Juha M. Kortelainen

Sleep Staging Based on Signals Acquired Through Bed Sensor

We describe a system for the evaluation of the sleep macrostructure on the basis of Emfit sensor foils placed into bed mattress and of advanced signal processing. The signals on which the analysis is based are heart-beat interval (HBI) and movement activity obtained from the bed sensor, the relevant features and parameters obtained through a time-variant autoregressive model (TVAM) used as feature extractor, and the classification obtained through a hidden Markov model (HMM). Parameters coming from the joint probability of the HBI features were used as input to a HMM, while movement features are used for wake period detection. A total of 18 recordings from healthy subjects, including also reference polysomnography, were used for the validation of the system. When compared to wake-nonrapid-eye-movement (NREM)-REM classification provided by experts, the described system achieved a total accuracy of 79±9% and a kappa index of 0.43±0.17 with only two HBI features and one movement parameter, and a total accuracy of 79±10% and a kappa index of 0.44±0.19 with three HBI features and one movement parameter. These results suggest that the combination of HBI and movement features could be a suitable alternative for sleep staging with the advantage of low cost and simplicity.

FFT averaging of multichannel BCG signals from bed mattress sensor to improve estimation of heart beat interval

A multichannel pressure sensing Emfit foil was integrated to a bed mattress for measuring ballistocardiograph signals during sleep. We calculated the heart beat interval with cepstrum method, by applying FFT for short time windows including pair of consequent heart beats. We decreased the variance of FFT by averaging the multichannel data in the frequency domain. Relative error of our method in reference to electrocardiograph RR interval was only 0.35% for 15 night recordings with six normal subjects, when 12% of data was automatically removed due to movement artifacts. Background motivation for this work is given from the studies applying heart rate variability for the sleep staging.

Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors

This study presents different methods for automatic sleep classification based on heart rate variability (HRV), respiration and movement signals recorded through bed sensors. Two methods for feature extraction have been implemented: time variant-autoregressive model (TVAM) and wavelet discrete transform (WDT); the obtained features are fed into two classifiers: Quadratic (QD) and Linear (LD) discriminant for staging sleep in REM, nonREM and WAKE periods. The performances of all the possible combinations of feature extractors and classifiers are compared in terms of accuracy and kappa index, using clinical polysomographyc evaluation as golden standard. 17 recordings from healthy subjects, including also polisomnography, were used to train and test the algorithms. When automatic classification is compared. QD-TVAM algorithm achieved a total accuracy of 76.81 ± 7.51 % and kappa index of 0.55 ± 0.10, while LD-WDT achieved a total accuracy of 79 ± 10% and kappa index of 0.51 ± 0.17. The results suggest that a good sleep evaluation can be achieved through non-conventional recording systems that could be used outside sleep centers.

Improvement of Force-Sensor-Based Heart Rate Estimation Using Multichannel Data Fusion

The aim of this paper is to present and evaluate algorithms for heartbeat interval estimation from multiple spatially distributed force sensors integrated into a bed. Moreover, the benefit of using multichannel systems as opposed to a single sensor is investigated. While it might seem intuitive that multiple channels are superior to a single channel, the main challenge lies in finding suitable methods to actually leverage this potential. To this end, two algorithms for heart rate estimation from multichannel vibration signals are presented and compared against a single-channel sensing solution. The first method operates by analyzing the cepstrum computed from the average spectra of the individual channels, while the second method applies Bayesian fusion to three interval estimators, such as the autocorrelation, which are applied to each channel. This evaluation is based on 28 night-long sleep lab recordings during which an eight-channel polyvinylidene fluoride-based sensor array was used to acquire cardiac vibration signals. The recruited patients suffered from different sleep disorders of varying severity. From the sensor array data, a virtual single-channel signal was also derived for comparison by averaging the channels. The single-channel results achieved a beat-to-beat interval error of 2.2% with a coverage (i.e., percentage of the recording which could be analyzed) of 68.7%. In comparison, the best multichannel results attained a mean error and coverage of 1.0% and 81.0%, respectively. These results present statistically significant improvements of both metrics over the single-channel results (p <; 0.05).

Challenges in Data Management in Product Life Cycle Engineering

It is expected that the capability of managing the complete product life cycle in its phases will give the necessary boost for European Manufacturing Industry. Many efforts have been put into the creation of product lifecycle management systems, but it would seem that there is a gap between the existing reality and the specification of expected features. The article addresses this subject from critical point of view and tries to pinpoint the weaknesses of the existing solutions such as standards and database solutions. This work also tries to show the possible ways to follow that could help in solving the problems.

Automatic detection of sleep macrostructure based on bed sensors

This study analyses the spectral components of the heart rate fluctuations of a new contact-less technology for sleep evaluation. Both heart beat interval (HBI) and movement activity were extracted from the multichannel ballistocardiographic (BCG) measurements, based on Emfit sensor foils placed into bed mattress. Powers spectral densities (PSD) of HBI have been compared with the ones obtained from the standard ECG during sleep stage 2. In addition, spectral features obtained from the contact-less technology and standard ECG has been used to automatically classify the sleep macrostructure through a time-varying autoregressive model and a Hidden Markov Model. Whole night recordings from six subjects were analyzed in this study. Spectral components did not show significant differences between the two measurements. Further, contactless technology achieved a total accuracy of 83 % and kappa index of 0.42, while standard ECG achieved an accuracy of 84 % and kappa index of 0.43 when compared to clinical sleep staging from polysomnography.

Evaluation of Pressure Bed Sensor for Automatic SAHS Screening

We evaluate the performance of an unobtrusive sleep monitoring system in the detection of the sleep apnea- hypopnea syndrome (SAHS). The proposed system is a pressure bed sensor (PBS) that incorporates multiple pressure sensors into a bed mattress to measure several physiological signals of the sleeping subject: respiration; heart rate; and body movements. An automatic algorithm is developed to calculate a respiratory event index (REI). The recordings of 24 patients with suspected sleep problems are analyzed, and the results are compared with the gold standard methods; first with manual scoring of polysomnography to calculate the apnea-hypopnea index (AHI), and second with automatic detection of REI from the respiratory inductive plethysmography belts. The correlation coefficient between AHI and REI from PBS is up to 0.93. Evaluating the ability of PBS in the diagnosis of pathologic (AHI ≥ 5) and nonpathologic (AHI <; 5) subjects, we obtained a sensitivity, specificity, and accuracy of 100%, 92%, and 96%, respectively. To diagnose three levels of SAHS, mild, moderate, and severe, the Cohens kappa value is 0.76. These findings support that PBS recording could provide a simple and unobtrusive method for detection of SAHS in home monitoring.

Evaluation of the sleep quality based on bed sensor signals: Time-variant analysis

Automatic detection of the sleep macrostructure (Wake, NREM -non Rapid Eye Movement- and REM -Rapid Eye Movement-) based on bed sensor signals is presented. This study assesses the feasibility of different methodologies to evaluate the sleep quality out of sleep centers. The study compares a) the features extracted from time-variant autoregressive modeling (TVAM) and Wavelet Decomposition (WD) and b) the performance of K-Nearest Neighbor (KNN) and Feed Forward Neural Networks (FFNN) classifiers. In the current analysis, 17 full polysomnography recordings from healthy subjects were used. The best agreement for Wake-NREM-REM with respect to the gold standard was 71.95 ± 7.47% of accuracy and 0.42 ± 0.10 of kappa index for TVAM-LD while WD-FFNN shows 67.17 ± 11.88% of accuracy and 0.39 ± 0.13 of kappa index. The results suggest that the sleep quality assessment out of sleep centers could be possible and as consequence more people could be beneficiated.

Measurement of Driver’s Visual Attention Capabilities Using Real-Time UFOV Method

This paper proposes a new real-time method to measure the driver’s useful field of view (UFOV) while driving a car in ordinary traffic situations in an urban environment. This is called the real-time useful field of view (rUFOV) method to discriminate it from conventional UFOV measurement, which is typically performed offline and with laboratory equipment developed by Visual Awareness Inc. The proposed real-time method first tracks traffic objects that appear in the driver’s peripheral vision using a road video camera, checks the degree of the driver’s attention to these objects using a driver monitoring camera, and finally calculates the percentage reduction in the driver’s UFOV using a database acquired over an extended period of time. Preliminary results showed better performance than originally expected. The rUFOV method was then incorporated into a driving simulation environment to enable more precise measurement of the driver’s gaze angle. This enabled the performance of safer tests for identifying conditions under which mental load reduced the driver’s visual capabilities, thus increasing the possibility of hasty driving, as well as the incorporation of more accurate control parameters into simulation software for risky driving scenarios. Consequently, this paper proposes a new methodology for measuring the driver’s UFOV as a potential real-time driver support system with automatic intrusive HMI adaptation and immediate alarm functions. The evaluation was conducted in two phases. First, the system was tested in real traffic using typical vehicle equipment and technically worked with a performance level of 81%.In the second phase, more test runs were performed in the simulator environment, which enabled near accident scenarios to be created without risking traffic safety and it was also measured its reaction time..

Industrial open source solutions for product life cycle management

Abstract The authors go through the open source for product life cycle management (PLM) and the efforts done from communities such as the open source initiative. The characteristics of the open source solutions are highlighted as well. Next, the authors go through the requirements for PLM. This is an area where more attention has been given as the manufacturers are competing with the quality and life cycle costs of their products. Especially, the need of companies to try to get a strong position in providing services for their products and thus to make themselves less vulnerable to changes in the market has led to high interest in product life cycle simulation. The potential of applying semantic data management to solve these problems discussed in the light of recent developments. In addition, a basic roadmap is presented as to how the above-described problems could be tackled with open software solutions.