Ultrasound Measurement of Rectus Femoris Muscle Parameters for Discriminating Sarcopenia in Community-Dwelling Adults.

OBJECTIVES: Sarcopenia patients require more medical attention and caretaking. As such, early detection of sarcopenia and appropriate interventions are crucial for decreasing medical costs and meeting the challenges of aging populations. The aim of the present study was to develop a reliable and accurate model to estimate muscle mass using ultrasound-derived parameters from the rectus femoris (RF), referenced by dual-energy X-ray absorptiometry. METHODS: Cross-sectional study was performed. The study patients were recruited by Taipei Veterans General Hospital (No. 2016-07-013C) between 2016 and 2019. A total of 91 community-dwelling adults (35 men and 56 women) were enrolled in this study. Ultrasound measurements of RF muscle thickness (MT), cross-sectional area (CSA), and muscle volume (MV) were performed in B-mode. Muscle strength and physical performance were also examined. Multivariate linear regression was used to build models for the prediction of appendicular skeletal muscle index (ASMI) based on MT, CSA, and MV values. The accuracy of ultrasound RF measurements for predicting sarcopenia was evaluated by using receiver operating characteristic (ROC) curve analysis. RESULTS: The regression equations used for ASMI prediction (adjusted body mass index, sex, and leg length) had high precision and low error. Moreover, the MV model results were close to those of the CSA model and higher than those of the MT model. The ROC analysis showed that both MV and CSA had excellent discrimination when assessing sarcopenia (AUC = 0.83 and 0.81, respectively), whereas MT showed acceptable discrimination (AUC = 0.73). CONCLUSIONS: Ultrasound-derived RF MV was accurate when predicting ASMI and diagnosing sarcopenia in community-dwelling adults.

Electromyography Parameter Variations with Electrocardiography Noise.

Electromyograms (EMG signals) may be contaminated by electrocardiographic (ECG) signals that cannot be easily separated with traditional filters, because both signals have some overlapping spectral components. Therefore, the first challenge encountered in signal processing is to extract the ECG noise from the EMG signal. In this study, the EMG, mixed with different degrees of noise (ECG), is simulated to investigate the variations of the EMG features. Simulated data were derived from the MIT-BIH Noise Stress Test (NSTD) Database. Two EMG and four ECG data were composed with four EMG/ECG SNR to 32 simulated signals. Following Pan-Tompkins R-peak detection, four ECG removal methods were used to remove ECG with different compensation algorithms to obtain the denoised EMG signal. A total of 13 time-domain and four frequency-domain EMG features were calculated from the denoised EMG. In addition, the similarity of denoised EMG features compared to clean EMG was also evaluated. Our results showed that with the ratio EMG/ECG SNR = 10 and 20, the ECG can be almost ignored, and the similarity of EMG features is close to 1. When EMG/ECG SNR = 1 and 2, there is a large variation of EMG features. The results of our simulation study would be beneficial for understanding the variations of EMG features upon the different EMG/ECG SNR.

Bed-Exit Behavior Recognition for Real-Time Images within Limited Range.

In the context of behavior recognition, the emerging bed-exit monitoring system demands a rapid deployment in the ward to support mobility and personalization. Mobility means the system can be installed and removed as required without construction; personalization indicates human body tracking is limited to the bed region so that only the target is monitored. To satisfy the above-mentioned requirements, the behavior recognition system aims to: (1) operate in a small-size device, typically an embedded system; (2) process a series of images with narrow fields of view (NFV) to detect bed-related behaviors. In general, wide-range images are preferred to obtain a good recognition performance for diverse behaviors, while NFV images are used with abrupt activities and therefore fit single-purpose applications. This paper develops an NFV-based behavior recognition system with low complexity to realize a bed-exit monitoring application on embedded systems. To achieve effectiveness and low complexity, a queueing-based behavior classification is proposed to keep memories of object tracking information and a specific behavior can be identified from continuous object movement. The experimental results show that the developed system can recognize three bed behaviors, namely off bed, on bed and return, for NFV images with accuracy rates of 95~100%.

Acupuncture stimulation improves balance function in stroke patients: a single-blinded controlled, randomized study.

The disturbance of balance function is one of main etiology resulting in falling down in stroke patients. A number of studies report that acupuncture may improve the motor function of stroke patients. Therefore, the aim of the present study was to investigate the effect of acupuncture on balance function. We designed a single-blinded, controlled, randomized study. A total of 30 stroke patients were randomized into experimental and control groups. Experimental groups received acupuncture treatment accompanied by the manual twisting of needles and obtaining of qi (the subjects experienced sensations of soreness, numbness, swelling and heaviness, while the acupuncturist experienced a sensation of needle resistance), whereas the control group did not receive manual twisting of needles and without obtaining of qi. All of the subjects were first-time stroke patients; infarction location was limited to either the left or right hemisphere, and all subjects were able to walk for at least 6 meters. Acupuncture stimulation (AS) was applied to Baihui (GV 20) acupoint as well as to 4 spirit acupoints (1.5 cun anterior, posterior, left and right laterals from Baihui acupoint, respectively) for 20 min. Balance function outcome measures were: (1) the displacement area of the patient's center of gravity; (2) the time taken for a patient to stand vertically from a seated position; (3) the time taken for a patient to walk a distance of 6 meters; (4) muscle strength of both lower extremities. Results indicated that the displacement area from the center of gravity decreased in the experimental group, but not in the control group. There was greater reduction in the displacement area in the experimental group than in the control group. Following AS, the time taken to reach a standing position from a seated position, as well as the time taken to walk 6 meters was decreased equally in both the experimental and control groups. The muscle strength of the hip flexor and knee extensor were increased in the paralyzed and non-paralyzed sides of patients in the experimental group, but not in the control group. The results of the present study suggest that acupuncture stimulation may induce an immediate effect that improves balance function in stroke patients.

Gait asymmetry, ankle spasticity, and depression as independent predictors of falls in ambulatory stroke patients.

BACKGROUND: Falls are the leading cause of injury in stroke patients. However, the cause of a fall is complicated, and several types of risk factors are involved. Therefore, a comprehensive model to predict falls with high sensitivity and specificity is needed. METHODS: This study was a prospective study of 112 inpatients in a rehabilitation ward with follow-up interviews in patients' homes. Evaluations were performed 1 month after stroke and included the following factors: (1) status of cognition, depression, fear of fall and limb spasticity; (2) functional assessments [walking velocity and the Functional Independence Measure (FIM)]; and (3) objective, computerized gait and balance analyses. The outcome variable was the number of accidental falls during the 6-month follow-up period after baseline measurements. RESULTS: The non-faller group exhibited significantly better walking velocity and FIM scale compared to the faller group (P < .001). The faller group exhibited higher levels of spasticity in the affected limbs, asymmetry of gait parameters in single support (P < .001), double support (P = .027), and step time (P = .003), and lower stability of center of gravity in the medial-lateral direction (P = .008). Psychological assessments revealed that the faller group exhibited more severe depression and lower confidence without falling. A multivariate logistic regression model identified three independent predictors of falls with high sensitivity (82.6%) and specificity (86.5%): the asymmetry ratio of single support [adjusted odds ratio, aOR = 2.2, 95% CI (1.2-3.8)], the level of spasticity in the gastrocnemius [aOR = 3.2 (1.4-7.3)], and the degree of depression [aOR = 1.4 (1.2-1.8)]. CONCLUSIONS: This study revealed depression, in additional to gait asymmetry and spasticity, as another independent factor for predicting falls. These results suggest that appropriate gait training, reduction of ankle spasticity, and aggressive management of depression may be critical to prevent falls in stroke patients.