Feasibility study of transfer function model on electrocardiogram change caused by acupuncture.

BACKGROUND: Acupuncture treatments that regulate the heart are used to treat various clinical disorders and conditions. Although many studies have been conducted to measure quantitatively the effects of acupuncture, thus far, models that describe these effects have not been established. The purpose of this study was to derive a transfer function model of acupuncture stimulation within the electrocardiograms based on the periods before, during, and after acupuncture. METHODS: Fourteen healthy subjects were included in this clinical trial. Five-minute electrocardiograms were captured before, during, and after acupuncture at HT7. For each period, signal-averaged electrocardiograms were created from all of the subjects' 5-min electrocardiograms for that period. Individual transfer functions, which has the highest average goodness of fit, were derived for each period pair. By averaging individual transfer functions, generalized transfer functions were derived. RESULTS: The transfer function with the highest average goodness of fit was a fraction with 4th order numerator and 5th order denominator. Fourteen individual transfer functions were derived separately for each pair of periods: before and during acupuncture, during and after acupuncture, and before and after acupuncture. Three generalized transfer functions were derived by averaging individual transfer functions for each period pair. CONCLUSION: The three generalized transfer functions that were derived may reflect the electrocardiogram changes caused by acupuncture. However, this clinical trial included only 14 subjects. Further studies with control groups and more subjects are needed. This clinical trial has been registered on the Clinical Research Information Service, Republic of Korea (No. KCT0001944). The first enrolment of subject started at 2 June 2015 and this trial was retrospectively registered at 14 June 2016.

The design of the m-health service application using a Nintendo DS game console.

In this article, we developed an m-health monitoring system using a Nintendo DS game console to demonstrate its utility. The proposed system consists of a biosignal acquisition device, wireless sensor network, base-station for signal reception from the sensor network and signal conversion according to Internet protocol, personal computer display program, and the Nintendo DS game console. The system collects three-channel electrocardiogram (ECG) signals for cardiac abnormality detection and three-axis accelerometer signals for fall detection of a person. The collected signals are then transmitted to the base-station through the wireless sensor network, where they are transformed according to the transmission control protocol/Internet protocol (TCP/IP) and sent to the destination IP through Internet network. To test the developed system, the collected signals were displayed on a computer located in different building through wired Internet network and also simultaneously displayed on the Nintendo DS game console connected to Internet network wirelessly. The system was able to collect and transmit signals for more than 24 h without any interruptions or malfunctions, showing the possibility of integrating healthcare monitoring functions into a small handheld-type electronic device developed for different purposes without significant complications. It is expected that the system can be used in an ambulance, nursing home, or general hospital where efficient patient monitoring from long distance is necessary.

Reconstruction of Precordial Lead Electrocardiogram From Limb Leads Using the State-Space Model.

A new electrocardiogram (ECG) reconstruction method based on a state-space model is presented. This method was applied to reconstruct precordial leads from limb leads (lead I, II, III) for its validity verification. The system matrices of the state-space model were estimated at the model estimation stage by considering the limb lead signals as the input of the system and precordial lead signals as the output. To evaluate the performance of the proposed method, all of the 549 records of the Physikalisch Technische Bundesanstalt diagnostic ECG database were used, and the correlation coefficients (CC) and root-mean-square errors between reconstructed ECG and measured ECG were calculated. For a more objective evaluation, the results were compared with those of linear regression model that has been typically used for ECG reconstruction. The mean and median values of CCs were higher than 0.988 and 0.995, respectively, for healthy subject data, and also higher than 0.981 and 0.993, respectively, for cardiac patient data and comparable to those by linear regression model. In addition, it was found that the reconstruction performance depended on the type of disease rather than lead type. Among cardiac patient data, hypertrophy, myocarditis, valvular heart disease, and stable heart angina showed higher CC (>0.990), while unstable angina and heart failure showed lower CC of 0.932 and 0.914, respectively. Moreover, when ECG contaminated with the noise was used for reconstruction, the proposed method demonstrated better performance than linear regression model in general.

Development of a 3D optical scanning-based automatic quality assurance system for proton range compensators.

PURPOSE: A new automatic quality assurance (AutoRCQA) system using a three-dimensional scanner (3DS) with system automation was developed to improve the accuracy and efficiency of the quality assurance (QA) procedure for proton range compensators (RCs). The system performance was evaluated for clinical implementation. METHODS: The AutoRCQA system consists of a three-dimensional measurement system (3DMS) based on 3DS and in-house developed verification software (3DVS). To verify the geometrical accuracy, the planned RC data (PRC), calculated with the treatment planning system (TPS), were reconstructed and coregistered with the measured RC data (MRC) based on the beam isocenter. The PRC and MRC inner surfaces were compared with composite analysis (CA) using 3DVS, using the CA pass rate for quantitative analysis. To evaluate the detection accuracy of the system, the authors designed a fake PRC by artificially adding small cubic islands with side lengths of 1.5, 2.5, and 3.5 mm on the inner surface of the PRC and performed CA with the depth difference and distance-to-agreement tolerances of [1 mm, 1 mm], [2 mm, 2 mm], and [3 mm, 3 mm]. In addition, the authors performed clinical tests using seven RCs [computerized milling machine (CMM)-RCs] manufactured by CMM, which were designed for treating various disease sites. The systematic offsets of the seven CMM-RCs were evaluated through the automatic registration function of AutoRCQA. For comparison with conventional technique, the authors measured the thickness at three points in each of the seven CMM-RCs using a manual depth measurement device and calculated thickness difference based on the TPS data (TPS-manual measurement). These results were compared with data obtained from 3DVS. The geometrical accuracy of each CMM-RC inner surface was investigated using the TPS data by performing CA with the same criteria. The authors also measured the net processing time, including the scan and analysis time. RESULTS: The AutoRCQA system accurately detected all fake objects in accordance with the given criteria. The median systematic offset of the seven CMM-RCs was 0.08 mm (interquartile range: -0.25 to 0.37 mm) and -0.08 mm (-0.58 to 0.01 mm) in the X- and Y-directions, respectively, while the median distance difference was 0.37 mm (0.23-0.94 mm). The median thickness difference of the TPS-manual measurement at points 1, 2, and 3 was -0.4 mm (-0.4 to -0.2 mm), -0.2 mm (-0.3 to 0.0 mm), and -0.3 mm (-0.6 to -0.1 mm), respectively, while the median difference of 3DMS was 0.0 mm (-0.1 to 0.2 mm), 0.0 mm (-0.1 to 0.3 mm), and 0.1 mm (-0.1 to 0.2 mm), respectively. Thus, 3DMS showed slightly better values compared to the manual measurements for points 1 and 3 in statistical analysis (p < 0.05). The average pass rate of the seven CMM-RCs was 97.97% ± 1.68% for 1-mm CA conditions, increasing to 99.98% ± 0.03% and 100% ± 0.00% for 2- and 3-mm CA conditions, respectively. The average net analysis time was 18.01 ± 1.65 min. CONCLUSIONS: The authors have developed an automated 3DS-based proton RC QA system and verified its performance. The AutoRCQA system may improve the accuracy and efficiency of QA for RCs.

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.

A design of the u-health monitoring system using a Nintendo DS game machine.

In this paper, we used the hand held type a Nintendo DS Game Machine for consisting of a u-Health Monitoring system. This system is consists of four parts. Biosignal acquire device is the first. The Second is a wireless sensor network device. The third is a wireless base-station for connecting internet network. Displaying units are the last part which were a personal computer and a Nintendo DS game machine. The bio-signal measurement device among the four parts the u-health monitoring system can acquire 7-channels data which have 3-channels ECG(Electrocardiogram), 3-axis accelerometer and tilting sensor data. Acquired data connect up the internet network throughout the wireless sensor network and a base-station. In the experiment, we concurrently display the bio-signals on to a monitor of personal computer and LCD of a Nintendo DS using wireless internet protocol and those monitoring devices placed off to the one side an office building. The result of the experiment, this proposed system effectively can transmit patient's biosignal data as a long time and a long distance. This suggestion of the u-health monitoring system need to operate in the ambulance, general hospitals and geriatric institutions as a u-health monitoring device.

An ECG signal processing algorithm based on removal of wave deflections in time domain.

This paper introduces a new approach to process biomedical signals by surgically removing wave deflections in time domain. The method first determines the epochs of high frequency deflections, cuts out them from the signal, and then connects the two disconnected points. To determine the epoch of a deflection to be removed, four slope trace waves are used to isolate the deflection based on signal characteristics of amplitude, slope, duration, and distance from neighboring deflections. The method has been applied to simulated data and MIT-BIH arrhythmia database to show its practical efficacy in the case of baseline wandering removal. It is found that the method has the capability to identify and remove high frequency deflections appropriately, leaving low frequency deflection such as baseline drifting.

A biomedical signal segmentation algorithm for event detection based on slope tracing.

In this paper a simple signal segmentation algorithm is introduced. The algorithm determines the epochs of signal components of interest based on signal characteristic such as amplitude, slope, deflection width, or distance between neighboring deflections. The epochs are segmented indirectly by means of a slope trace wave that traces a signal with its average slope and predetermined delay. The algorithm is applied to ECG and electrogram to show its practical applicability and efficiency. It is found that the algorithm can be used to choose particular signal components appropriately without significant signal preprocessing or complexity.