Automatic Detection of Respiration Rate From Ambulatory Single-Lead ECG

Ambulatory electrocardiography is increasingly being used in clinical practice to detect abnormal electrical behavior of the heart during ordinary daily activities. The utility of this monitoring can be improved by deriving respiration, which previously has been based on overnight apnea studies where patients are stationary, or the use of multilead ECG systems for stress testing. We compared six respiratory measures derived from a single-lead portable ECG monitor with simultaneously measured respiration air flow obtained from an ambulatory nasal cannula respiratory monitor. Ten controlled 1-h recordings were performed covering activities of daily living (lying, sitting, standing, walking, jogging, running, and stair climbing) and six overnight studies. The best method was an average of a 0.2–0.8 Hz bandpass filter and RR technique based on lengthening and shortening of the RR interval. Mean error rates with the reference gold standard were $pm$4 breaths per minute (bpm) (all activities), $pm$2 bpm (lying and sitting), and $pm$1 breath per minute (overnight studies). Statistically similar results were obtained using heart rate information alone (RR technique) compared to the best technique derived from the full ECG waveform that simplifies data collection procedures. The study shows that respiration can be derived under dynamic activities from a single-lead ECG without significant differences from traditional methods.      

Pattern recognition of sleep in rodents using piezoelectric signals generated by gross body movements

Current research on sleep using experimental animals is limited by the expense and time-consuming nature of traditional EEG/EMG recordings. We present here an alternative, noninvasive approach utilizing piezoelectric films configured as highly sensitive motion detectors. These film strips attached to the floor of the rodent cage produce an electrical output in direct proportion to the distortion of the material. During sleep, movement associated with breathing is the predominant gross body movement and, thus, output from the piezoelectric transducer provided an accurate respiratory trace during sleep. During wake, respiratory movements are masked by other motor activities. An automatic pattern recognition system was developed to identify periods of sleep and wake using the piezoelectric generated signal. Due to the complex and highly variable waveforms that result from subtle postural adjustments in the animals, traditional signal analysis techniques were not sufficient for accurate classification of sleep versus wake. Therefore, a novel pattern recognition algorithm was developed that successfully distinguished sleep from wake in approximately 95% of all epochs. This algorithm may have general utility for a variety of signals in biomedical and engineering applications. This automated system for monitoring sleep is noninvasive, inexpensive, and may be useful for large-scale sleep studies including genetic approaches towards understanding sleep and sleep disorders, and the rapid screening of the efficacy of sleep or wake promoting drugs.     

FBG-based smart bed system for healthcare applications

This paper presents a smart fiber Bragg grating (FBG) sensor system with an unobtrusive and easy-to-use FBG sensor bed, which automatically monitors the behavior of bedridden patients and their vital signs based on indicative spatio-temporal signature for adaptive intervention triggering and activity planning. We present the subtle design, fabrication, calibration, implementation and deployment issues of the FBG pressure sensors to be used in hospitals or nursing homes to prevent bedsore generation, patient falling out of the bed, and life-threatening situations such as patient's heart rate weakening, breathing pattern change, etc. Through trials conducted in the laboratory for respiratory rate monitoring with a sample group of 10 subjects, the system showed maximum error of ± 1 breaths per minute as compared to manual counting.     

基于PVDF的可穿戴生理信号监测系统

呼吸、心率、鼾声反映了人体在睡眠时的大量信息，该文以聚偏氟乙烯(PVDF)作为敏感单元进行呼吸、脉搏、鼾声信号的监测。根据所采用的传感器特性分别进行了电荷放大电路、陷波电路及电压放大电路的设计。硬件电路通过聚合物锂电池进行供电，根据聚合物电池的特性分别为电池设计了充电电路、保护电路及放电电路，硬件电路整体集成在一块印制电路板(PCB)上。同时设计了基于Android 设备的APP，以可视化形式实时显示生理信号数据，并对其进行长期储存，便于后期医生进行睡眠呼吸病症的分析诊断。该研究的目的是能准确监测睡眠生理参数，提高被测试者的使用舒适感。     

Cardiorespiratory-based sleep staging in subjects with obstructive sleep apnea

A cardiorespiratory-based automatic sleep staging system for subjects with sleep-disordered breathing is described. A simplified three-state system is used: Wakefulness (W), rapid eye movement (REM) sleep (R), and non-REM sleep (S). The system scores the sleep stages in standard 30-s epochs. A number of features associated with the epoch RR-intervals, an inductance plethysmography estimate of rib cage respiratory effort, and an electrocardiogram-derived respiration (EDR) signal were investigated. A subject-specific quadratic discriminant classifier was trained, randomly choosing 20% of the subject's epochs (in appropriate proportions of W, S and R) as the training data. The remaining 80% of epochs were presented to the classifier for testing. An estimated classification accuracy of 79% (Cohen's kappa value of 0.56) was achieved. When a similar subject-independent classifier was trained, using epochs from all other subjects as the training data, a drop in classification accuracy to 67% (kappa = 0.32) was observed. The subjects were further broken in groups of low apnoea-hypopnea index (AHI) and high AHI and the experiments repeated. The subject-specific classifier performed better on subjects with low AHI than high AHI; the performance of the subject-independent classifier is not correlated with AHI. For comparison an electroencephalograms (EEGs)-based classifier was trained utilizing several standard EEG features. The subject-specific classifier yielded an accuracy of 87% (kappa = 0.75), and an accuracy of 84% (kappa = 0.68) was obtained for the subject-independent classifier, indicating that EEG features are quite robust across subjects. We conclude that the cardiorespiratory signals provide moderate sleep-staging accuracy, however, features exhibit significant subject dependence which presents potential limits to the use of these signals in a general subject-independent sleep staging system.     

Strain-Isolating Materials and Interfacial Physics for Soft Wearable Bioelectronics and Wireless,Motion Artifact-Controlled Health Monitoring

Recent developments of micro-sensors and flexible electronics allow for the manufacturing of health monitoring devices, including electrocardiogram (ECG) detection systems for inpatient monitoring and ambulatory health diagnosis, by mounting the device on the chest. Although some commercial devices in reported articles show examples of a portable recording of ECG, they lose valuable data due to significant motion artifacts. Here, a new class of strain-isolating materials, hybrid interfacial physics, and soft material packaging for a strain-isolated, wearable soft bioelectronic system (SIS) is reported. The fundamental mechanism of sensor-embedded strain isolation is defined through a combination of analytical and computational studies and validated by dynamic experiments. Comprehensive research of hard-soft material integration and isolation mechanics provides critical design features to minimize motion artifacts that can occur during both mild and excessive daily activities. A wireless, fully integrated SIS that incorporates a breathable, perforated membrane can measure real-time, continuous physiological data, including high-quality ECG, heart rate, respiratory rate, and activities. In vivo demonstration with multiple subjects and simultaneous comparison with commercial devices captures the SIS's outstanding performance, offering real-world, continuous monitoring of the critical physiological signals with no data loss over eight consecutive hours in daily life, even with exaggerated body movements.     

Precision Digital Instrument for Calculation of Heart Rate and R-R Interval

An economical precision digital heart rate meter has been constructed for use in general laboratory EKG studies and especially as a diagnostic aid in pacemaker clinics. The instrument features a digital method for rate computation with no calibration required, a system test feature and ability to measure and digitally display both heart rate in beats per minute (bpm) and R-R interval in miliseconds.     

Sleep versus wake classification from heart rate variability using computational intelligence: consideration of rejection in classification models

Reliability of classification performance is important for many biomedical applications. A classification model which considers reliability in the development of the model such that unreliable segments are rejected would be useful, particularly, in large biomedical data sets. This approach is demonstrated in the development of a technique to reliably determine sleep and wake using only the electrocardiogram (ECG) of infants. Typically, sleep state scoring is a time consuming task in which sleep states are manually derived from many physiological signals. The method was tested with simultaneous 8-h ECG and polysomnogram (PSG) determined sleep scores from 190 infants enrolled in the collaborative home infant monitoring evaluation (CHIME) study. Learning vector quantization (LVQ) neural network, multilayer perceptron (MLP) neural network, and support vector machines (SVMs) are tested as the classifiers. After systematic rejection of difficult to classify segments, the models can achieve 85%-87% correct classification while rejecting only 30% of the data. This corresponds to a Kappa statistic of 0.65-0.68. With rejection, accuracy improves by about 8% over a model without rejection. Additionally, the impact of the PSG scored indeterminate state epochs is analyzed. The advantages of a reliable sleep/wake classifier based only on ECG include high accuracy, simplicity of use, and low intrusiveness. Reliability of the classification can be built directly in the model, such that unreliable segments are rejected.     

Comparison of impedance and inductance ventilation sensors onadults during breathing, motion, and simulated airway obstruction

The goal of this study was to compare the relative performance of two noninvasive ventilation sensing technologies on adults during artifacts. The authors recorded changes in transthoracic impedance and cross-sectional area of the abdomen (abd) and ribcage (rc) using impedance pneumography (IP) and respiratory inductance plethysmography (RIP) on ten adult subjects during natural breathing, motion artifact, simulated airway obstruction, yawning, snoring, apnea, and coughing. The authors used a pneumotachometer to measure air flow and tidal volume as the standard. They calibrated all sensors during natural breathing, and performed measurements during all maneuvers without changing the calibration parameters. No sensor provided the most-accurate measure of tidal volume for all maneuvers. Overall, the combination of inductance sensors [RIP(sum)] calibrated during an isovolume maneuver had a bias (weighted mean difference) as low or lower than all individual sensors and all combinations of sensors. The IP(rc) sensor had a bias as low or lower than any individual sensor. The cross-correlation coefficient between sensors was high during natural breathing, but decreased during artifacts. The cross correlation between sensor pairs was lower during artifacts without breathing than it was during maneuvers with breathing for four different sensor combinations. The authors tested a simple breath-detection algorithm on all sensors and found that RIP(sum) resulted in the fewest number of false breath detections, with sensitivity of 90.8% and positive predictivity of 93.6%     

基于压电薄膜的非接触式人体生理信号监测椅

对人体的心率和呼吸信号进行日常监测有助于人体健康生活管理,该文设计了一种非接触式生理信号监测系统。通过装有聚偏氟乙烯(PVDF)压电薄膜传感器的新型椅子结构感知人体振动产生的微小压力作用,并将振动信号传送给上位机软件;从振动信号中提取心冲击信号与呼吸信号,并通过LabVIEW显示波形变化,可以完成心率和呼吸的实时监测。结果表明,装置监测的心率值与血氧脉搏仪相比,其平均误差为2.013％,平均呼吸率误差为4.88％,具有较好的实用性。     

Automated recognition of obstructive sleep apnea syndrome using support vector machine classifier

Obstructive sleep apnea (OSA) is a common sleep disorder that causes pauses of breathing due to repetitive obstruction of the upper airways of the respiratory system. The effect of this phenomenon can be observed in other physiological signals like the heart rate variability, oxygen saturation, and the respiratory effort signals. In this study, features from these signals were extracted from 50 control and 50 OSA patients from the Sleep Heart Health Study database and implemented for minute and subject classifications. A support vector machine (SVM) classifier was used with linear and second-order polynomial kernels. For the minute classification, the respiratory features had the highest sensitivity while the oxygen saturation gave the highest specificity. The polynomial kernel always had better performance and the highest accuracy of 82.4% (Sen: 69.9%, Spec: 91.4%) was achieved using the combined-feature classifier. For subject classification, the polynomial kernel had a clear improvement in the oxygen saturation accuracy as the highest accuracy of 95% was achieved by both the oxygen saturation (Sen: 100%, Spec: 90.2%) and the combined-feature (Sen: 91.8%, Spec: 98.0%). Further analysis of the SVM with other kernel types might be useful for optimizing the classifier with the appropriate features for an OSA automated detection algorithm.     

Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea

A method for the automatic processing of the electrocardiogram (ECG) for the detection of obstructive apnoea is presented. The method screens nighttime single-lead ECG recordings for the presence of major sleep apnoea and provides a minute-by-minute analysis of disordered breathing. A large independently validated database of 70 ECG recordings acquired from normal subjects and subjects with obstructive and mixed sleep apnoea, each of approximately eight hours in duration, was used throughout the study. Thirty-five of these recordings were used for training and 35 retained for independent testing. A wide variety of features based on heartbeat intervals and an ECG-derived respiratory signal were considered. Classifiers based on linear and quadratic discriminants were compared. Feature selection and regularization of classifier parameters were used to optimize classifier performance. Results show that the normal recordings could be separated from the apnoea recordings with a 100% success rate and a minute-by-minute classification accuracy of over 90% is achievable.     

The PPG Physiological Signal for Heart Rate Variability Analysis

The photoplethysmography (PPG) sensor can be applied to measure the situation and function of human blood circulation. The PPG sensor is not only existed the characteristics of simple, convenient and low price but also easy non-invasive to measure physiological signal. The advantage of PPG signal is easy to measure from various sensing location. The physiological information of the clinical detection method is broadly implemented for such type. In this paper, we utilize “the green LED reflective” PPG sensor to capture physiological signals operated in static and exercise modes. Therefore, we adopted the short-term measurement in 5 min. Those captured signals are divided into five segments and 1 min for each segment. We calculated heart beats per minute and heart rate variability (HRV) operated in time domain analysis criteria. The related theory of short-time Fourier transform (STFT) combined with power spectral density (PSD) is implemented for finding HRV in frequency domain analysis. Then, we derived random process theory and the autocorrelation function which are verified the PPG measurement is stationary process or not. In the future experiment, we can compare the 24 h data with the previous results. Consequently, we apply the physical health status monitoring of long-term and short-term modes to observe subject varies of HRV and ANS after listening music concurrently.     

Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea

Sleep has been regarded as a testing situation for the autonomic nervous system, because its activity is modulated by sleep stages. Sleep-related breathing disorders also influence the autonomic nervous system and can cause heart rate changes known as cyclical variation. We investigated the effect of sleep stages and sleep apnea on autonomic activity by analyzing heart rate variability (HRV). Since spectral analysis is suited for the identification of cyclical variations and detrended fluctuation analysis can analyze the scaling behavior and detect long-range correlations, we compared the results of both complementary techniques in 14 healthy subjects, 33 patients with moderate, and 31 patients with severe sleep apnea. The spectral parameters VLF, LF, HF, and LF/HF confirmed increasing parasympathetic activity from wakefulness and REM over light sleep to deep sleep, which is reduced in patients with sleep apnea. Discriminance analysis was used on a person and sleep stage basis to determine the best method for the separation of sleep stages and sleep apnea severity. Using spectral parameters 69.7% of the apnea severity assignments and 54.6% of the sleep stage assignments were correct, while using scaling analysis these numbers increased to 74.4% and 85.0%, respectively. We conclude that changes in HRV are better quantified by scaling analysis than by spectral analysis.     

A Practical Method for Automatic Real-Time EEG Sleep State Analysis

A computer system for real-time analysis of the electroencephalograph (EEG) is described. The system performs continuous analysis, with graphic, analog, and tabular outputs, and storage of selected samples on disk for off-line analysis. Implemented mostly in high-level software, it is based on a two-component model of the signal in which waves are detected by a combination zero-crossing and peak detection algorithm. Each sample is classified by a pattern recognition scheme into one of several classes on the basis of the frequency distribution of waves; the classes correspond to normal sleep-awake states. Samples are taken ten times per minute and tabulated once per minute to provide a concise quantified history which is well suited to long-term EEG studies. Alternate independent information channels allow verification of results. Samples stored on disk may be grouped and averaged for statistical comparisons of EEG signal characteristics. The state classification algorithm has been tailored to the EEG of the cat; the results of a series of 7-8 day sleep studies are presented.     

A Fetal Phonocardiotachometer for Use in Labor

Certain obstetrical problems in labor affecting the unborn baby may result in death of the baby before and after birth or may be responsible for brain damage disorders. Because these problems occur frequently, it is necessary to evaluate fetal well-being as closely as possible in labor. The use of the fetal heart rate as an indication of the fetal condition in labor is discussed and the necessity for constant monitoring of the fetal heart rate is emphasized. The special problems associated with monitoring in labor are noted. A fetal phonocardiotachometer is described which operates with an acoustic signal to noise ratio of +3 db after filtering to monitor heart rate over a range of approximately 70 to 210 beats per minute. It will follow a maximum rate of change of rate within this range of six beats per minute per second. So far over 300 cases were followed with the instrument.     

便携家庭式远程定位ECG监测预警系统的设计与实现

针对远程定位ECG监测预警系统的需求,设计了一套家庭式心电实时监测预警系统。系统包括远程监测平台与移动预警端,远程监测平台采用Labview编写了ECG波形显示与回放程序,并利用自主开发的心电采集设备实现了心电分析与监测预警等功能。移动预警端将ECG采集模块与Android手机相结合,实现了ECG信号的实时监控与远程定位等功能。实测结果表明,系统准确率在94%以上,携带方便,在出现心率紊乱等症状时,可及时发送报警信息,帮助患者得到医疗救助。     

基于压电薄膜传感器的睡眠监测枕研究

针对人体睡眠的健康监测问题,分析睡眠时人体头部对枕头压力状态变化,研究出一种基于聚偏氟乙烯(PVDF)压电薄膜传感器的睡眠监测枕。睡眠时采集人体脉搏、呼吸及鼾声生理信号,硬件电路调理、MSP430F149单片机有机处理后经蓝牙上传至LabVIEW程序,结合打鼾无规则振动特征,依据信号幅值、均值同预设值对比的双阈值分析方法,实现睡眠时呼吸暂停识别报警,鼾声自动监测记录及睡眠呼吸暂停综合症前期检查监测等功能。     

采矿工人生理状况监测系统设计

采矿工人生理状况监测系统是工人矿井下作业时,进行实时、连续、长时间地采集、监测心电、呼吸、体温、血氧饱和度和体动等参数,并实现数据无线传输的系统。针对传统监控设备对工人状态掌控缺乏、矿难频发等重大问题,设计了一款无线、可穿戴、无创、低心理负荷的多参数采矿工人生理状况监测系统,以便准确地了解井下工人生理状况,及时预防危险状况发生,安全顺利地完成采矿工作。     

Breathing detection: towards a miniaturized, wearable, battery-operated monitoring system

This paper analyzes the main challenges associated with noninvasive, continuous, wearable, and long-term breathing monitoring. The characteristics of an acoustic breathing signal from a miniature sensor are studied in the presence of sources of noise and interference artifacts that affect the signal. Based on these results, an algorithm has been devised to detect breathing. It is possible to implement the algorithm on a single integrated circuit, making it suitable for a miniature sensor device. The algorithm is tested in the presence of noise sources on five subjects and shows an average success rate of 91.3% (combined true positives and true negatives).