Obstructive sleep apnoea detection using convolutional neural network based deep learning framework

Dey, Debangshu; Chaudhuri, Sayanti; Munshi, Sugata

doi:10.1007/s13534-017-0055-y

Cited by 116 publications

(68 citation statements)

References 12 publications

(15 reference statements)

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“…Hand motion recognition [9][10][11][12][13][14][15][16][17], Muscle activity recognition [18][19][20][21][22][23] ECG Heartbeat signal classification , Heart disease classification [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], Sleep-stage classification [64][65][66][67][68], Emotion classification [69], age and gender prediction [70] EEG Brain functionality classification , Brain disease classification , Emotion classification [122][123][124][125][126][127][128][129], Sleep-stage classification [130][131][132][133]…”

Section: Emgmentioning

confidence: 99%

Deep Learning in Physiological Signal Data: A Survey

Rim

Sung

Min

et al. 2020

Sensors

157

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Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources.

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Section: Emgmentioning

confidence: 99%

Deep Learning in Physiological Signal Data: A Survey

Rim

Sung

Min

et al. 2020

Sensors

157

View full text Add to dashboard Cite

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“…Whether denoising images will improve our template-based segmentation is an interesting future research topic [32]. In addition, segmenting myocardium in PET using fast-growing deep learning approach that outperforms conventional signal and image processing algorithms for some applications is of interest [33][34][35][36][37]. Also, the generation of synthetic lesions in PET images will be a useful method to compare the performance of different approaches for myocardial segmentation [32,38,39].…”

Section: Tablementioning

confidence: 99%

Multi-atlas cardiac PET segmentation

et al. 2019

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We propose a multi-atlas based segmentation method for cardiac PET and SPECT images to deal with the high variability of tracer uptake characteristics in myocardium. In addition, we verify its performance by comparing it to the manual segmentation and single-atlas based approach, using dynamic myocardial PET. Methods: Twelve left coronary artery ligated SD rats underwent ([ 18 F]fluoropentyl) triphenylphosphonium salt PET/CT scans. Atlas-based segmentation is based on the spatial normalized template with pre-defined region-ofinterest (ROI) for each anatomical or functional structure. To generate multiple left ventricular (LV) atlases, each LV image was segmented manually and divided into angular segments. The segmentation methods performances were compared in regional count information using leave-one-out cross-validation. Additionally, the polar-maps of kinetic parameters were estimated. Results: In all images, the highest r 2 template yielded the lowest root-mean-square error (RMSE) between the source image and the best-matching templates ranged between 0.91-0.97 and 0.06-0.11, respectively. The single-atlas and multi-atlas based ROIs yielded remarkably different perfusion distributions: only the multi-atlas based segmentation showed equivalent high correlation results (r 2 = 0.92) with the manual segmentation compared with the single-atlas based (r 2 = 0.88). The high perfusion value underestimation was remarkable in single-atlas based segmentation. Conclusions: The main advantage of the proposed multi-atlas based cardiac segmentation method is that it does not require any prior information on the tracer distribution to be incorporated into the image segmentation algorithms. Therefore, the same procedure suggested here is applicable to any other cardiac PET or SPECT imaging agents without modification.

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“…In [27], a sequential CNN model outperformed the other CNN structures like multi-task learning based CNN. The CNN architecture has an advantage of capturing high level information that is directly related to the problem [28]. However, researchers still are using predefined model based features [29], such as Fourier based synchronization features using handcrafted sinusoidal basis functions [30], before the CNN classification process.…”

Section: Introductionmentioning

confidence: 99%

Deep-ACTINet: End-to-End Deep Learning Architecture for Automatic Sleep-Wake Detection Using Wrist Actigraphy

et al. 2019

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Sleep scoring is the first step for diagnosing sleep disorders. A variety of chronic diseases related to sleep disorders could be identified using sleep-state estimation. This paper presents an end-to-end deep learning architecture using wrist actigraphy, called Deep-ACTINet, for automatic sleep-wake detection using only noise canceled raw activity signals recorded during sleep and without a feature engineering method. As a benchmark test, the proposed Deep-ACTINet is compared with two conventional fixed model based sleep-wake scoring algorithms and four feature engineering based machine learning algorithms. The datasets were recorded from 10 subjects using three-axis accelerometer wristband sensors for eight hours in bed. The sleep recordings were analyzed using Deep-ACTINet and conventional approaches, and the suggested end-to-end deep learning model gained the highest accuracy of 89.65%, recall of 92.99%, and precision of 92.09% on average. These values were approximately 4.74% and 4.05% higher than those for the traditional model based and feature based machine learning algorithms, respectively. In addition, the neuron outputs of Deep-ACTINet contained the most significant information for separating the asleep and awake states, which was demonstrated by their high correlations with conventional significant features. Deep-ACTINet was designed to be a general model and thus has the potential to replace current actigraphy algorithms equipped in wristband wearable devices.

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Obstructive sleep apnoea detection using convolutional neural network based deep learning framework

Cited by 116 publications

References 12 publications

Deep Learning in Physiological Signal Data: A Survey

Deep Learning in Physiological Signal Data: A Survey

Multi-atlas cardiac PET segmentation

Deep-ACTINet: End-to-End Deep Learning Architecture for Automatic Sleep-Wake Detection Using Wrist Actigraphy

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