Combining Photoplethysmography and Ballistocardiography to Address Voluntary Head Movements in Heart Rate Monitoring

Lomaliza, Jean-Pierre; Park, Hanhoon; Billinghurst, Mark

doi:10.1109/access.2020.3045387

Cited by 6 publications

(8 citation statements)

References 36 publications

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“…Recall (Re k ), accuracy (Acc k ) and specificity (Sp k ) were used to evaluate the results of sleep staging. Overall recall (Re ε ), overall accuracy (Acc ε ) and overall specificity (Sp ε ) were expressed by Equation ( 16) - (18).…”

Section: Performancementioning

confidence: 99%

“…There are two main types of current research on sleep monitoring and staging methods, one is to simplify and optimize the signal acquisition channel, the other is to explore change rules of other physiological signals during sleep [11][12][13][14][15][16][17][18][19][20]. Wearable measurements of physiological signals such as ECG, EOG and respiration, as well as body movements and other behaviors, offer the potential of low cost and easy operation for longterm dynamic sleep monitoring [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Rahman [15] used EOG signals to obtain up to 73% accuracy of sleep staging, but the classification effect of N1 stage still needs to be improved. In recent years, non-invasive or noncontact measurement of cardiac, respiratory and body movement signals has gradually gained the favor of researchers [16][17][18][19][20]. Ballistocardiograph (BCG) measurements based on pressure-sensitive sheets or bedmounted measurements have been used for sleep analysis and preliminary screening for diseases [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, non-invasive or noncontact measurement of cardiac, respiratory and body movement signals has gradually gained the favor of researchers [16][17][18][19][20]. Ballistocardiograph (BCG) measurements based on pressure-sensitive sheets or bedmounted measurements have been used for sleep analysis and preliminary screening for diseases [16][17][18]. Methods such as video monitoring or microwave radar were also used in the detection of cardiac, respiratory and body movement of patients [19][20].…”

Section: Introductionmentioning

confidence: 99%

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A Multi-scale Residual Convolutional Neural Network for Sleep Staging Based on Single Channel Electroencephalography Signal

Zhong¹,

Lei²,

Chen³

et al. 2021

Preprint

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Sleep disorder is a serious public health problem. Non hospital sleep monitoring system for monitoring sleep quality can effectively support the screening of sleep disorder related diseases. A new algorithm of multi-scale residual convolutional neural network (MS-RESCNN) was proposed to discover the feature of electroencephalography (EEG) signals detected with wearable system and staging the sleep stage. EEG signals were analyzed by this algorithm every 30 seconds, and then sleep staging results of wake-up (W), rapid eye movement sleep (REM) and non-rapid eye movement sleep (NREM) were outputed. NREM can also be subdivided into N1, N2 and N3 stages. 5-fold cross validation and independent subject cross validation were performed on the dataset with Kappa cofficients 0.7360 and 0.7001, respectively. The accuracy rates of those methods were 92.06% and 91.13%, respectively. Compared with the other methods, our proposed method can obtain the information of sleep stages from single channel EEG signals without special feature extraction. It has a good performance and can provide support for clinical application based on automatic sleep staging.

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Section: Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Multi-scale Residual Convolutional Neural Network for Sleep Staging Based on Single Channel Electroencephalography Signal

Zhong¹,

Lei²,

Chen³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In recent years, noninvasive [7,8] or noncontact [9,10] measurement of cardiac, respiratory, and body movement signals which offers the potential of low cost and easy operation for long term dynamic sleep monitoring has gradually gained the favour of researchers. But its performances badly depend on the quality of signal acquisition and complex signal processing.…”

Section: Introductionmentioning

confidence: 99%

A Sleep Stage Classification Algorithm of Wearable System Based on Multiscale Residual Convolutional Neural Network

et al. 2021

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Sleep disorder is a serious public health problem. Unobtrusive home sleep quality monitoring system can better open the way of sleep disorder-related diseases screening and health monitoring. In this work, a sleep stage classification algorithm based on multiscale residual convolutional neural network (MRCNN) was proposed to detect the characteristics of electroencephalogram (EEG) signals detected by wearable systems and classify sleep stages. EEG signals were analyzed in each epoch of every 30 seconds, and then 5-class sleep stage classification, wake (W), rapid eye movement sleep (REM), and nonrapid eye movement sleep (NREM) including N1, N2, and N3 stages was outputted. Good results (accuracy rate of 92.06% and 91.13%, Cohen’s kappa of 0.7360 and 0.7001) were achieved with 5-fold cross-validation and independent subject cross-validation, respectively, which performed on European Data Format (EDF) dataset containing 197 whole-night polysomnographic sleep recordings. Compared with several representative deep learning methods, this method can easily obtain sleep stage information from single-channel EEG signals without specialized feature extraction, which is closer to clinical application. Experiments based on CinC2018 dataset also proved that the method has a good performance on large dataset and can provide support for sleep disorder-related diseases screening and health surveillance based on automatic sleep staging.

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Heart rate monitoring of physical fitness training load based on wavelet transform

Zhang¹,

Sharma

Samori

2022

The Journal of Engineering

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The detection of fluctuations is a crucial step in the process of isolating heart rate oscillation signals. This is necessary to circumvent the issue wherein a variety of auditory signals will invariably interfere with the collection of centre velocity signals during physical activity, which has a significant impact on the detection efficiency of automated testing. To obtain heart rate fluctuation signals that are more precise, the paper proposes a method of R‐wave identification that is based on heartbeat conversion while the athlete is exercising. The proposed model relies heavily on the technique of wave analysis to filter out the background noise of the heartbeat when the athlete exercises and isolates the R‐wave signal of the heartbeat from the background noise. The proposed algorithm is evaluated in terms of noise interference that removes approximately 90% of noise, the heart rate data which is obtained after the noise is normally distributed, the average detection rate of the R‐wave algorithm is 98.65%. It has been demonstrated that the detection accuracy of the proposed algorithm is sufficient to meet the requirements of generating heart rate oscillatory signals.

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Combining Photoplethysmography and Ballistocardiography to Address Voluntary Head Movements in Heart Rate Monitoring

Cited by 6 publications

References 36 publications

A Multi-scale Residual Convolutional Neural Network for Sleep Staging Based on Single Channel Electroencephalography Signal

A Multi-scale Residual Convolutional Neural Network for Sleep Staging Based on Single Channel Electroencephalography Signal

A Sleep Stage Classification Algorithm of Wearable System Based on Multiscale Residual Convolutional Neural Network

Heart rate monitoring of physical fitness training load based on wavelet transform

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