Abstract The authors informed us of their intention to withdraw this article after acceptance but prior to completion of the version of record. The authors have not responded to our communications since then. We hereby withdraw this article from publication. No issues are known regarding the scientific content of the article, and there are no concerns regarding publication ethics. Physiological monitoring systems have been widely used for the collection of key physio-logical parameters such as heart and respiration rates. However, conventional monitoring systems rely on electrodes or bandages which are not well accepted by subjects due to the requirements of direct contacts with their skins. In this paper, a new physiological monitoring system without the direct contacts with hu-man skins was proposed, where physiological movements of subjects were translated into micro bends of optical fibers. More specifically, the movements due to blood pumping were used as the inputs of the system and variations of light intensities of optical fibers were used as the outputs, which were further processed to obtain heart and respiration rates. In data analysis, adaptive regulations and statistical classifications were used to address potential concerns of individual differences and body interferences. The physiological monitoring systems developed in this study were used to quantify heart and reparation rates for healthy volunteers. Experimental results included 1) the heart rates of 40-150bpm and respiration rates of 10-20bpm for individual differences; 2)the heart rates of the mean error 1.60±0.98 beats per minute (bpm), 1.94±0.83bpm, 1.24±0.59bpm, 1.06±0.62 bpm contract to polar beat device in same individuals at four different posture contacts and mean error 1.09±0.96bpm, 1.44±0.99bpm, 1.78±0.94bpm at three different breathing states. Furthermore, the results based on this system were validated by conventional counterparts relying on skin-contacting electrodes where comparable results of 0.26±2.80 bpm in 95% confidence intervals (± 1.96SD) vs. Philips sure-signs of the VM6 medical monitor for heart rates and 0.41 ± 1.49 bpm in 95% confidence intervals (± 1.96SD) vs. ECG-derived Respiratory(EDR) for respiration rates were reported. It is indicated that the developed system has nice performances and can be senselessly used under complex environments.
Sleep is one of the most fundamental physiological activities of human beings. Automatic sleep staging can efficiently assist human experts to diagnose the sleep health of people. However, most of the existing methods only considered one or two kinds of time-domain, frequency-domain, and spatial-domain information from EEG signals. Therefore, how to make full use of the complementarity of different features of EEG signals is still a challenge. To tackle this challenge, in this paper we design BrainSleepNet to capture the comprehensive features of multivariant EEG signals for automatic sleep staging. BrainSleepNet consists of an EEG temporal feature extraction module and an EEG spectral-spatial feature extraction module for the temporal-spectral-spatial representation of EEG signals. To the best of our knowledge, it is the first attempt to integrate EEG temporal-spectral-spatial features simultaneously in a unified model for sleep staging. Experiments on the benchmark dataset MASSSS3 demonstrate that BrainSleepNet outperforms all baseline models. The implementation code of BrainSleepNet is available at https://github.com/ziyujia/sleep.
Sleep stage classification is of great importance in sleep analysis, which provides information for the diagnosis and monitoring of sleep-related conditions. To accurately analyze sleep structure under comfortable conditions, many studies have applied deep learning to sleep staging based on single-lead electrocardiograms (ECGs). However, there is still great room for improvement in inter-subject classification. In this paper, we propose an end-to-end, multi-scale, subject-adaptive network that improves the performance of the model according to the model architecture, training method, and loss calculation. In our investigation, a multi-scale residual feature encoder extracted various details to support the feature extraction of single-lead ECGs in different situations. After taking the domain shift caused by individual differences and acquisition conditions into consideration, we introduced a domain-aligning layer to confuse the domain. Moreover, to enhance the performance of the model, the multi-class focal loss was used to reduce the negative impact of class imbalance on the learning of the model, and the loss of sequence prediction was added to the classification task to assist the model in judging sleep stages. The model was evaluated on the public test datasets SHHS2, SHHS1, and MESA, and we obtained mean accuracies (Kappa) of 0.849 (0.837), 0.827 (0.790), and 0.868 (0.840) for awake/light sleep/deep sleep/REM stage classification, which confirms that this is an improved solution compared to the baseline. The model also performed outstandingly in cross-dataset testing. Hence, this article makes valuable contributions toward improving the reliability of sleep staging.
The continuous monitoring of blood pressure (BP) has been found to significantly predict the risk of severe cardiovascular disease. Pulse arrival time (PAT), generally extracted from synchronized photoplethysmogram (PPG) and electrocardiogram (ECG) signals, is widely adopted in noninvasive blood pressure studies. However, motion artifact and physical activities introduce different levels of noise to the ECG and PPG signals in wearable devices, resulting in large fluctuations in PAT-based BP estimation, which may confuse and mislead users. We explored the potential of Kalman filter to enhance the stability of continuous BP estimation. We developed an Android application collecting data from the wearable device via Bluetooth technology, two Kalman filters were designed and implemented to evaluate the systolic blood pressure (SBP) and pulse pressure (PP) separately, whose gains were adjusted automatically by signal quality indicators. Validation experiments were performed on 6 volunteers to ensure the effectiveness of Kalman filters, and the preliminary results compared with a standard commercial sphygmomanometer showed that our approach can achieve higher stability than the method without Kalman filtering.
Objective: This paper aims to present how physiological signals can be processed based on wavelet decomposition to calculate multiple physiological parameters in real-time on an embedded platform. Approach: An ECG and PPG are decomposed to the appropriate scale based on a quadratic spline wavelet base in order to obtain high and narrow pulse peaks at the location of the mutation points. Based on the decomposed waveforms, feature points are positioned to calculate physiological parameters in real-time, including heart rate, pulse rate, blood oxygen, and blood pressure. The proposed algorithm has been implemented on a Texas Instruments' CC2640R2F. Main results: The misdetection rate of feature point location based on the square wavelet decomposition waveform is only 0.57% in the acquired ECG and 0.23% in the acquired PPG. Heart rate and pulse rate are both highly correlated with the reference, both having correlation coefficients of 0.99. The pulse rate and heart rate are 3.85% (51/1326) and 2.94% (39/1326) outside the 95% consistency limit, respectively. The systolic and diastolic blood pressures are significantly associated with standard equipment measurements, with correlation coefficients of 0.87 and 0.83. The systolic and diastolic blood pressures were 5.88% (21/357) and 5.32% (19/357) outside the 95% consistency limit, respectively. Significance: The real-time calculation of multiple physiological parameters based on wavelet decomposition on an embedded platform presented here shows outstanding accuracy.
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