Contactless Sensing-Aided Respiration Signal Acquisition Using Improved Empirical Wavelet Transform for Rhythm Detection.

Respiration is one of the most important vital signs indicating physical condition, while the signal detection is challenging due to the complex rhythm and effort in practical scenarios. In this paper, we propose a contactless sensing-aided respiration signal acquisition technique, which can adaptively extract the desired signal under time-varying respiration rhythms within a wide range. To be specific, respiration is perceived by piezoelectric ceramics sensors along with ballistocardiography and other interference in a contactless manner, and the proposed improved empirical wavelet transform (IEWT) performs spectrum division and recognition based on upper envelop and principal component criteria, respectively, to adaptively extract the respiration spectrum for signal reconstruction. For validations, we extracted respiration signals from 8 healthy individuals in lab breathing at specified rhythms from 0.2 Hz to 0.6 Hz as well as 38 in-patients suffering from sleep-disordered-breathing with reference of polysomnogram in practical clinic scenario. The results showed that the detected respiration rhythms perfectly fitted the ones in experimental lab dataset with a correlation coefficient of 0.98, which validated the effectiveness of the respiration spectrum extraction of the proposed IEWT method. Besides, in practical clinical dataset, the proposed IEWT method could yield mean absolute and relative errors of respiration intervals of 0.4 and 0.05 seconds, respectively, achieving significant improvement in comparison with conventional ones. Meanwhile, the performance of IEWT was robust to rhythm variation, individual difference and breathing cycle detection techniques, which demonstrated the feasibility and superiority of the proposed IEWT method for practical respiration monitoring.

Sleep Staging Using Noncontact-Measured Vital Signs.

As a physiological phenomenon, sleep takes up approximately 30% of human life and significantly affects people's quality of life. To assess the quality of night sleep, polysomnography (PSG) has been recognized as the gold standard for sleep staging. The drawbacks of such a clinical device, however, are obvious, since PSG limits the patient's mobility during the night, which is inconvenient for in-home monitoring. In this paper, a noncontact vital signs monitoring system using the piezoelectric sensors is deployed. Using the so-designed noncontact sensing system, heartbeat interval (HI), respiratory interval (RI), and body movements (BM) are separated and recorded, from which a new dimension of vital signs, referred to as the coordination of heartbeat interval and respiratory interval (CHR), is obtained. By extracting both the independent features of HI, RI, and BM and the coordinated features of CHR in different timescales, Wake-REM-NREM sleep staging is performed, and a postprocessing of staging fusion algorithm is proposed to refine the accuracy of classification. A total of 17 all-night recordings of noncontact measurement simultaneous with PSG from 10 healthy subjects were examined, and the leave-one-out cross-validation was adopted to assess the performance of Wake-REM-NREM sleep staging. Taking the gold standard of PSG as reference, numerical results show that the proposed sleep staging achieves an averaged accuracy and Cohen's Kappa index of 82.42% and 0.63, respectively, and performs robust to subjects suffering from sleep-disordered breathing.

Non-Contact Heartbeat Detection Based on Ballistocardiogram Using UNet and Bidirectional Long Short-Term Memory.

Benefiting from non-invasive sensing tech- nologies, heartbeat detection from ballistocardiogram (BCG) signals is of great significance for home-care applications, such as risk prediction of cardiovascular disease (CVD) and sleep staging, etc. In this paper, we propose an effective deep learning model for automatic heartbeat detection from BCG signals based on UNet and bidirectional long short-term memory (Bi-LSTM). The developed deep learning model provides an effective solution to the existing challenges in BCG-aided heartbeat detection, especially for BCG in low signal-to-noise ratio, in which the waveforms in BCG signals are irregular due to measured postures, rhythm and artifact motion. For validations, performance of the proposed detection is evaluated by BCG recordings from 43 subjects with different measured postures and heart rate ranges. The accuracy of the detected heartbeat intervals measured in different postures and signal qualities, in comparison with the R-R interval of ECG, is promising in terms of mean absolute error and mean relative error, respectively, which is superior to the state-of-the-art methods. Numerical results demonstrate that the proposed UNet-BiLSTM model performs robust to noise and perturbations (e.g. respiratory effort and artifact motion) in BCG signals, and provides a reliable solution to long term heart rate monitoring.

Machine learning-aided risk prediction for metabolic syndrome based on 3 years study.

Metabolic syndrome (MetS) is a group of physiological states of metabolic disorders, which may increase the risk of diabetes, cardiovascular and other diseases. Therefore, it is of great significance to predict the onset of MetS and the corresponding risk factors. In this study, we investigate the risk prediction for MetS using a data set of 67,730 samples with physical examination records of three consecutive years provided by the Department of Health Management, Nanfang Hospital, Southern Medical University, P.R. China. Specifically, the prediction for MetS takes the numerical features of examination records as well as the differential features by using the examination records over the past two consecutive years, namely, the differential numerical feature (DNF) and the differential state feature (DSF), and the risk factors of the above features w.r.t different ages and genders are statistically analyzed. From numerical results, it is shown that the proposed DSF in addition to the numerical feature of examination records, significantly contributes to the risk prediction of MetS. Additionally, the proposed scheme, by using the proposed features, yields a superior performance to the state-of-the-art MetS prediction model, which provides the potential of effective prescreening the occurrence of MetS.

Chromatic dispersion mitigation using a SEFDM-based diversity technique for IM/DD long reach optical links.

Spectral efficient frequency division multiplexing (SEFDM) can offer a higher spectral efficiency (SE) than orthogonal frequency division multiplexing (OFDM). In this work, we propose a diversity technique based on SEFDM for beyond 100-Gb/s optical intensity modulation and direct detection (IM/DD) long reach (LR) applications. We mathematically demonstrate that the self-created inter-carrier interference of SEFDM signals can be reused to achieve a diversity gain on each sub-carrier and, in turn, improve the tolerance to power fading induced by chromatic dispersion (CD) in IM/DD LR links. Based on the proposed diversity technique, we further demonstrated a 112-Gb/s SEFDM transmission over 80-km standard single-mode fiber, using only 28-GHz bandwidth and modulation format of up to 16-QAM. Experimental results show that SEFDM with the proposed diversity technique performs robust against CD effects and outperforms the conventional OFDM with adaptive bit and power loading of the same bandwidth and data rate, which validates the superiority of the proposed SEFDM in optical IM/DD LR transmissions.

150-Gb/s SEFDM IM/DD transmission using log-MAP Viterbi decoding for short reach optical links.

Spectral efficient frequency division multiplexing (SEFDM) can improve the spectral efficiency for next-generation optical and wireless communications. In this work, we apply SEFDM in beyond 100-Gb/s optical intensity modulation and direct detection transmissions and propose a low-complexity logarithmic-maximum-a-posteriori (log-MAP) Viterbi decoding algorithm to achieve the maximum likelihood (ML) detection. We evaluate the likelihood of detections using a posteriori probability instead of Euclidean distance by taking both noise and inter-carrier interference into consideration. In order to balance the performance and complexity, we then employ Viterbi decoding principle to retain only certain paths with ML detections (a.k.a., the surviving paths) while discarding the others during the decoding procedure. Results show that the proposed log-MAP Viterbi decoding scheme achieves optimal performance due to the precise likelihood evaluation, which guarantees the retention of the global ML detection. By using the proposed decoding scheme, the data rate of SEFDM signals can reach 150-Gb/s in a 2-km standard single mode fiber transmission, using only 28-GHz bandwidth and 16-QAM modulation. Experimental results show that the 16-QAM modulated SEFDM signal with a bandwidth compression factor of 0.8 outperforms 32-QAM modulated OFDM, while both signals have the same bandwidth (28-GHz) and data rate (140-Gb/s), which demonstrate the superiority of SEFDM in optical short reach applications.