Myoungho Lee

Can photoplethysmography variability serve as an alternative approach to obtain heart rate variability information

Heart rate variability (HRV), extracted from an electrocardiogram, is known to be a noninvasive indicator reflecting the dynamic interplay between perturbations to cardiovascular function and the dynamic response of the cardiovascular regulatory system. Photoplethysmography (PPG) is a noninvasive method to monitor arterial oxygen saturation on a continuous basis. Given the rich cardiovascular information in the PPG signal, and the ubiquity and simplicity of pulse oximetry, we are investigating the feasibility of acquiring dynamics pertaining to the autonomic nervous system from PPG waveforms. To do this, we are quantifying PPG variability (PPGV). Detailed algorithmic approaches for extracting accurate PPGV signals are presented. We compare PPGV to HRV by computing time and frequency domain parameters often associated with HRV measurements, as well as approximate entropy calculations. Our results demonstrate that the parameters of PPGV are highly correlated with the parameters of HRV. Thus, our results indicate that PPGV could be used as an alternative measurement of HRV.

Autonomic differences between athletes and nonathletes: spectral analysis approach

The purpose of this study was to assess the adaptive effects of endurance training on autonomic function in athletes with spectral analysis of cardiovascular variability signals. Continuous ECG, arterial blood pressure (ABP), and respiratory signals were recorded from 15 athletes (VO2max > 55 mL.min-1.kg-1) and 15 nonathletes (VO2max < 45 mL.min-1.kg-1) during 10 min at sitting position. Autonomic function was assessed by low frequency power (LF power: 0.06-0.14 Hz) and high frequency power (HF power: the region of the respiratory frequency based on respiratory spectrum) obtained from the autospectra of RR interval, systolic arterial pressure (SAP), and diastolic arterial pressure (DAP) variability signals. The spontaneous baroreflex sensitivity was evaluated by the moduli, BRSLF and BRSHF, of the transfer function between RR interval and SAP variability in LF and HF bands. The resting HR in athletes was significantly lower than that in nonathletes. The HF power, an index of parasympathetic activity, in RR interval spectra were significantly higher in athletes than in nonathletes. Meanwhile, the LF power (an indicator of sympathetic activities contributing to RR interval and of ABP variabilities) showed no significant difference between both groups, although that of athletes was slightly less than that of nonathletes. Also, BRSLF and BRSHF were not significantly different between athletes and nonathletes. These results indicate that endurance training results in the enhanced vagal activities in athletes, which may contribute in part to the resting bradycardia.

Adaptive threshold method for the peak detection of photoplethysmographic waveform

Photoplethysmography (PPG)-based temporal analyses have been widely used as a useful analytical method in physiological and cardiovascular diagnosis. Most of temporal approaches of PPG are based on detected peak points, peak and foot of PPG. The aim of presented study is the development of improved peak detection algorithm of PPG waveform. The present study demonstrates a promising approach to overcome respiration effect and to detect PPG peak. More extensive investigation is necessary to adapt for the cardiovascular diseases, whose PPG morphology has different form.

A Real-Time ECG Data Compression and Transmission Algorithm for an e-Health Device

This paper introduces a real-time data compression and transmission algorithm between e-health terminals for a periodic ECGsignal. The proposed algorithm consists of five compression procedures and four reconstruction procedures. In order to evaluate the performance of the proposed algorithm, the algorithm was applied to all 48 recordings of MIT-BIH arrhythmia database, and the compress ratio (CR), percent root mean square difference (PRD), percent root mean square difference normalized (PRDN), rms, SNR, and quality score (QS) values were obtained. The result showed that the CR was 27.9:1 and the PRD was 2.93 on average for all 48 data instances with a 15% window size. In addition, the performance of the algorithm was compared to those of similar algorithms introduced recently by others. It was found that the proposed algorithm showed clearly superior performance in all 48 data instances at a compression ratio lower than 15:1, whereas it showed similar or slightly inferior PRD performance for a data compression ratio higher than 20:1. In light of the fact that the similarity with the original data becomes meaningless when the PRD is higher than 2, the proposed algorithm shows significantly better performance compared to the performance levels of other algorithms. Moreover, because the algorithm can compress and transmit data in real time, it can be served as an optimal biosignal data transmission method for limited bandwidth communication between e-health devices.

Robust algorithm for arrhythmia classification in ECG using extreme learning machine.

BackgroundRecently, extensive studies have been carried out on arrhythmia classification algorithms using artificial intelligence pattern recognition methods such as neural network. To improve practicality, many studies have focused on learning speed and the accuracy of neural networks. However, algorithms based on neural networks still have some problems concerning practical application, such as slow learning speeds and unstable performance caused by local minima.MethodsIn this paper we propose a novel arrhythmia classification algorithm which has a fast learning speed and high accuracy, and uses Morphology Filtering, Principal Component Analysis and Extreme Learning Machine (ELM). The proposed algorithm can classify six beat types: normal beat, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature beat, and paced beat.ResultsThe experimental results of the entire MIT-BIH arrhythmia database demonstrate that the performances of the proposed algorithm are 98.00% in terms of average sensitivity, 97.95% in terms of average specificity, and 98.72% in terms of average accuracy. These accuracy levels are higher than or comparable with those of existing methods. We make a comparative study of algorithm using an ELM, back propagation neural network (BPNN), radial basis function network (RBFN), or support vector machine (SVM). Concerning the aspect of learning time, the proposed algorithm using ELM is about 290, 70, and 3 times faster than an algorithm using a BPNN, RBFN, and SVM, respectively.ConclusionThe proposed algorithm shows effective accuracy performance with a short learning time. In addition we ascertained the robustness of the proposed algorithm by evaluating the entire MIT-BIH arrhythmia database.

Noncontact Respiration Rate Measurement System Using an Ultrasonic Proximity Sensor

This research presents the ultrasonic proximity sensor approach to respiration measurement. The ultrasonic proximity sensor measures respiration signatures and rates in real-time and for long-term monitoring, which is necessary for mobility from the end-user perspective. The study used a 240 kHz ultrasonic sensor to measure the time of flight of a sound wave between the transmitted signal and received signal during respiration in the abdominal wall-motion. Respiration rates measured with the ultrasonic proximity sensor were then compared with those measured with a thermocouple sensor on ten male subjects. Data from the measurement of respiration rates at 100 cm is provided. We have used this data from the method comparison study to confirm agreement with the reference signal to determine that the current version of respiratory rate detection system using ultrasonic can successfully measure respiration rates. The proposed respiratory measurement method could be used to monitor an unconscious person without the need to apply electrodes or other sensors in the correct position and to wire the subject to the system. Monitoring respiration using ultrasonic sensor offers a promising possibility of noncontact measurement of respiration rates. In particular, this technology offers a potentially inexpensive means to extend applications to consumer home-healthcare and mobile-healthcare products. Further advances in the sensor design, system design and signal processing can increase the range and quality of the measurement, broadening the potential application areas of this technology.

Localization of a mobile robot using images of a moving target

In this paper, the localization of a mobile robot using images of a moving target is introduced. Typical objects are stored in the database for the localization of the mobile robot. With a fixed camera, a perspective camera model and a given object database, an image frame can provide the pose (distance and orientation) of the object with respect to the camera. Utilizing the consecutive image frames and motion estimation technology, the relative pose of the object with respect to the camera can be obtained accurately; and during the process, calibration of camera with respect to the world frame, i.e. localization of a mobile robot, is gradually performed. This localization scheme is demonstrated by the experiments.

A simple real-time QRS detection algorithm

A simple algorithm using topological mapping has been developed for a real-time detection of the QRS complexes of ECG signals. As a measure of QRS complex energy, the authors used topological mapping from one dimensional sampled ECG signals to two dimensional vectors. To describe a change of curvature, the authors derive modified spatial velocity (MSV), from MSV the authors can locate QRS complexes more easily. The proposed algorithm consists of very small C-language procedures which reliably recognize the QRS complexes. For evaluation the authors used the MIT/BIH arrhythmia database. The proposed algorithm provides a good performance, a 99.58% detection rate of QRS complexes, a 99.57% sensitivity and 99.87% positive predictivity, respectively.

Cardiomyocytes from phorbol myristate acetate-activated mesenchymal stem cells restore electromechanical function in infarcted rat hearts

Despite the safety and feasibility of mesenchymal stem cell (MSC) therapy, an optimal cell type has not yet emerged in terms of electromechanical integration in infarcted myocardium. We found that poor to moderate survival benefits of MSC-implanted rats were caused by incomplete electromechanical integration induced by tissue heterogeneity between myocytes and engrafted MSCs in the infarcted myocardium. Here, we report the development of cardiogenic cells from rat MSCs activated by phorbol myristate acetate, a PKC activator, that exhibited high expressions of cardiac-specific markers and Ca2+ homeostasis-related proteins and showed adrenergic receptor signaling by norepinephrine. Histological analysis showed high connexin 43 coupling, few inflammatory cells, and low fibrotic markers in myocardium implanted with these phorbol myristate acetate-activated MSCs. Infarct hearts implanted with these cells exhibited restoration of conduction velocity through decreased tissue heterogeneity and improved myocardial contractility. These findings have major implications for the development of better cell types for electromechanical integration of cell-based treatment for infarcted myocardium.

Adaptive Motion Artifacts Reduction Using 3-axis Accelerometer in E-textile ECG Measurement System

The electro-conductive fabric (e-textile or e-fabric) as an electrode for ECG measurement is one of the best application for ubiquitous healthcare system. However, it is difficult to measure the bio-signal due to its sensitivity variation caused by impedance change, especially by motion of the subject. In this paper, adaptive motion artifacts reduction using motion information from 3-axis accelerometer is proposed and analyzed in quantitative manner.