs, the model in [21] is focused only on Deep Reinforcement Learning (DRL) models covering a wide range of applications including Human-Robot Interaction (HRI), music listening and generation, AE, emotions modeling, spoken dialogue systems and automatic speech recognition. The paper in [22] reviews the DNN-based AE models used exclusively for automatic speech recognition applications. The research [23] also reviews only speech enhancement models using deep diffusion networks and [24] is also dedicated to speech enhancement DNN models.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike previously published reviews (e.g. [20], [4], [6], [22], [24], and [26]), which cover all sorts of DNNs, we focus only on image U-Net models used for AE. To the best of our knowledge, this is the first such review of AE, using only 2D U-Nets, is presented.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Audio Enhancement Algorithms for Music, Speech, Bioacoustics, Biomedical, Industrial, and Environmental Sounds by Image U-Net

Gul,

Khan

2023

IEEE Access

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The recent surge in the use of Deep Neural Networks (DNNs) has also made its mark in the field of Audio Enhancement (AE), providing much better quality than the classical methods. Although, there are dedicated audio processing DNNs, yet, many recent models of AE have utilized U-Net: a DNN based on Convolutional Neural Network (CNN), fundamentally developed for image segmentation. It is found that the useful features hidden in the time domain are highlighted when the audio signal is converted to a spectrogram, which can be treated as an image. In this article, we will review the recent work, utilizing U-Nets for different AE applications. Different than other published reviews, this review focuses entirely on AE techniques based on image U-Nets. We will discuss the need for AE, U-Net comparison to other DNNs, the benefits of converting the audio to 2D, input representations that are useful for different AE applications, the architecture of vanilla U-Net and the pre-trained models, variations in vanilla architecture incorporated in different E models, and the state-of-the-art AE algorithms based on U-Net in various applications. Apart from speech and music, this article discusses a wide range of audio signals e.g. environmental, biomedical, bioacoustics, and industrial sounds, not covered collectively in a single article in previously published studies. The article ends with the discussion of colored spectrograms in future AE applications.INDEX TERMS CNNs; image processing deep neural networks; pre-trained networks; spectrogram.;U-Net.

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Deep Learning for Sleep Disorders: A Review

Cited by 6 publications

References 18 publications

Speech Enhancement with Background Noise Suppression in Various Data Corpus Using Bi-LSTM Algorithm

Speech Enhancement with Background Noise Suppression in Various Data Corpus Using Bi-LSTM Algorithm

Sleep Apnea Detection by Tracheal Motion and Sound, and Oximetry via Application of Deep Neural Networks

A Survey of Audio Enhancement Algorithms for Music, Speech, Bioacoustics, Biomedical, Industrial, and Environmental Sounds by Image U-Net

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