Effects of ECG Data Length on Heart Rate Variability among Young Healthy Adults

Development of a Remote Health-Monitoring System for Frail People: A Preliminary Study

Respiratory and cardiovascular functions decline with age in elderly individuals. Consequently, the incidence of chronic respiratory and cardiovascular diseases increases with age. Heart disease and pneumonia are the leading causes of death in Japan. Given the pathophysiological nature of these diseases, patients inevitably require monitoring of their cardiac and pulmonary functions, such as heart rate and arterial blood oxygenation, as systemic parameters. In addition, monitoring skin temperature and humidity as local parameters is preferable for elderly individuals to maintain healthy daily conditions. In the present study, we developed a wearable vital sign monitoring system and validated the accuracy of the device under development as compared to authorised medical devices that measure these systemic and local parameters in the peripheral tissue of the palm. For the systemic parameters, mean values showed no significant differences between the two devices, but the data bias was greater for the device under development. For the local parameters, mean values showed significant differences between the two devices; however, the data bias was the same for both devices. The acceptable data acquisition of the device under development was approximately 89%, with error acquisition mainly caused by the measurement of systemic parameters. We conclude that further improvements in measurement of systemic parameters are required to increase the data acquisition beyond 90%.

Complexity of locomotion activities in an outside-of-the-lab wearable motion capture dataset

Gait complexity is widely used to understand risk factors for injury, rehabilitation, the performance of assistive devices, and other matters of clinical interest. We analyze the complexity of out-of-the-lab locomotion activities via measures that have previously been used in gait analysis literature, as well as measures from other domains of data analysis. We categorize these broadly as quantifying either the intrinsic dimensionality, the variability, or the regularity, periodicity, or self-similarity of the data from a nonlinear dynamical systems perspective. We perform this analysis on a novel full-body motion capture dataset collected in out-of-the-lab conditions for a variety of indoor environments. This is a unique dataset with a large amount (over 24 h total) of data from participants behaving without low-level instructions in out-of-the-lab indoor environments. We show that reasonable complexity measures can yield surprising, and even profoundly contradictory, results. We suggest that future complexity analysis can use these guidelines to be more specific and intentional about what aspect of complexity a quantitative measure expresses. This will become more important as wearable motion capture technology increasingly allows for comparison of ecologically relevant behavior with lab-based measurements.

Association of cardiac autonomic modulation with different intensities of physical activity in a small Brazilian inner city: a gender analysis

AbstractPhysical activity has been linked to higher cardiac autonomic modulation. However, it is not clear in the literature which type of intensity of physical activity (sedentary, light, moderate, or vigorous) could be related to better cardiac autonomic modulation and whether this relationship is similar in men and women. The aim of this study was to analyze the relationship between cardiac autonomic modulation and the different intensities of physical activity in men and women. The sample included 100 men and 131 women. Cardiac autonomic modulation was obtained by heart rate variability (SDNN, RMSSD, LF un, HF un, SD1, and SD2). Time spent in different intensities (sedentary, light, moderate, and vigorous intensity physical activity) was measured using a waist-worn accelerometer for seven days. Linear regression was used to verify the relationship between physical activity and cardiac autonomic modulation, considering the adjustment for age, ethnicity, and socioeconomic level. Among men, a significant relationship of moderate intensity was observed with SDNN (β=2.442; p = 0.021) and SD2 (β=2.936, p = 0.028), and of vigorous intensity with SDNN (β=12.826, p = 0.028), RMSSD (β=16.370, p = 0.018), and SD1 (β=12.344, p = 0.025) indices. Among women, light-intensity physical activity was significantly related with LFnu (β= .655, p = 0.005) and HFnu (β= -.590, p = 0.010). Moderate and vigorous physical activity was associated with better cardiac autonomic modulation in men. Light physical activity was associated with lower sympathetic modulation in women.

Hybrid-Pattern Recognition Modeling with Arrhythmia Signal Processing for Ubiquitous Health Management

We established a web-based ubiquitous health management (UHM) system, "ECG4UHM", for processing ECG signals with AI-enabled models to recognize hybrid arrhythmia patterns, including atrial premature atrial complex (APC), atrial fibrillation (AFib), ventricular premature complex (VPC), and ventricular tachycardia (VT), versus normal sinus rhythm (NSR). The analytical model coupled machine learning methods, such as multiple layer perceptron (MLP), random forest (RF), support vector machine (SVM), and naive Bayes (NB), to process the hybrid patterns of four arrhythmia symptoms for AI computation. The data pre-processing used Hilbert-Huang transform (HHT) with empirical mode decomposition to calculate ECGs' intrinsic mode functions (IMFs). The area centroids of the IMFs' marginal Hilbert spectrum were suggested as the HHT-based features. We engaged the MATLAB^TM compiler and runtime server in the ECG4UHM to build the recognition modules for driving AI computation to identify the arrhythmia symptoms. The modeling extracted the crucial data sets from the MIT-BIH arrhythmia open database. The validated models, including the premature pattern (i.e., APC-VPC) and the fibril-rapid pattern (i.e., AFib-VT) against NSR, could reach the best area under the curve (AUC) of the receiver operating characteristic (ROC) of approximately 0.99. The models for all hybrid patterns, without VPC versus AFib and VT, achieved an average accuracy of approximately 90%. With the prediction test, the respective AUCs of the NSR and APC versus the AFib, VPC, and VT were 0.94 and 0.93 for the RF and SVM on average. The average accuracy and the AUC of the MLP, RF, and SVM models for APC-VT reached the value of 0.98. The self-developed system with AI computation modeling can be the backend of the intelligent social-health system that can recognize hybrid arrhythmia patterns in the UHM and home-isolated cares.