Nasal pressure recordings for automatic snoring detection.

This study presents a rule-based method for automated, real-time snoring detection using nasal pressure recordings during overnight sleep. Although nasal pressure recordings provide information regarding nocturnal breathing abnormalities in a polysomnography (PSG) study or continuous positive airway pressure (CPAP) system, an objective assessment of snoring detection using these nasal pressure recordings has not yet been reported in the literature. Nasal pressure recordings were obtained from 55 patients with obstructive sleep apnea. The PSG data were also recorded simultaneously to evaluate the proposed method. This rule-based method for automatic, real-time snoring detection employed preprocessing, short-time energy and the central difference method. Using this methodology, a sensitivity of 85.4% and a positive predictive value of 92.0% were achieved in all patients. Therefore, we concluded that the proposed method is a simple, portable and cost-effective tool for real-time snoring detection in PSG and CPAP systems that does not require acoustic analysis using a microphone.

Development of a noncontact heart rate monitoring system for sedentary behavior based on an accelerometer attached to a chair.

Although people spend a third of their day engaged in sedentary activities, research on heart activity during sitting is almost nonexistent because of the discomfort experienced when electrocardiogram (ECG) measurement electrodes are attached to the body. Accordingly, in this study, a system was developed to monitor heart rate (HR) in a noncontact and unconstrained way while subjects were seated, by attaching an accelerometer on the backrest of a chair. Acceleration signals were obtained three times from 20 healthy adults, a detection algorithm was applied, and HR detection performance was evaluated by comparing the R-peak values from the ECG. The system had excellent performance results, with a sensitivity of 96.10% and a positive predictive value of 96.43%. In addition, the HR calculated by the new system developed in this study was compared with HR calculated using ECG. The new system exhibited excellent performance; its results were strongly correlated with that of ECG (r = 0.97, p [Formula: see text] 0.0001; average difference of -0.08  ±  4.60 [mean ± 1.96∙standard deviation] in Bland-Altman analysis). Accordingly, the method presented in this study is expected to be applicable for evaluating diverse autonomic nervous system components in a noncontact and unconstrained way using an accelerometer to monitor the HR of sedentary workers or adolescents.

Automatic classification of apnea/hypopnea events through sleep/wake states and severity of SDB from a pulse oximeter.

This study proposes a method of automatically classifying sleep apnea/hypopnea events based on sleep states and the severity of sleep-disordered breathing (SDB) using photoplethysmogram (PPG) and oxygen saturation (SpO2) signals acquired from a pulse oximeter. The PPG was used to classify sleep state, while the severity of SDB was estimated by detecting events of SpO2 oxygen desaturation. Furthermore, we classified sleep apnea/hypopnea events by applying different categorisations according to the severity of SDB based on a support vector machine. The classification results showed sensitivity performances and positivity predictive values of 74.2% and 87.5% for apnea, 87.5% and 63.4% for hypopnea, and 92.4% and 92.8% for apnea + hypopnea, respectively. These results represent better or comparable outcomes compared to those of previous studies. In addition, our classification method reliably detected sleep apnea/hypopnea events in all patient groups without bias in particular patient groups when our algorithm was applied to a variety of patient groups. Therefore, this method has the potential to diagnose SDB more reliably and conveniently using a pulse oximeter.

Automatic snoring detection from nasal pressure data.

This study presents a method for automatic snoring detection from a nasal pressure data. First, a spectrogram analysis was performed in order to obtain information about the spectral characteristic of nasal pressure data. The automatic method is based on a simple signal filtering and short-time energy technique. Fifteen patients were participated in order to evaluation the performance of the proposed method. Results are compared with manually labeled snoring events by watching video records. The sensitivity and positive predictivity value were 93.73% and 93.70%, respectively. The results in this study could provide sleep experts with the method to objectively monitor sleep-disordered breathing in CPAP system or PSG study.

Snoring detection using a piezo snoring sensor based on hidden Markov models.

This study presents a snoring detection method based on hidden Markov models (HMMs) using a piezo snoring sensor. Snoring is a major symptom of obstructive sleep apnea (OSA). In most sleep studies, snoring is detected with a microphone. Since these studies analyze the acoustic properties of snoring, they need to acquire data at high sampling rates, so a large amount of data should be processed. Recently, several sleep studies have monitored snoring using a piezo snoring sensor. However, an automatic method for snoring detection using a piezo snoring sensor has not been reported in the literature. This study proposed the HMM-based method to detect snoring using this sensor, which is attached to the neck. The data from 21 patients with OSA were gathered for training and test sets. The short-time Fourier transform and short-time energy were computed so they could be applied to HMMs. The data were classified as snoring, noise and silence according to their HMMs. As a result, the sensitivity and the positive predictivity values were 93.3% and 99.1% for snoring detection, respectively. The results demonstrated that the method produced simple, portable and user-friendly detection tools that provide an alternative to the microphone-based method.

Repeatability of the accelerometric-based method to detect step events for hemiparetic stroke patients.

This study is to evaluate the repeatability of the accelerometric-method to detect step events for hemiparetic stroke patients. To evaluate this method, four adults with chronic hemiparetic stroke were participated. The repeatability of this method using a single three-axis accelerometer was evaluated with a six optical camera motion capture system. The correlation statistics and Bland-Altman plot were then used to evaluate the agreement between the step-time differences from the accelerometer data and the reflective markers data. The correlation coefficient of each two data was 0.99 (p < 0.001) and retest result was 0.99 (p < 0.001). The mean ± standard deviation (SD) between each two data along with the 95% limits of agreement (LOA = ± 1.96 SD) was 2.58 ± 2.37 ms (LOA = -2.07 ms and 7.23 ms), and retest result was 3.73 ± 2.02 ms (LOA = -0.22 ms and 7.68 ms). These results show that the suggested method is useful to detect step events for hemiparetic stroke patients.

Development of a cardiorespiratory monitoring system based on pressure change of aircushion.

Heartbeat and respiration are fundamental vital signs used for estimation of patient's status. In this study, we have proposed a simple method to monitor the heartbeat and respiration based on displacements of human body which occur due to periodic heartbeat and breathing.

Novel algorithm for the hemiplegic gait evaluation using a single 3-axis accelerometer.

This paper suggests the novel algorithm for the estimating gait parameters of the hemiplegic patients using a 3-axis accelerometer. The signal processing for algorithm consists of a bandpass filter and a least square acceleration filter. To evaluate the performance of the algorithm, the correlation coefficients of the stride and the step time between the 3-axis accelerometer and the Vicon motion analysis system are compared. In consequence, correlation coefficient ranged from 0.90 to 0.99 for patients and ranged from 0.92 to 0.99 for normal subjects. The results showed that the novel algorithm is very useful for estimating not only hemiplegic gait but also normal gait.

A new noise reduction method for oscillometric blood pressure measurement.

In this paper, a new noise reduction method was proposed for oscillometric blood pressure measurement. The proposed method uses a capacitive sensor and an adaptive filter to minimize blood pressure measurement error. Noise such as undesired external pressure applied to cuff was focused on. Three types of the distorted oscillation signals (no overlap, non-consecutive overlap, consecutive overlap between the noise and the oscillation) were used to compare the conventional method using linear interpolation and the proposed method using the adaptive filter. The proposed method outperformed the conventional method in the case of consecutive overlap between the noise and the oscillation. The proposed method could be useful for measuring blood pressure in such a noisy environment that the subject is being transported.

The concurrent validity of the body center of mass in accelerometric measurement.

Accurate estimation of the body center of mass (COM) location has important clinical ramifications in locomotion associated with daily activities of living (ADL). This paper compared two computational estimation methods of COM using an accelerometric measurement and a VICON motion analysis system measurement (established or golden standard), respectively. A convenient sample of four healthy subjects participated. The body COM was measured when the subjects walked on the 6-m long walkway at their self-selected speed for 5 trials. VICON and accelerometer COM data signals were computed by VICON Polygon and trapezoidal double integration methods, respectively and compared to determine the concurrent validity of our COM estimation method. Correlation statistics showed excellent relationship between the two methods (r =0.87, P< 0.05), reflecting an acceptable validity. Our results suggest that the COM estimation using the accelerometer method is as accurate as the conventional method and provide clinical insights when designing a portable and inexpensive COM measurement tool for locomotion evaluation.