A Novel CNN-Based Framework for Classification of Signal Quality and Sleep Position from a Capacitive ECG Measurement

Kido, Koshiro; Tamura, Toshiyo; Ono, Naoaki; Altaf‐Ul‐Amin, Md.; Shimizu, Masaki; Kanaya, Shigehiko; Huang, Ming

doi:10.3390/s19071731

Cited by 33 publications

(21 citation statements)

References 18 publications

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“…One of the main challenges in the use of these technologies is the presence of motion artefacts (MA) [2]. To partially overcome this, researchers have proposed multiple solutions, with the use of signal quality indicators (SQIs) [3], [4] being more suitable for real-life scenarios.…”

Section: Introductionmentioning

confidence: 99%

Unobtrusive, through-clothing ECG and Bioimpedance Monitoring in Sleep Apnea Patients

Castro

Patel

Deviaene

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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A real-life validation of a system for simultaneous acquisition of capacitively-coupled ECG (ccECG) and capacitively-coupled bioimpedance (ccBioz) is presented. The heart rate (HR) and respiration rate (RR) estimation performance was evaluated using polysomnography (PSG) signals as ground-truth, in recordings from 28 patients with suspected obstructive sleep apnea (OSA). A ccECG beat detection sensitivity of 98.4% and an R-R interval mean absolute error (MAE) of 17.1 ms were achieved when applying quality-based algorithms. RR MAE values of 3.48 and 6.37 breaths per minute were also achieved when using two different RR extraction methods. High similarity between unobtrusive signals and PSG ground-truth was observed, with a correlation between ccECG and psgECG of 91.5% and a correlation between ccBioz and PSG thoracic belt (TB) of 89.5%. Even in episodes containing OSA events, the characteristic respiration behavior of TB signals was also observed in the ccBioz signals. This shows the potential of ccECG and ccBioz for use in long-term monitoring without adding discomfort to the patient or user. Sleep-related applications as well as more generic cardiorespiratory monitoring in (patient) beds are obvious applications, but also other daily life monitoring can be done using a similar approach (e.g. in seats).

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Section: Introductionmentioning

confidence: 99%

Unobtrusive, through-clothing ECG and Bioimpedance Monitoring in Sleep Apnea Patients

Castro

Patel

Deviaene

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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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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“…Lee et al tested the potential of their ccECG recording system for detection of REM sleep and wakefulness through heart rate variability parameters [ 7 ]. Furthermore, Kido et al classified the sleeping position from ccECG as a step towards personal healthcare [ 8 ]. Deviaene et al applied the ccECG and ccBioZ on an apnea detection algorithm [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Capacitively-Coupled ECG and Respiration for Sleep–Wake Prediction and Risk Detection in Sleep Apnea Patients

Huysmans

Castro

Borzée

et al. 2021

Sensors

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Obstructive sleep apnea (OSA) patients would strongly benefit from comfortable home diagnosis, during which detection of wakefulness is essential. Therefore, capacitively-coupled electrocardiogram (ccECG) and bioimpedance (ccBioZ) sensors were used to record the sleep of suspected OSA patients, in parallel with polysomnography (PSG). The three objectives were quality assessment of the unobtrusive signals during sleep, prediction of sleep–wake using ccECG and ccBioZ, and detection of high-risk OSA patients. First, signal quality indicators (SQIs) determined the data coverage of ccECG and ccBioZ. Then, a multimodal convolutional neural network (CNN) for sleep–wake prediction was tested on these preprocessed ccECG and ccBioZ data. Finally, two indices derived from this prediction detected patients at risk. The data included 187 PSG recordings of suspected OSA patients, 36 (dataset “Test”) of which were recorded simultaneously with PSG, ccECG, and ccBioZ. As a result, two improvements were made compared to prior studies. First, the ccBioZ signal coverage increased significantly due to adaptation of the acquisition system. Secondly, the utility of the sleep–wake classifier increased as it became a unimodal network only requiring respiratory input. This was achieved by using data augmentation during training. Sleep–wake prediction on “Test” using PSG respiration resulted in a Cohen’s kappa (κ) of 0.39 and using ccBioZ in κ = 0.23. The OSA risk model identified severe OSA patients with a κ of 0.61 for PSG respiration and κ of 0.39 using ccBioZ (accuracy of 80.6% and 69.4%, respectively). This study is one of the first to perform sleep–wake staging on capacitively-coupled respiratory signals in suspected OSA patients and to detect high risk OSA patients based on ccBioZ. The technology and the proposed framework could be applied in multi-night follow-up of OSA patients.

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A Novel CNN-Based Framework for Classification of Signal Quality and Sleep Position from a Capacitive ECG Measurement

Cited by 33 publications

References 18 publications

Unobtrusive, through-clothing ECG and Bioimpedance Monitoring in Sleep Apnea Patients

Unobtrusive, through-clothing ECG and Bioimpedance Monitoring in Sleep Apnea Patients

Deep Learning in Physiological Signal Data: A Survey

Capacitively-Coupled ECG and Respiration for Sleep–Wake Prediction and Risk Detection in Sleep Apnea Patients

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