Instability of non-REM sleep in older women evaluated by sleep-stage transition and envelope analyses.

Study objective: Traditionally, age-related deterioration of sleep architecture in older individuals has been evaluated by visual scoring of polysomnographic (PSG) recordings with regard to total sleep time and latencies. In the present study, we additionally compared the non-REM sleep (NREM) stage and delta, theta, alpha, and sigma wave stability between young and older subjects to extract features that may explain age-related changes in sleep. Methods: Polysomnographic recordings were performed in 11 healthy older (72.6 ± 2.4 years) and 9 healthy young (23.3 ± 1.1 years) females. In addition to total sleep time, the sleep stage, delta power amplitude, and delta, theta, alpha, and sigma wave stability were evaluated by sleep stage transition analysis and a novel computational method based on a coefficient of variation of the envelope (CVE) analysis, respectively. Results: In older subjects, total sleep time and slow-wave sleep (SWS) time were shorter whereas wake after sleep onset was longer. The number of SWS episodes was similar between age groups, however, sleep stage transition analysis revealed that SWS was less stable in older individuals. NREM sleep stages in descending order of delta power were: SWS, N2, and N1, and delta power during NREM sleep in older subjects was lower than in young subjects. The CVE of the delta-band is an index of delta wave stability and showed significant differences between age groups. When separately analyzed for each NREM stage, different CVE clusters in NREM were clearly observed between young and older subjects. A lower delta CVE and amplitude were also observed in older subjects compared with young subjects in N2 and SWS. Additionally, lower CVE values in the theta, alpha and sigma bands were also characteristic of older participants. Conclusion: The present study shows a decrease of SWS stability in older subjects together with a decrease in delta wave amplitude. Interestingly, the decrease in SWS stability coincided with an increase in short-term delta, theta, sigma, and alpha power stability revealed by lower CVE. Loss of electroencephalograms (EEG) variability might be a useful marker of brain age.

Automated sleep stage scoring employing a reasoning mechanism and evaluation of its explainability.

Scoring sleep stages from biological signals is an essential but labor-intensive inspection for sleep diagnosis. The existing automated scoring methods have achieved high accuracy but are not widely applied in clinical practice. In our understanding, the existing methods have failed to establish the trust of sleep experts (e.g., physicians and clinical technologists) due to a lack of ability to explain the evidences/clues for scoring. In this study, we developed a deep-learning-based scoring model with a reasoning mechanism called class activation mapping (CAM) to solve this problem. This mechanism explicitly shows which portions of the signals support our model's sleep stage decision, and we verified that these portions overlap with the "characteristic waves," which are evidences/clues used in the manual scoring process. In exchange for the acquisition of explainability, employing CAM makes it difficult to follow some scoring rules. Although we concerned the negative effect of CAM on the scoring accuracy, we have found that the impact is limited. The evaluation experiment shows that the proposed model achieved a scoring accuracy of [Formula: see text]. It is superior to those of some existing methods and the inter-rater reliability among the sleep experts. These results suggest that Sleep-CAM achieved both explainability and required scoring accuracy for practical usage.

Detection Accuracy of Soccer Players in Aerial Images Captured from Several Viewpoints.

In the fields of professional and amateur sports, players' health, physical and physiological conditions during exercise should be properly monitored and managed. The authors of this paper previously proposed a real-time vital-sign monitoring system for players using a wireless multi-hop sensor network that transmits their vital data. However, existing routing schemes based on the received signal strength indicator or global positioning system do not work well, because of the high speeds and the density of sensor nodes attached to players. To solve this problem, we proposed a novel scheme, image-assisted routing (IAR), which estimates the locations of sensor nodes using images captured from cameras mounted on unmanned aerial vehicles. However, it is not clear where the best viewpoints are for aerial player detection. In this study, the authors investigated detection accuracy from several viewpoints using an aerial-image dataset generated with computer graphics. Experimental results show that the detection accuracy was best when the viewpoints were slightly distant from just above the center of the field. In the best case, the detection accuracy was very good: 0.005524 miss rate at 0.01 false positive-per-image. These results are informative for player detection using aerial images and can facilitate to realize IAR.

A speed-up scheme based on multiple-instance pruning for pedestrian detection using a support vector machine.

In pedestrian detection, as sophisticated feature descriptors are used for improving detection accuracy, its processing speed becomes a critical issue. In this paper, we propose a novel speed-up scheme based on multiple-instance pruning (MIP), one of the soft cascade methods, to enhance the processing speed of support vector machine (SVM) classifiers. Our scheme mainly consists of three steps. First, we regularly split an SVM classifier into multiple parts and build a cascade structure using them. Next, we rearrange the cascade structure for enhancing the rejection rate, and then train the rejection threshold of each stage composing the cascade structure using the MIP. To verify the validity of our scheme, we apply it to a pedestrian classifier using co-occurrence histograms of oriented gradients trained by an SVM, and experimental results show that the processing time for classification of the proposed scheme is as low as one-hundredth of the original classifier without sacrificing detection accuracy.