Yeon-Sik Noh

Improvement of Dynamic Respiration Monitoring Through Sensor Fusion of Accelerometer and Gyro-sensor

In this paper, we suggest a method to improve the fusion of an accelerometer and gyro sensor by using a Kalman filter to produce a more high-quality respiration signal to supplement the weakness of using a single accelerometer. To evaluate our proposed algorithm’s performance, we developed a chest belt-type module. We performed experiments consisting of aerobic exercise and muscular exercises with 10 subjects. We compared the derived respiration signal from the accelerometer with that from our algorithm using the standard respiration signal from the piezoelectric sensor in the time and frequency domains during the aerobic and muscular exercises. We also analyzed the time delay to verify the synchronization between the output and standard signals. We confirmed that our algorithm improved the respiratory rate’s detection accuracy by 4.6% and 9.54% for the treadmill and leg press, respectively, which are dynamic. We also confirmed a small time delay of about 0.638 s on average. We determined that real-time monitoring of the respiration signal is possible. In conclusion, our suggested algorithm can acquire a more high-quality respiration signal in a dynamic exercise environment away from a limited static environment to provide safer and more effective exercises and improve exercise sustainability.

Comparison of CWT with DWT for detecting Qrs Complex on Wearable ECG Recorder

Wearable ECG Recorder can detect not only Biosignal but also Motion artifact and other surrounding noises. This study used wavelet transform as a way of removing such noise and compared Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT). Each transform is designed to optimize the QRS Complex. CWT was designed to detect the Maximum energy scale from QRS Complex. DWT was designed to decompose 8-Levels and to reconstruct detailed coefficient with the frequency of the QRS Complex. To test the performance of two methods, data were collected from MIT-BIH Arrhythmia Database and Wearable ECG Recorder(WER) at the speed of 3km/h, 6km/h, 9km/h, 12km/h. By analyzing the data from two methods, the effectiveness for detecting QRS Complex while eliminating the surrounding noises.

A Study of Significant data Classification between EDR extracted and frequency analysis of Heart Rate Variability from ECG using Conductive textile

The aim of this study is classification of correlative data through comparison between ECG-Derived Respiration (EDR) and High Frequency (HF) component obtained frequency analysis of heart rate variability (HRV) from ECG using two of conductive textiles. Generally, individual frequency range of the HF component has been determined only by means of distinctive parameters of respiration such as respiratory rate, range and median value of respiratory rate, etc. However, in many cases Low Frequency (LF) components and High Frequency (HF) components may be superimposed on each other totally or particularly and can not be diagnostically estimated because respiratory rhythms are individually much remarkably differentiated. This study, in consequence, analyze frequency component of EDR derived from ECG and then compare with high frequency components of HRV and finally find out valuable data for further analysis. Hardware constitution is used to bed type ECG measurement equipment based on U-Healthcare, can identify user using Radio Frequency (RF) module as well as manage data each of user. We used Frequency Modulation (FM) method based on Respiration Sinus Arrhythmia (RSA) in order to extract EDR from ECG signal. ECG data and EDR data were processed with Lab-VIEW, then classified through each of frequency analysis.

Screen-Printed PEDOT:PSS Electrodes on Commercial Finished Textiles for Electrocardiography

Electrocardiography (ECG) is an essential technique for analyzing and monitoring cardiovascular physiological conditions such as arrhythmia. This article demonstrates the integration of screen-printed ECG circuitry from a commercially available conducting polymer, PEDOT:PSS, onto commercially available finished textiles. ECG signals were recorded in dry skin conditions due to the ability of PEDOT:PSS to function as both ionic and electronic conductors. The signal amplifies when the skin transpires water vapor or by applying a common lotion on the skin. Finally, PEDOT:PSS wires connected to PEDOT:PSS electrodes have been shown to record ECG signals comparable to Ag/AgCl connected to copper wires.

Electrocardiogram signal processing method for exact Heart Rate detection in physical activity monitoring system: Wavelet approach

Physical Activity Monitoring is a device that can measure the human activity quantity quantitatively through Heart Rate detection in real time. R-Spike detection of ECG is required for this Heart Rate detection. Since Physical Activity Monitoring System is usually used during activity or exercise, however, signal measured in ECG System is contaminated by diverse noises. Diverse noises become the factors of failure in R-Spike detection. Such factors impede the exact HR detection. This paper suggests method to convolute wavelet function and scaling function as the optimum signal disposition method for optimum R-Spike detection. This method was compared with the R-Spike detection method that uses quadratic spline wavelet presented before. To verify performance of signal disposition method suggested in this paper, the ECG of noise stress test database (NSTDB) and MIT-Database were tested in combination. Then, the sensitivity of R-Spike detection rate for noise was also additionally tested by gradually lowering SNR of NSTDB. Then, it was verified through ECG signal that was actually measured in physical activity monitoring.

A Novel Approach to Classify Significant ECG Data Based on Heart Instantaneous Frequency and ECG-derived Respiration using Conductive Textiles

Our study focuses on classifying as significant electrocardiogram (ECG) data from home healthcare system. Generally, spectral analysis of RR interval (RRI) time series is used to determine periodic component of heart rate variability (HRV). It is well known, moreover, that low frequency (LF) component is associated with heart rate variability regulation, and high frequency (HF) component is referred to respiration as respiration sinus arrhythmia (RSA) in the HRV power spectra. In many cases, however, LF and HF components may be entirely superimposed on each other and, therefore, the method by division of power spectra range can not be evaluated diagnostically. We propose another approach to interpret well better than before. The method which we suggest is that it finds high correlative data using frequency analysis comparison heart instantaneous frequency (HIF) based on extracting the instantaneous fundamental frequency with EDR. The reason which we use HIF is that it is simpler and more powerful against noise than HRV. First of all, we show the EDR extraction process, and then prove that HIF signal is useful or not through comparison with HRV. Finally, we classify significant signal data through comparison high frequency (HF) component obtained frequency analysis of HIF with that of EDR.

Design of Real-Time Autonomic Nervous System Evaluation System Using Heart Instantaneous Frequency

In this study, we attempt to design a real-time autonomic nervous system (ANS) evaluation system usable during exercise using heart instantaneous frequency (HIF). Although heart rate variability (HRV) is considered to be a representative signal widely used ANS evaluation system, the R-peak detection process must be included to obtain an HRV signal, which involves a high sampling frequency and interpolation process. In particular, it cannot accurately evaluate the ANS using HRV signals during exercise because it is difficult to detect the R-peak of electrocardiogram (ECG) signals with exposure to many noises during exercise. Therefore, in this study, we develop the ground for a system that can analyze an ANS in real-time by using the HIF signal circumventing the problem of the HRV signal during exercise. First, we compare the HRV and HIF signals in order to prove that the HIF signal is more efficient for ANS analysis than HRV signals during exercise. Further, we performed real-time ANS analysis using H1F and confirmed that the exercisers ANS variation experiences massive surges at points of acceleration and deceleration of the treadmill (similar to HRV).

Analysis of Consistency of Transthoracic Bioimpedance Measurements Acquired with Dry Carbon Black PDMS Electrodes, Adhesive Electrodes, and Wet Textile Electrodes

The detection of intrathoracic volume retention could be crucial to the early detection of decompensated heart failure (HF). Transthoracic Bioimpedance (TBI) measurement is an indirect, promising approach to assessing intrathoracic fluid volume. Gel-based adhesive electrodes can produce skin irritation, as the patient needs to place them daily in the same spots. Textile electrodes can reduce skin irritation; however, they inconveniently require wetting before each use and provide poor adherence to the skin. Previously, we developed waterproof reusable dry carbon black polydimethylsiloxane (CB/PDMS) electrodes that exhibited a good response to motion artifacts. We examined whether these CB/PDMS electrodes were suitable sensing components to be embedded into a monitoring vest for measuring TBI and the electrocardiogram (ECG). We recruited N = 20 subjects to collect TBI and ECG data. The TBI parameters were different between the various types of electrodes. Inter-subject variability for copper-mesh CB/PDMS electrodes and Ag/AgCl electrodes was lower compared to textile electrodes, and the intra-subject variability was similar between the copper-mesh CB/PDMS and Ag/AgCl. We concluded that the copper mesh CB/PDMS (CM/CB/PDMS) electrodes are a suitable alternative for textile electrodes for TBI measurements, but with the benefit of better skin adherence and without the requirement of wetting the electrodes, which can often be forgotten by the stressed HF subjects.

Effect of Shallow and Deep SCUBA Dives on Heart Rate Variability

Prolonged and high pressure diving may lead to various physiological changes including significant alterations of autonomic nervous system (ANS) activity that may be associated with altered physical performance, decompression sickness, or central nervous system oxygen toxicity. Ideally, researchers could elucidate ANS function before, during, and after dives that are most associated with altered function and adverse outcomes. However, we have a limited understanding of the activities of the ANS especially during deeper prolonged SCUBA diving because there has never been a convenient way to collect physiological data during deep dives. This work is one of the first studies which was able to collect electrocardiogram (ECG) data from SCUBA divers at various depths (33, 66, 99, 150, and 200 ftsw; equivalent to 10.05, 20.10, 30.17, 45.72, and 60.96 m of salt water, respectively) breathing different gas mixtures (air, nitrox and trimix). The aim of this study was to shed light on cardiac ANS behavior during dives, including deep dives. With the aid of dry suits, a Holter monitor that could handle the pressure of a 200 ft. dive, and a novel algorithm that can provide a useful assessment of the ANS from the ECG signal, we investigated the effects of SCUBA dives with different time durations, depths and gas mixtures on the ANS. Principal dynamic mode (PDM) analysis of the ECG, which has been shown to provide accurate separation of the sympathetic and parasympathetic dynamics, was employed to assess the difference of ANS behavior between baseline and diving conditions of varying depths and gas mixtures consisting of air, nitrox and trimix. For all depths and gas mixtures, we found consistent dominance in the parasympathetic activity and a concomitant increase of the parasympathetic dynamics with increasing diving duration and depth. For 33 and 66 ft. dives, we consistently found significant decreases in heart rates (HR) and concomitant increases in parasympathetic activities as estimated via the PDM and root mean square of successive differences (RMSSD) for all time intervals (from the first 5 min to the last 30 min) at the bottom depth when compared to the baseline depth at sea level. The sympathetic dynamics did not change with dive duration or gas mixtures, but at the 150 and 200 ft. dives, we found a significant increase in the sympathetic dynamics in addition to the elevated parasympathetic dynamics when compared to baseline The power spectral density (PSD) measures such as the low frequency (LF), high frequency (HF) and its ratio, and approximate entropy (ApEn) indices were not as consistent when compared to PDM-derived parasympathetic dynamics and RMSSD index.

Feasibility Testing of Hydrophobic Carbon Electrodes for Acquisition of Underwater Surface Electromyography Data

Underwater surface electromyography (sEMG) signals are especially of interest for rehabilitation and sports medicine applications. Silver/silver chloride (Ag/AgCl) hydrogel electrodes, although the gold standard for sEMG data collection, require waterproofing for underwater applications. Having to apply waterproof tape over electrodes impedes the deployment of sEMG in immersed conditions. As a better alternative for underwater applications, we have developed carbon black/polydimethylsiloxane (CB/PDMS) electrodes for collecting sEMG signals under water. We recruited twenty subjects to collect simultaneous recordings of sEMG signals using Ag/AgCl and CB/PDMS electrodes on biceps brachii, triceps brachii, and tibial anterior muscles. The Ag/AgCL electrodes were covered in waterproof tape, and the CB/PDMS electrodes were not. We found no differences in sEMG signal amplitudes between both sensors, for the three muscles. Moderate mean correlation between Ag/AgCl and CB/PDMS electrodes was found on the linear envelopes (≥ 0.7); correlation was higher for power spectral densities (≥ 0.84). Ag/AgCl electrodes performed better in response to noise, whilst the CB/PDMS electrodes were more sensitive to myoelectric activity in triceps and tibialis, and exhibited better response to motion artifacts in the measurements on the triceps and tibialis. Results suggest that sEMG signal collection is possible under water using CB/PDMS electrodes without requiring any waterproof or adhesive tape.