Published papers

© 2017 Wiley Periodicals, Inc. This paper presents an earbud-based photoplethysmography (PPG) and its application. This PPG recording system permits long-term continuous monitoring of cardiovascular function since it can be worn comfortably on ears. Experimental investigations showed that stress and drowsiness could be estimated by analyzing measured PPG signals. Furthermore, noise suppression techniques for motion noises and ambient light interferences are introduced to make a recorded signal more acceptable.

© 2019 Background: Actigraphy has enabled consecutive observation of individual health conditions such as sleep or daily activity. This study aimed to examine the usefulness of actigraphy in evaluating depressive and/or bipolar disorder symptoms. Method: A systematic review and meta-analysis was conducted. We selected studies that used actigraphy to compare either patients vs. healthy controls, or pre- vs. post-treatment data from the same patient group. Common actigraphy measurements, namely daily activity and sleep-related data, were extracted and synthesized. Results: Thirty-eight studies (n = 3,758) were included in the analysis. Compared with healthy controls, depressive patients were less active (standardized mean difference; SMD=1.27, 95%CI=[0.97, 1.57], P<0.001) and had longer wake after sleep onset (SMD= − 0.729, 95%CI=[− 1.20, − 0.25], p = 0.003). Total sleep time (SMD= − 0.33, 95%CI=[− 0.55, − 0.11], P = 0.004), sleep latency (SMD= − 0.22, 95%CI=[− 0.42, − 0.02], P = 0.032), and wake after sleep onset (SMD= − 0.22, 95%CI=[− 0.39, − 0.04], P = 0.015) were longer in euthymic/remitted patients compared to healthy controls. In pre- and post-treatment comparisons, sleep latency (SMD=− 0.85, 95%CI=[− 1.53, − 0.17], P = 0.015), wake after sleep onset (SMD= − 0.65, 95%CI=[− 1.20, − 0.10], P = 0.022), and sleep efficiency (SMD=0.77, 95%CI=[0.29, 1.24], P = 0.002) showed significant improvement. Limitation: The sample sizes for each outcome were small. The type of actigraphy devices and patients’ illness severity differed across studies. It is possible that hospitalizations and medication influenced the outcomes. Conclusion: We found significant differences between healthy controls and mood disorders patients for some actigraphy-measured modalities. Specific measurement patterns characterizing each mood disorder/status were also found. Additional actigraphy data linked to severity and/or treatment could enhance the clinical utility of actigraphy.

This study aims to verify the effects of “legume-based noodles” as a staple food for lunch, specifically: blood glucose, cognitive function tests, Kansei value, work questionnaires, typing, and body weight. The experiment is divided into two groups: the intervention group (legumes-based noodle) and the control group (regular lunch). Both groups have similar menu except the staple food. The intervention group resulted in a statistically significant lower blood glucose area under the curve (AUC) and lower maximum blood glucose levels during the afternoon work hours on weekdays. In addition, the Kansei value “concentration” decreased at the end of the workday in the control group compared to before and after lunch but did not decrease in the intervention group. Furthermore, the number of typing accuracy was higher in the intervention group than in the control group, and the questionnaire responses for “work efficiency” and “motivation” were more positive. These results suggest that eating legume-based noodles may lead to improved performance of office workers.

The purpose of this study was to quantify the clearance of amyloid-β (Aβ) protein by auditory stimulation in a mouse model of Alzheimer's disease (AD). Accumulation of Aβ is thought to be one of the causes of AD. It has been confirmed that Aβ in 5XFAD mice, a mouse model of AD, is reduced by auditory stimulation at a period of 40 Hz, in which sounds are switched on and off 40 times per second. A previous study showed that auditory stimulation of 1-8 msec duration may be particularly effective for Aβ clearance. However, quantitative evaluation of Aβ has not yet been conducted. Therefore, in this study, we quantitatively evaluated the effect of auditory stimulation on Aβ clearance by analyzing the amount of Aβ. As a result, auditory stimulation using a sound with a period of 40 Hz, a pitch of 10 kHz, and a duration of 3 msec reduced the magnitude of Aβ.

Purpose: The purpose of this study was to investigate the association between autonomic nerve activity and symptom intensity according to the type of dry eye (DE). Methods: This prospective, cross-sectional, comparative study included 25 eyes of 25 patients with short tear break-up time DE (sBUTDE; mean age = 57.4 ± 11.4 years, range = 30–74 years) and 24 eyes of 24 patients with aqueous tear-deficient DE (ADDE; mean age = 62.3 ± 10.7 years, range = 29–76 years) were studied. Autonomic nerve activity was examined, and the Japanese version of the Ocular Surface Disease Index (J-OSDI) and a stress check questionnaire were administered. Autonomic nerve activity was continuously measured for 10 minutes. The parameters were low-frequency (LF) and high-frequency (HF) components of heart rate variability, reflecting cardiac sympathetic and parasympathetic nerve activity, and parasympathetic nerve activity alone, respectively; and the coefficient of variation of R wave-to-R wave (RR) interval (cvRR), component coefficient of variation of LF (ccvLF), and component coefficient of variation of HF (ccvHF), reflecting fluctuation of RR interval, LF, and HF, respectively. Results: Higher J-OSDI scores were associated with higher HF, ccvHF, and subjective stress in sBUTDE, showing a significant correlation (r = 0.53, P < 0.01; r = 0.55, P = 0.01; and r = −0.66, P = 0.01); no correlations were observed between the J-OSDI score and autonomic parameters and stress in ADDE. Conclusions: DE symptoms were significantly associated with the magnitude and fluctuation of parasympathetic activity in sBUTDE. Thus, among the autonomic parameters, parasympathetic activity is involved in the development of symptoms in sBUTDE, whereas the involvement of the autonomic nervous system may be minimal in ADDE.

This paper presents a novel signal processing pipeline for estimating pulse signals, onset peaks, and heart rate using an event-based camera (EVS). EVS capture changes in brightness as a series of events, including both spatially induced events triggered by motion artifacts and temporally induced events caused by changes in skin luminance. When reconstructing pulse signals, spatially induced ON- and OFF-events within the region of interest (ROI) often occur simultaneously, canceling each other out and compromising signal stability. Additionally, biases between ON- and OFF-events can cause peak inversions under certain conditions, further destabilizing the signal. The proposed method addresses these challenges and temporal characteristics of EVS by defining event streams in a Hilbert space and applying Gram-Schmidt orthogonalization to isolate the temporally induced event stream. This enables robust estimation of pulse signals, heart rate through frequency analysis, and onset peak detection. To our knowledge, this is the first study that applies orthogonalization in Hilbert space to improve the signal processing of event-based pulse waves. The performance of the proposed approach is evaluated by comparing the extracted parameters with ground truth photoplethysmography (PPG) signals under three different lighting conditions. In the setup combining a green LED and a green optical filter, the method achieves a mean absolute error (MAE) of 1.13 BPM, a root mean square error (RMSE) of 1.69 BPM, and a peak detection accuracy of 92.72%. These results demonstrate the effectiveness of the proposed event-based method and highlight the potential of EVS for non-contact heart rate monitoring.

Cynomolgus monkeys (Macaca fascicularis) are vital in biomedical research, particularly for drug development and studying neurological diseases. However, accurately identifying individuals in group housing environments remains a significant challenge. This paper presents a near real-time facial recognition system tailored for cynomolgus monkeys, utilizing a fine-tuned Detectron2 model for face detection, followed by eigenface-based classification with Support Vector Machine (SVM) and radial basis function (RBF) kernel. The system achieved an accuracy of 97.65% in 10-fold cross-validation and identified individuals in under 1 minute under ideal conditions. This method eliminates the need for invasive identification techniques, potentially reducing stress and improving animal welfare, and has the potential to reduce the need for individualized housing or specialized enclosures. Additionally, as the system reduces the time and labor required for identifying monkeys, it might benefit research facilities with high turnover rates. This method could improve identification in non-human primate research while minimizing stress associated with traditional techniques.

Thelevel of human consciousness is a critical indicator in medicine, essential for the accurate diagnosis of specific conditions such as brain death. This study aims to provide an objective and visual consciousness indicator for medical applications by proposing a new algorithm to quantify consciousness. Traditionally, consciousness levels have been qualitatively assessed using scales like the Glasgow Coma Scale (GCS). In this study, we introduce a novel algorithm, termed D-MaxApEn, designed to quantify consciousness. This algorithm was applied to EEG data from four different states: wakefulness, anesthesia, coma, and brain death. Through calculation and analysis, we objectively quantified these four levels of consciousness and validated the effectiveness of this algorithm in a real-time brain death diagnosis system. The results demonstrate the significant potential of this algorithm for the future diagnosis of the level of consciousness. Its use could revolutionize early intervention, improve patient outcomes, and reduce the burden on individuals and healthcare systems

This study aims to automatically construct a three-dimensional model of the spine using CT scans, with a focus on the diagnosis and progression prediction of scoliosis. Scoliosis is a condition in which the spine curves sideways and progresses with three-dimensional twisting. Current diagnostic methods rely on evaluating the curvature angle of the spine using X-ray images; however, this method does not adequately reflect three-dimensional twisting, and diagnoses based on two-dimensional images depend on the physician's experience, leading to variability in diagnostic accuracy. On the other hand, while current three-dimensional spinal models are primarily used for surgical planning, these models are specialized for shape information, and there is a need for methods that can quantify the HU values, which represent X-ray absorption rates necessary for diagnosing the condition and visually evaluate them. In this study, we propose a method that uses a segmentation model trained through transfer learning to extract the spinal region from CT scan data, integrate the spatial information with the HU values from the CT images, and generate a 3D point cloud model to improve visibility of spinal structures. The results of this study provide an important foundation for the development of diagnostic and progression prediction models.