Background: The third and fourth heart sound (S3 and S4) are two abnormal heart sound components which are proved to be indicators of heart failure during diastolic period. The combination of using diastolic heart sounds with the standard ECG as a measurement of ventricular dysfunction may improve the noninvasive diagnosis and early detection of myocardial ischemia. Methods: In this paper, an adaptive method based on time-frequency analysis is proposed to detect the presence of S3 and S4. Heart sound signals during diastolic periods were analyzed with Hilbert-Huang Transform (HHT). A discrete plot of maximal instantaneous frequency and its amplitude was generated and clustered. S3 and S4 were recognized by the clustered points, and performance of the method was further enhanced by period definition and iteration tracking. Results: Using the proposed method, S3 and S4 could be detected adaptively in a same method. 90.3% of heart sound cycles with S3 were detected using our method, 9.6% were missed, and 9.6% were false positive. 94% of S4 were detected using our method, 5.5% were missed, and 16% were false positive. Conclusions: The proposed method is adaptive for detecting low-amplitude and low-frequency S3 and S4 simultaneously compared with previous detection methods, which would be practical in primary care.

The American Urological Association guidelines for the management of non-neurogenic overactive bladder (OAB) recommend the use of OnabotulinumtoxinA, sacral neuromodulation (SNM), and peripheral tibial nerve stimulation (PTNS) as third line treatment options with no treatment hierarchy. The current study used network meta-analysis to compare the efficacy of these three modalities for managing adult OAB syndrome. We performed systematic literature searches of several databases from January 1995 to September 2019 with language restricted to English. All randomized control trials that compared any dose of OnabotulinumtoxinA, SNM, and PTNS with each other or a placebo for the management of adult OAB were included in the study. Overall, 17 randomized control trials, with a follow up of 3–6 months in the predominance of trials (range 1.5–24 months), were included for analysis. For each trial outcome, the results were reported as an average number of episodes of the outcome at baseline. Compared with the placebo, all three treatments were more efficacious for the selected outcome parameters. OnabotulinumtoxinA resulted in a higher number of complications, including urinary tract infection and urine retention. Compared with OnabotulinumtoxinA and PTNS, SNM resulted in the greatest reduction in urinary incontinence episodes and voiding frequency. However, comparison of their long-term efficacy was lacking. Further studies on the long-term effectiveness of the three treatment options, with standardized questionnaires and parameters are warranted.

Rapid eye movement (REM) sleep behavior disorder (RBD) is associated with Parkinson’s disease (PD). In this study, a smartwatch-based sensor is utilized as a convenient tool to detect the abnormal RBD phenomenon in PD patients. Instead, a questionnaire with sleep quality assessment and sleep physiological indices, such as sleep stage, activity level, and heart rate, were measured in the smartwatch sensors. Therefore, this device can record comprehensive sleep physiological data, offering several advantages such as ubiquity, long-term monitoring, and wearable convenience. In addition, it can provide the clinical doctor with sufficient information on the patient’s sleeping patterns with individualized treatment. In this study, a three-stage sleep staging method (i.e., comprising sleep/awake detection, sleep-stage detection, and REM-stage detection) based on an accelerometer and heart-rate data is implemented using machine learning (ML) techniques. The ML-based algorithms used here for sleep/awake detection, sleep-stage detection, and REM-stage detection were a Cole–Kripke algorithm, a stepwise clustering algorithm, and a k-means clustering algorithm with predefined criteria, respectively. The sleep staging method was validated in a clinical trial. The results showed a statistically significant difference in the percentage of abnormal REM between the control group (1.6 ± 1.3; n = 18) and the PD group (3.8 ± 5.0; n = 20) (p = 0.04). The percentage of deep sleep stage in our results presented a significant difference between the control group (38.1 ± 24.3; n = 18) and PD group (22.0 ± 15.0, n = 20) (p = 0.011) as well. Further, our results suggested that the smartwatch-based sensor was able to detect the difference of an abnormal REM percentage in the control group (1.6 ± 1.3; n = 18), PD patient with clonazepam (2.0 ± 1.7; n = 10), and without clonazepam (5.7 ± 7.1; n = 10) (p = 0.007). Our results confirmed the effectiveness of our sensor in investigating the sleep stage in PD patients. The sensor also successfully determined the effect of clonazepam on reducing abnormal REM in PD patients. In conclusion, our smartwatch sensor is a convenient and effective tool for sleep quantification analysis in PD patients.

Objectives: Adequately addressing the scope of disability in patients with chronic neck pain is crucial in a clinical setting. The aim of this study was to determine the disease profile of patients with chronic neck pain and to analyze the factors related to the level of disability. Methods: We studied 121 patients with chronic neck pain. The following data were obtained from the participants: Chinese Health Questionnaire-12, the Pittsburg Sleep Quality Index, the visual analog scale to pain, pressure pain threshold, the Neck Disability Index, and heart rate variability analysis. Cluster analysis was performed to divide patients into separate groups according to their presentations. A Pearson correlation was computed to analyze factors correlated with disability (Neck Disability Index). The significance level was set at P<0.05. Results: Cluster analysis yielded 3 groups of patients with distinct features. Group I had a sex composition similar to the original population, was younger, and displayed the lowest overall deficits. Group II included middle-to-older-aged women, and presented with a higher level of pain, psychological distress, sleep disorder, and disability. Group III, comprising middle-to-older-aged men, showed moderate symptom severity. Correlation revealed that the factors significantly associated with a high degree of disability were low heart rate variability, high pain intensity, older age, poor sleep quality, and high psychological distress. Discussion: We suggest that 3 distinct subgroups of patients with chronic neck pain exhibit particular features. Furthermore, reduced heart rate variability was associated with subjective disability in these patients. Further study is advised to establish the pathologic mechanism and clinical applications of our findings.

In the daily life of the elderly, falling is a common accident and results in severe consequences. Hence, considerable research has been conducted on fall detection for the elderly. Smartphones, which have motion sensors and transmitting modules, have been applied as fall detectors. However, the performance of human activity recognition using various smartphones is usually lower than those using a dedicated smartphone due to different specification values of built-in smartphone accelerometers. This presents a challenge in the development of technology for using smartphones as fall detectors. In this study, we compared the specifications of built-in accelerometers which are essential for fall detection in four smartphones from different manufacturers. To test fall detection performance on various smartphones, 10 young healthy volunteers were included to wear the four smartphones and perform daily activities and fall event trials. Then the accuracies of a fall detection algorithm performed on four different smartphones were calculated and compared. The results show that the same algorithm has different accuracies when performed using smartphones from four different manufacturers. Also, a smartphone with insufficient accelerometer specifications exhibits low accuracy in fall detection. Based on our results, we suggest the effect of specifications of built-in accelerometers in smartphones should be considered when developing a fall detection system for multiple models of smartphones. To maintain high accuracy, a smartphone-based fall detection algorithm should only be used in cases in which built-in accelerometers meet certain specifications.