Aseptic survival of the 1.5-stage exchange arthroplasty for periprosthetic joint infection was acceptable when using an autoclaved femoral component and a new polyethylene insert.

PURPOSE: To investigate the aseptic survival of 1.5-stage exchange arthroplasty for periprosthetic joint infection (PJI) after total knee arthroplasty (TKA). METHODS: Eighty-eight cases of 1.5-stage exchange arthroplasty for PJI without reinfection were retrospectively analysed. The autoclaved femoral component and new polyethylene insert (PE) were implanted using antibiotic mixed cement. The explanted tibial component was not reinserted. The Western Ontario and McMaster Universities Osteoarthritis Index and range of motion were clinically evaluated preoperatively and at the last follow-up (the last time for the implant in situ). Radiographically, hip-knee-ankle angle (HKA) and component positions were measured preoperatively, postoperatively (1 month after the 1.5-stage exchange arthroplasty), and at the last follow-up. The survival rate was analysed using the Kaplan-Meier method, in which failure was defined as reoperation due to aseptic failure. Mean period to failure and failure site were analysed. Factors affecting survival were investigated in terms of demographics and inappropriateness of the postoperative HKA (HKA > 0 ± 3°) and component positions (α angle > 95 ± 3°, ß angle > 90 ± 3°, γ angle > 3 ± 3°, and δ angle > 87 ± 3°). RESULTS: The spacer in-situ time was 3.7 years (0.2-6.4). The clinical results improved hip-knee-ankle significantly at the last follow-up. Radiographically, the average HKA was valgus 0.1° postoperatively. The average α, ß, γ, and δ angles of the postoperative component positions were 95.9°, 90.4°, 3.8°, and 86.7°, respectively. The 1-, 2-, and 5-year postoperative survival rates were 90.9%, 86.4%, and 80.6%, respectively. The mean period to failure was 2.0 years (0.2-5.3). There were 18 cases of aseptic loosening (20.8%), occurring on both the femur and tibial sides in 1 knee, and only on the tibial side in 17 knees. Inappropriate coronal position of the PE (ß angle > 90 ± 3°) was a significant factor affecting survival (odds ratio = 5.491; p = 0.011). CONCLUSION: The aseptic survival of the 1.5-stage exchange arthroplasty was acceptable when using an autoclaved femoral component and new PE. The appropriate coronal position of the PE helps ensure favourable survival of 1.5-stage exchange arthroplasty. LEVEL OF EVIDENCE: IV.

External Auditory Stimulation as a Non-Pharmacological Sleep Aid.

The increased demand for well-being has fueled interest in sleep. Research in technology for monitoring sleep ranges from sleep efficiency and sleep stage analysis to sleep disorder detection, centering on wearable devices such as fitness bands, and some techniques have been commercialized and are available to consumers. Recently, as interest in digital therapeutics has increased, the field of sleep engineering demands a technology that helps people obtain quality sleep that goes beyond the level of monitoring. In particular, interest in sleep aids for people with or without insomnia but who cannot fall asleep easily at night is increasing. In this review, we discuss experiments that have tested the sleep-inducing effects of various auditory stimuli currently used for sleep-inducing purposes. The auditory stimulations were divided into (1) colored noises such as white noise and pink noise, (2) autonomous sensory meridian response sounds such as natural sounds such as rain and firewood burning, sounds of whispers, or rubbing various objects with a brush, and (3) classical music or a preferred type of music. For now, the current clinical method of receiving drugs or cognitive behavioral therapy to induce sleep is expected to dominate. However, it is anticipated that devices or applications with proven ability to induce sleep clinically will begin to appear outside the hospital environment in everyday life.

Drowsiness Detection Based on Intelligent Systems with Nonlinear Features for Optimal Placement of Encephalogram Electrodes on the Cerebral Area.

Drowsiness while driving can lead to accidents that are related to the loss of perception during emergencies that harm the health. Among physiological signals, brain waves have been used as informative signals for the analyses of behavioral observations, steering information, and other biosignals during drowsiness. We inspected the machine learning methods for drowsiness detection based on brain signals with varying quantities of information. The results demonstrated that machine learning could be utilized to compensate for a lack of information and to account for individual differences. Cerebral area selection approaches to decide optimal measurement locations could be utilized to minimize the discomfort of participants. Although other statistics could provide additional information in further study, the optimized machine learning method could prevent the dangers of drowsiness while driving by considering a transitional state with nonlinear features. Because brain signals can be altered not only by mental fatigue but also by health status, the optimization analysis of the system hardware and software will be able to increase the power-efficiency and accessibility in acquiring brain waves for health enhancements in daily life.

A Comparative Study of Brachial-Ankle Pulse Wave Velocity and Heart-Finger Pulse Wave Velocity in Korean Adults.

Arterial stiffness is considered an index of vascular aging. The brachial-ankle pulse wave velocity (baPWV) method is widely used because of its proven effectiveness; and the pulse wave velocity measurement method using both electrocardiogram (ECG) and photoplethysmogram (PPG) is actively being studied due to the convenience of measurement and the possibility of miniaturization. The aim of this study was to evaluate and compare the effects of age and gender in Korean adults using both the baPWV method and the PWV method with ECG and finger PPG (heart-finger PWV). The measurements have been carried out for 185 healthy subjects of Korean adults, and the results showed that the baPWV was highly correlated with age in both genders (r = 0.94 for both males and females). However, the correlation values in heart-finger PWV measurement were significantly lower than those of baPWV (r = 0.37 for males and r = 0.71 for females). Although the heart-finger PWV method is suitable for mobile applications because it can be easily miniaturized while maintaining its signal quality, these results show that the heart-finger PWV method is not as effective as baPWV at evaluating the arterial stiffness.

Novel heart rate variability index for wrist-worn wearable devices subject to motion artifacts that complicate measurement of the continuous pulse interval.

OBJECTIVE: Wearable health monitoring devices have recently become popular, but they can still only measure the average heart rate. Heart rate variability (HRV) is known to represent changes in the autonomic nervous system and analysis of HRV has the potential to be used for monitoring various wellness-related parameters such as sleep or stress. HRV analysis requires accurate measurement of the heartbeat interval. In wearable devices, it is difficult to accurately measure the heartbeat interval due to motion noise. In this paper we propose a new method for performing HRV analysis on photoplethysmographic (PPG) signals corrupted by motion artifacts measured at the wrist. APPROACH: A frequency-tracking algorithm based on the oscillator-based adaptive notch filter was used to measure instantaneous heart rate. The algorithm consists of a time-varying bandpass filter for enhancing the heartbeat signal and an adaptive mechanism for tracking heart rate frequency. By optimizing the filter bandwidth and forgetting factor of the adaptive mechanism, the frequency-tracking algorithm better reflects the variability of instantaneous heart rate. The new HRV index was calculated as the standard deviation of the heartbeat interval data converted using the heart rate estimated by the frequency-tracking algorithm. In order to verify the effectiveness of the proposed index, the new HRV index calculated for each sleep stage was compared with SDNN, the standard deviation of the heartbeat interval, which was calculated using simultaneous electrocardiogram measurements. In addition, changes in SDNN and the new index were compared during a socially evaluated speech task. Finally, the relationship between the new index and SDNN was compared with the data collected during daily activities over a 24 h period. MAIN RESULTS: Experimental results showed that statistically significant changes in HRV could be monitored in different sleep stages using the proposed method. In addition, when subjects were stressed by a socially evaluated speech task, significant reduction in HRV was observed using the proposed method. Finally, HRV values measured during daily activities over a 24 h period showed a high correlation coefficient of 0.812 with reference HRVs. SIGNIFICANCE: The new HRV index calculated by the proposed method is expected to be an effective new solution for noisy PPG signals.

Enhancing the Usability of Brain-Computer Interface Systems.

Brain-computer interfaces (BCIs) aim to enable people to interact with the external world through an alternative, nonmuscular communication channel that uses brain signal responses to complete specific cognitive tasks. BCIs have been growing rapidly during the past few years, with most of the BCI research focusing on system performance, such as improving accuracy or information transfer rate. Despite these advances, BCI research and development is still in its infancy and requires further consideration to significantly affect human experience in most real-world environments. This paper reviews the most recent studies and findings about ergonomic issues in BCIs. We review dry electrodes that can be used to detect brain signals with high enough quality to apply in BCIs and discuss their advantages, disadvantages, and performance. Also, an overview is provided of the wide range of recent efforts to create new interface designs that do not induce fatigue or discomfort during everyday, long-term use. The basic principles of each technique are described, along with examples of current applications in BCI research. Finally, we demonstrate a user-friendly interface paradigm that uses dry capacitive electrodes that do not require any preparation procedure for EEG signal acquisition. We explore the capacitively measured steady-state visual evoked potential (SSVEP) response to an amplitude-modulated visual stimulus and the auditory steady-state response (ASSR) to an auditory stimulus modulated by familiar natural sounds to verify their availability for BCI. We report the first results of an online demonstration that adopted this ergonomic approach to evaluating BCI applications. We expect BCI to become a routine clinical, assistive, and commercial tool through advanced EEG monitoring techniques and innovative interface designs.

The Effect of Optical Crosstalk on Accuracy of Reflectance-Type Pulse Oximeter for Mobile Healthcare.

According to the theoretical equation of the pulse oximeter expressed by the ratio of amplitude (AC) and baseline (DC) obtained from the photoplethysmographic signal of two wavelengths, the difference of the amount of light absorbed depending on the melanin indicating the skin color is canceled by normalizing the AC value to the DC value of each wavelength. Therefore, theoretically, skin color does not affect the accuracy of oxygen saturation measurement. However, if there is a direct path for the light emitting unit to the light receiving unit instead of passing through the human body, the amount of light reflected by the surface of the skin changes depending on the color of the skin. As a result, the amount of crosstalk that varies depending on the skin color affects the ratio of AC to DC, resulting in errors in the calculation of the oxygen saturation value. We made crosstalk sensors and crosstalk-free sensors and performed desaturation experiments with respiratory gas control on subjects with various skin colors to perform oxygen saturation measurements ranging from 60 to 100%. Experimental results showed that there was no difference in the measurement error of oxygen saturation according to skin color in the case of the sensor which prevented crosstalk (-0.8824 ± 2.2859 for Asian subjects, 0.6741 ± 3.2822 for Caucasian subjects, and 0.9669 ± 2.2268 for African American subjects). However, a sensor that did not prevent crosstalk showed a large error in dark skin subjects (0.8258 ± 2.1603 for Asian subjects, 0.8733 ± 1.9716 for Caucasian subjects, and -3.0591 ± 3.9925 for African Americans). Based on these results, we reiterate the importance of sensor design in the development of pulse oximeters using reflectance-type sensors.

Real-Time Automatic Apneic Event Detection Using Nocturnal Pulse Oximetry.

OBJECTIVE: Nocturnal pulse oximetry has been proposed as a simpler alternative to polysomnography in diagnosing sleep apnea. However, existing techniques are limited in terms of inability to provide time information on sleep apnea occurrence. This study aimed to propose a new strategy for near real-time automatic detection of apneic events and reliable estimation of apnea-hypopnea index using nocturnal pulse oximetry. METHODS: Among 230 polysomnographic recordings with apnea-hypopnea index values ranging from 0 to 86.5 events/h, 138 (60%) and the remaining 92 recordings (40%) were categorized as training and test sets, respectively. By extracting the quantitative characteristics caused by the apneic event for the amount and duration of the change in blood oxygen saturation value, we established the criteria to determine the occurrence of apneic event. Regression modeling was used to estimate the apnea-hypopnea index from the apneic event detection results. RESULTS: The minute-by-minute apneic segment detection exhibited an average accuracy of 91.0% and an average Cohen's kappa coefficient of 0.71. Between the apnea-hypopnea index estimations and reference values, the mean absolute error was 2.30 events/h. The average accuracy of our diagnosis of sleep apnea was 96.7% for apnea-hypopnea index cutoff values of ≥5, 10, 15, and 30 events/h. CONCLUSION: We developed an effective strategy to detect apneic events by using morphometric characteristics in the fluctuation of blood oxygen saturation values. SIGNIFICANCE: Our study could be potentially useful in home-based multinight apneic event monitoring for purposes of therapeutic intervention and follow-up study on sleep apnea.

Reliability of the Parabola Approximation Method in Heart Rate Variability Analysis Using Low-Sampling-Rate Photoplethysmography.

Photoplethysmographic signals are useful for heart rate variability analysis in practical ambulatory applications. While reducing the sampling rate of signals is an important consideration for modern wearable devices that enable 24/7 continuous monitoring, there have not been many studies that have investigated how to compensate the low timing resolution of low-sampling-rate signals for accurate heart rate variability analysis. In this study, we utilized the parabola approximation method and measured it against the conventional cubic spline interpolation method for the time, frequency, and nonlinear domain variables of heart rate variability. For each parameter, the intra-class correlation, standard error of measurement, Bland-Altman 95% limits of agreement and root mean squared relative error were presented. Also, elapsed time taken to compute each interpolation algorithm was investigated. The results indicated that parabola approximation is a simple, fast, and accurate algorithm-based method for compensating the low timing resolution of pulse beat intervals. In addition, the method showed comparable performance with the conventional cubic spline interpolation method. Even though the absolute value of the heart rate variability variables calculated using a signal sampled at 20 Hz were not exactly matched with those calculated using a reference signal sampled at 250 Hz, the parabola approximation method remains a good interpolation method for assessing trends in HRV measurements for low-power wearable applications.

Effect of Missing Inter-Beat Interval Data on Heart Rate Variability Analysis Using Wrist-Worn Wearables.

Most of the wrist-worn devices on the market provide a continuous heart rate measurement function using photoplethysmography, but have not yet provided a function to measure the continuous heart rate variability (HRV) using beat-to-beat pulse interval. The reason for such is the difficulty of measuring a continuous pulse interval during movement using a wearable device because of the nature of photoplethysmography, which is susceptible to motion noise. This study investigated the effect of missing heart beat interval data on the HRV analysis in cases where pulse interval cannot be measured because of movement noise. First, we performed simulations by randomly removing data from the RR interval of the electrocardiogram measured from 39 subjects and observed the changes of the relative and normalized errors for the HRV parameters according to the total length of the missing heart beat interval data. Second, we measured the pulse interval from 20 subjects using a wrist-worn device for 24 h and observed the error value for the missing pulse interval data caused by the movement during actual daily life. The experimental results showed that mean NN and RMSSD were the most robust for the missing heart beat interval data among all the parameters in the time and frequency domains. Most of the pulse interval data could not be obtained during daily life. In other words, the sample number was too small for spectral analysis because of the long missing duration. Therefore, the frequency domain parameters often could not be calculated, except for the sleep state with little motion. The errors of the HRV parameters were proportional to the missing data duration in the presence of missing heart beat interval data. Based on the results of this study, the maximum missing duration for acceptable errors for each parameter is recommended for use when the HRV analysis is performed on a wrist-worn device.

Music and natural sounds in an auditory steady-state response based brain-computer interface to increase user acceptance.

Patients with total locked-in syndrome are conscious; however, they cannot express themselves because most of their voluntary muscles are paralyzed, and many of these patients have lost their eyesight. To improve the quality of life of these patients, there is an increasing need for communication-supporting technologies that leverage the remaining senses of the patient along with physiological signals. The auditory steady-state response (ASSR) is an electro-physiologic response to auditory stimulation that is amplitude-modulated by a specific frequency. By leveraging the phenomenon whereby ASSR is modulated by mind concentration, a brain-computer interface paradigm was proposed to classify the selective attention of the patient. In this paper, we propose an auditory stimulation method to minimize auditory stress by replacing the monotone carrier with familiar music and natural sounds for an ergonomic system. Piano and violin instrumentals were employed in the music sessions; the sounds of water streaming and cicadas singing were used in the natural sound sessions. Six healthy subjects participated in the experiment. Electroencephalograms were recorded using four electrodes (Cz, Oz, T7 and T8). Seven sessions were performed using different stimuli. The spectral power at 38 and 42Hz and their ratio for each electrode were extracted as features. Linear discriminant analysis was utilized to classify the selections for each subject. In offline analysis, the average classification accuracies with a modulation index of 1.0 were 89.67% and 87.67% using music and natural sounds, respectively. In online experiments, the average classification accuracies were 88.3% and 80.0% using music and natural sounds, respectively. Using the proposed method, we obtained significantly higher user-acceptance scores, while maintaining a high average classification accuracy.

Sleep Period Time Estimation Based on Electrodermal Activity.

We proposed and tested a method to estimate sleep period time (SPT) using electrodermal activity (EDA) signals. Eight healthy subjects and six obstructive sleep apnea patients participated in the experiments. Each subject's EDA signals were measured at the middle and ring fingers of the dominant hand during polysomnography (PSG). For nine of the 17 participants, wrist actigraphy was also measured for a quantitative comparison of EDA- and actigraphy-based methods. Based on the training data, we observed that sleep onset was accompanied by a gradual reduction of amplitude of the EDA signals, whereas sleep offset was accompanied by a rapid increase in amplitude of EDA signals. We developed a method based on these EDA fluctuations during sleep-wake transitions, and applied it to a test dataset. The performance of the method was assessed by comparing its results with those from a physician's sleep stage scores. The mean absolute errors in the obtained values for sleep onset, offset, and period time between the proposed method, and the results of the PSG were 4.1, 3.0, and 6.1 min, respectively. Furthermore, there were no significant differences in the corresponding values between the methods. We compared these results with those obtained by applying actigraphic methods, and found that our algorithm outperformed these in terms of each estimated parameter of interest in SPT estimation. Long awakening periods were also detected based on sympathetic responses reflected in the EDA signals. The proposed method can be applied to a daily sleep monitoring system.

Reliability of ultra-short-term analysis as a surrogate of standard 5-min analysis of heart rate variability.

BACKGROUND: Despite the increasing demands of ultra-short-term heart rate (HR) variability (HRV) for practical ambulatory applications, there have been few studies that have investigated R-R interval recording for less than 5 min for HRV analysis. It has not been extensively validated, and, currently, no normative data for ultra-short-term HRV exist. The aim of this study was to investigate the relationship between standard 5-min and ultra-short-term HRV by collecting data from a large population consisting of a wide range of age groups. MATERIALS AND METHODS: The 5-min R-R interval series were obtained from 467 healthy volunteers ranging from 8 to 69 years of age. The original R-R interval was segmented into 270, 240, 210, 180, 150, 120, 90, 60, 30, 20, and 10 s, and those HRV features most commonly reported within the literature were calculated and compared with those using the original 5-min R-R interval series. The Pearson correlation r, the p value by the Kruskal-Wallis test, and the Bland-Altman plot analysis computations were performed for each HRV variable calculated using different lengths of R-R interval series. RESULTS: For each HRV variable, the minimum length of the R-R interval required to reliably estimate the 5-min HRV was identified. The results were different for each age group: 10 s for HR, 20 s for high-frequency, 30 s for root mean square difference, 60 s for proportion of the number of interval differences of successive NN intervals greater than 50 ms divided by total number of NNs, 90 s for low-frequency, normalized low-frequency, normalized high-frequency, and low-frequency/high-frequency, 240 s for standard deviation of successive NN interval differences and time-frequency, and 270 s for very low-frequency. In addition, the reference value for short-term HRV from normal healthy subjects was also presented. CONCLUSIONS: Some HRV variables calculated from R-R interval series shorter than 5 min were well matched with those calculated from the 5-min R-R interval. Thus, ultra-short-term HRV is likely to be a good surrogate method to assess trends in HRV.

Photoplethysmographic signals to predict the success of lumbar sympathetic blockade for lower extremity pain.

OBJECTIVE: A prospective, observational study to investigate how photoplethysmography (PPG) signals change during lumbar sympathetic blockade (LSB), and whether these changes can predict sympathetically mediated pain (SMP). METHODS: Patients with unilateral lower extremity pain and self-reported cold hyperalgesia underwent LSB. Bilateral temperature and PPG signals (AC and DC) were recorded. Power spectrum analysis (PSA) was performed. RESULTS: Of the total patient cohort (n = 38), eight patients (22.1%) had excellent pain-relief after LSB and were determined to have SMP. In all patients, the PPG AC signal changed immediately after drug administration, before any temperature change. DC signals decreased slowly in a linear fashion. PSA of DC signals showed significantly lower low-frequency/high-frequency (LF/HF) ratios in the SMP group than the sympathetically independent pain group, both before and after LSB. A cut-off value of 2.92 for LF/HF resulted in sensitivity, specificity and positive predictive values for SMP of 75.0%, 76.7% and 3.21 [1.5, 6.9], respectively. CONCLUSIONS: PPG may be used as an early indicator of a successful LSB and could also be helpful in diagnosing SMP.

An amplitude-modulated visual stimulation for reducing eye fatigue in SSVEP-based brain-computer interfaces.

OBJECTIVE: A high-frequency steady-state visual evoked potential (SSVEP) has been suggested for the reduction of eye fatigue for SSVEP-based brain-computer interfaces (BCIs). However, the poor performance of high-frequency SSVEP requires a novel stimulus of better performance even with low eye fatigue. As an alternative to the high-frequency SSVEP, we explore the SSVEP response to an amplitude-modulated stimulus (AM-SSVEP) to verify its availability for brain-computer interfaces (BCIs). METHODS: An amplitude-modulated stimulus was generated as the product of two sine waves at a carrier frequency (fc) and a modulating frequency (fm). The carrier frequency was higher than 40 Hz to reduce eye fatigue, and the modulating frequency ranged around the α-band (9-12 Hz) to utilize low-frequency harmonic information. Four targets were used in combinations of three different modulating frequencies and two different carrier frequencies in the offline experiment, and two additional targets were added with one additional modulating and one carrier frequency in online experiments. RESULTS: In the AM-SSVEP spectra, seven harmonic components were identified at 2fc, 2fm, fc±fm, fc±3fm, and 2fc-4fm. Using an optimized combination of the harmonic frequencies, online experiments demonstrated that the accuracy of the AM-SSVEP was equivalent to that of the low-frequency SSVEP. Furthermore, subject evaluation indicated that an AM stimulus caused lower eye fatigue and less sensing of flickering than a low-frequency stimulus, in a manner similar to a high-frequency stimulus. CONCLUSIONS: The actual stimulus frequencies of AM-SSVEPs are in the high-frequency band, resulting in reduced eye fatigue. Furthermore, AM-SSVEPs can utilize both fundamental stimulus frequencies and non-integer harmonic frequencies including low frequencies for SSVEP recognition. The feasibility of AM-SSVEP with high BCI performance and low eye fatigue was confirmed through offline and online experiments. SIGNIFICANCE: AM-SSVEPs combine the advantages of both low- and high-frequency SSVEPs--high power and low eye fatigue, respectively. AM-SSVEP-based BCI systems exploit these advantages, making them promising for application in practical BCI systems.

Brain-computer interfaces using capacitive measurement of visual or auditory steady-state responses.

OBJECTIVE: Brain-computer interface (BCI) technologies have been intensely studied to provide alternative communication tools entirely independent of neuromuscular activities. Current BCI technologies use electroencephalogram (EEG) acquisition methods that require unpleasant gel injections, impractical preparations and clean-up procedures. The next generation of BCI technologies requires practical, user-friendly, nonintrusive EEG platforms in order to facilitate the application of laboratory work in real-world settings. APPROACH: A capacitive electrode that does not require an electrolytic gel or direct electrode-scalp contact is a potential alternative to the conventional wet electrode in future BCI systems. We have proposed a new capacitive EEG electrode that contains a conductive polymer-sensing surface, which enhances electrode performance. This paper presents results from five subjects who exhibited visual or auditory steady-state responses according to BCI using these new capacitive electrodes. The steady-state visual evoked potential (SSVEP) spelling system and the auditory steady-state response (ASSR) binary decision system were employed. MAIN RESULTS: Offline tests demonstrated BCI performance high enough to be used in a BCI system (accuracy: 95.2%, ITR: 19.91 bpm for SSVEP BCI (6 s), accuracy: 82.6%, ITR: 1.48 bpm for ASSR BCI (14 s)) with the analysis time being slightly longer than that when wet electrodes were employed with the same BCI system (accuracy: 91.2%, ITR: 25.79 bpm for SSVEP BCI (4 s), accuracy: 81.3%, ITR: 1.57 bpm for ASSR BCI (12 s)). Subjects performed online BCI under the SSVEP paradigm in copy spelling mode and under the ASSR paradigm in selective attention mode with a mean information transfer rate (ITR) of 17.78 ± 2.08 and 0.7 ± 0.24 bpm, respectively. SIGNIFICANCE: The results of these experiments demonstrate the feasibility of using our capacitive EEG electrode in BCI systems. This capacitive electrode may become a flexible and non-intrusive tool fit for various applications in the next generation of BCI technologies.

Conductive polymer foam surface improves the performance of a capacitive EEG electrode.

In this paper, a new conductive polymer foam-surfaced electrode was proposed for use as a capacitive EEG electrode for nonintrusive EEG measurements in out-of-hospital environments. The current capacitive electrode has a rigid surface that produces an undefined contact area due to its stiffness, which renders it unable to conform to head curvature and locally isolates hairs between the electrode surface and scalp skin, making EEG measurement through hair difficult. In order to overcome this issue, a conductive polymer foam was applied to the capacitive electrode surface to provide a cushioning effect. This enabled EEG measurement through hair without any conductive contact with bare scalp skin. Experimental results showed that the new electrode provided lower electrode-skin impedance and higher voltage gains, signal-to-noise ratios, signal-to-error ratios, and correlation coefficients between EEGs measured by capacitive and conventional resistive methods compared to a conventional capacitive electrode. In addition, the new electrode could measure EEG signals, while the conventional capacitive electrode could not. We expect that the new electrode presented here can be easily installed in a hat or helmet to create a nonintrusive wearable EEG apparatus that does not make users look strange for real-world EEG applications.

Effect of missing RR-interval data on nonlinear heart rate variability analysis.

The effects of missing RR-interval data on nonlinear heart rate variability (HRV) analysis were investigated using simulated missing data in actual RR-interval tachograms and actual missing RR-interval data. For the simulation study, randomly selected data (ranging from 0 to 100s) were removed from actual data in the MIT-BIH normal sinus rhythm RR-interval database. The selected data are considered as a simulated artefact section. In all, 7182 tachograms of 5-min duration were used for this analysis. For each missing interval, the analysis was performed by 100 Monte Carlo runs. Poincaré plot, detrended fluctuation, and entropy analysis were executed for the nonlinear HRV parameters in each run, and the normalized errors between the data with and without the missing data duration for these parameters, were calculated. In this process, the usefulness of reconstruction was considered, for which bootstrapping and several interpolation methods (nearest neighbour, linear, cubic spline, and piecewise cubic Hermite) were used. The rules for the reconstruction, derived from the results of these simulations, were evaluated with actual missing RR-interval data obtained from a capacitive-coupled ECG during sleep. In conclusion, nonlinear parameters, excepting Poincaré-plot-analysis parameters, may not be appropriate for the accurate HRV analysis with missing data, since these parameters have relatively larger error values than time- or frequency-domain HRV parameters. However, the analysis of the long-term variation for nonlinear HRV values can be available through applying the rules for the reconstruction obtained in this study.