Guanzheng Liu

A Wearable Respiratory Biofeedback System Based on Generalized Body Sensor Network

Wearable medical devices have enabled unobtrusive monitoring of vital signs and emerging biofeedback services in a pervasive manner. This article describes a wearable respiratory biofeedback system based on a generalized body sensor network (BSN) platform. The compact BSN platform was tailored for the strong requirements of overall system optimizations. A waist-worn biofeedback device was designed using the BSN. Extensive bench tests have shown that the generalized BSN worked as intended. In-situ experiments with 22 subjects indicated that the biofeedback device was discreet, easy to wear, and capable of offering wearable respiratory trainings. Pilot studies on wearable training patterns and resultant heart rate variability suggested that paced respirations at abdominal level and with identical inhaling/exhaling ratio were more appropriate for decreasing sympathetic arousal and increasing parasympathetic activities.

A Low-Cost Body Inertial-Sensing Network for Practical Gait Discrimination of Hemiplegia Patients

Gait analysis is widely used in detecting human walking disorders. Current gait analysis methods like video- or optical-based systems are expensive and cause invasion of human privacy. This article presents a self-developed low-cost body inertial-sensing network, which contains a base station, three wearable inertial measurement nodes, and the affiliated wireless communication protocol, for practical gait discrimination between hemiplegia patients and asymptomatic subjects. Every sensing node contains one three-axis accelerometer, one three-axis magnetometer, and one three-axis gyroscope. Seven hemiplegia patients (all were abnormal on the right side) and 7 asymptomatic subjects were examined. The three measurement nodes were attached on the thigh, the shank, and the dorsum of the foot, respectively (all on the right side of the body). A new method, which does not need to obtain accurate positions of the sensors, was used to calculate angles of knee flexion/extension and foot in the gait cycle. The angle amplitudes of initial contact, toe off, and knee flexion/extension were extracted. The results showed that there were significant differences between the two groups in the three angle amplitudes examined (-0.52±0.98° versus 6.94±2.63°, 28.33±11.66° versus 47.34±7.90°, and 26.85±8.6° versus 50.91±6.60°, respectively). It was concluded that the body inertial-sensing network platform provided a practical approach for wearable biomotion acquisition and was effective for discriminating gait symptoms between hemiplegia and asymptomatic subjects.

A New Approach to Detect Congestive Heart Failure Using Short-Term Heart Rate Variability Measures

Heart rate variability (HRV) analysis has quantified the functioning of the autonomic regulation of the heart and hearts ability to respond. However, majority of studies on HRV report several differences between patients with congestive heart failure (CHF) and healthy subjects, such as time-domain, frequency domain and nonlinear HRV measures. In the paper, we mainly presented a new approach to detect congestive heart failure (CHF) based on combination support vector machine (SVM) and three nonstandard heart rate variability (HRV) measures (e.g. SUM_TD, SUM_FD and SUM_IE). The CHF classification model was presented by using SVM classifier with the combination SUM_TD and SUM_FD. In the analysis performed, we found that the CHF classification algorithm could obtain the best performance with the CHF classification accuracy, sensitivity and specificity of 100%, 100%, 100%, respectively.

Estimation of Respiration Rate from Three-Dimensional Acceleration Data Based on Body Sensor Network

Respiratory monitoring is widely used in clinical and healthcare practice to detect abnormal cardiopulmonary function during ordinary and routine activities. There are several approaches to estimate respiratory rate, including accelerometer(s) worn on the torso that are capable of sensing the inclination changes due to breathing. In this article, we present an adaptive band-pass filtering method combined with principal component analysis to derive the respiratory rate from three-dimensional acceleration data, using a body sensor network platform previously developed by us. In situ experiments with 12 subjects indicated that our method was capable of offering dynamic respiration rate estimation during various body activities such as sitting, walking, running, and sleeping. The experimental studies also suggested that our frequency spectrum-based method was more robust, resilient to motion artifact, and therefore outperformed those algorithms primarily based on spatial acceleration information.

A Novel and Effective Method for Congestive Heart Failure Detection and Quantification Using Dynamic Heart Rate Variability Measurement.

Risk assessment of congestive heart failure (CHF) is essential for detection, especially helping patients make informed decisions about medications, devices, transplantation, and end-of-life care. The majority of studies have focused on disease detection between CHF patients and normal subjects using short-/long-term heart rate variability (HRV) measures but not much on quantification. We downloaded 116 nominal 24-hour RR interval records from the MIT/BIH database, including 72 normal people and 44 CHF patients. These records were analyzed under a 4-level risk assessment model: no risk (normal people, N), mild risk (patients with New York Heart Association (NYHA) class I-II, P1), moderate risk (patients with NYHA III, P2), and severe risk (patients with NYHA III-IV, P3). A novel multistage classification approach is proposed for risk assessment and rating CHF using the non-equilibrium decision-tree–based support vector machine classifier. We propose dynamic indices of HRV to capture the dynamics of 5-minute short term HRV measurements for quantifying autonomic activity changes of CHF. We extracted 54 classical measures and 126 dynamic indices and selected from these using backward elimination to detect and quantify CHF patients. Experimental results show that the multistage risk assessment model can realize CHF detection and quantification analysis with total accuracy of 96.61%. The multistage model provides a powerful predictor between predicted and actual ratings, and it could serve as a clinically meaningful outcome providing an early assessment and a prognostic marker for CHF patients.

A Principal Component Analysis Based Data Fusion Method for Estimation of Respiratory Volume

Impedance plethysmography (IP) is widely used in pulmonary volume measurement in recent years. Previous researches mainly focused on improving respiratory volume measurement accuracy by improving filter performance, electrode configuration, and so on, ignoring the influence of sleep posture changes. To solve this problem, we presented a principal component analysis (PCA)-based data fusion algorithm to minimize the effects of sleep posture changes on pulmonary volume measurement using a new dual-channel IP system. In situ experiments with ten subjects indicated that the PCA-based data fusion method improved the performance with the mean absolute error decreased ~25%. Thus, the novel method potentially achieves a higher sensitivity of the sleep respiratory function diagnosis.

A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal

Abstract Obstructive sleep apnea (OSA) is the most common sleep-related breathing disorder that potentially threatened peoples cardiovascular system. As an alternative to polysomnography for OSA detection, ECG-based methods have been developed for several years. However, previous work is focused on feature engineering, which is highly dependent on the prior knowledge of human experts and maybe subjective. Moreover, feature engineering also highlights the prominent shortcoming of current learning algorithms that the features are unable to extracted and organized from the data. In this study, we proposed a method to detect OSA based on deep neural network and Hidden Markov model (HMM) using single-lead ECG signal. The method utilized sparse auto-encoder to learn features, which belongs to unsupervised learning that only requires unlabeled ECG signals. Two types classifiers (SVM and ANN) are used to classify the features extracted from the sparse auto-encoder. Considering the temporal dependency, HMM was adopted to improve the classification accuracy. Finally, a decision fusion method is adopted to improve the classification performance. About 85% classification accuracy is achieved in the per-segment OSA detection, and the sensitivity is up to 88.9%. Based on the results of per-segment OSA detection, we perfectly separate the OSA recording from normal with accuracy of 100%. Experimental results demonstrated that our proposed method is reliable for OSA detection.

Use of Mutual Information and Transfer Entropy to Assess Interaction between Parasympathetic and Sympathetic Activities of Nervous System from HRV

Obstructive sleep apnea (OSA) is a common sleep disorder that often associates with reduced heart rate variability (HRV) indicating autonomic dysfunction. HRV is mainly composed of high frequency components attributed to parasympathetic activity and low frequency components attributed to sympathetic activity. Although, time domain and frequency domain features of HRV have been used to sleep studies, the complex interaction between nonlinear independent frequency components with OSA is less known. This study included 30 electrocardiogram recordings (20 OSA patient recording and 10 healthy subjects) with apnea or normal label in 1-min segment. All segments were divided into three groups: N-N group (normal segments of normal subjects), P-N group (normal segments of OSA subjects) and P-OSA group (apnea segments of OSA subjects). Frequency domain indices and interaction indices were extracted from segmented RR intervals. Frequency domain indices included nuLF, nuHF, and LF/HF ratio; interaction indices included mutual information (MI) and transfer entropy (TE (H→L) and TE (L→H)). Our results demonstrated that LF/HF ratio was significant higher in P-OSA group than N-N group and P-N group. MI was significantly larger in P-OSA group than P-N group. TE (H→L) and TE (L→H) showed a significant decrease in P-OSA group, compared to P-N group and N-N group. TE (H→L) were significantly negative correlation with LF/HF ratio in P-N group (r = −0.789, p = 0.000) and P-OSA group (r = −0.661, p = 0.002). Our results indicated that MI and TE is powerful tools to evaluate sympathovagal modulation in OSA. Moreover, sympathovagal modulation is more imbalance in OSA patients while suffering from apnea event compared to free event.

A Robust Electrode Configuration for Bioimpedance Measurement of Respiration

Electrode configuration is an important issue in the continuous measurement of respiration using impedance pneumography (IP). The robust configuration is usually confirmed by comparing the amplitude of the IP signals acquired with different electrode configurations, while the relative change in waveform and the effects of body posture and respiratory pattern are ignored. In this study, the IP signals and respiratory volume are simultaneously acquired from 8 healthy subjects in supine, left lying, right lying and prone postures, and the subjects are asked to perform four respiratory patterns including free breathing, thoracic breathing, abdominal breathing and apnea. The IP signals are acquired with four different chest electrode configurations, and the volume are measured using pneumotachograph (PNT). Differences in correlation and absolute deviation between the IP-derived and PNT-derived respiratory volume are assessed. The influences of noise, respiratory pattern and body posture on the IP signals of different configurations have significant difference (p < 0.05). The robust electrode configuration is found on the axillary midline, which is suitable for long term respiration monitoring.

Robustness evaluation of heart rate variability measures for age gender related autonomic changes in healthy volunteers

AbstractnTo analyze motion artifact’s affect on HRV measures, the age/gender related autonomic changes were investigated by using different HRV measures from wearable medical devices under ambulatory home-monitoring condition. Twelve healthy undergraduates and 20 healthy elderly subjects participated in the research. The electrocardiogram data was collected by using waist-worn device developed by us. Ten HRV measures were used to analyze the age-related automatic change including linear and nonlinear HRV indexes. Many linear HRV indexes were seriously contaminated by motion artefact, and did not reflect the age-related autonomic change. The approximate entropy (pxa0<xa00.001) was the best indicator among 10 HRV indexes. However, the approximate entropy was also contaminated by motion artefact and did not reflect the gender-related autonomic change. The study verified the hypothesis that the HRV measures could be contaminated under ambulatory monitoring condition. It is importance for ambulatory home-monitoring to study the robustness of HRV measures.